Low-Cost IoT-Based Downtime Detection For UPS and Behaviour Analysis
Sannidhya Gupta,Prakash Nayak,Sachin Chaudhari
International Conference on Communication Systems & Networks, COMSNETS, 2025
@inproceedings{bib_Low-_2025, AUTHOR = {Gupta, Sannidhya and Nayak, Prakash and Chaudhari, Sachin }, TITLE = {Low-Cost IoT-Based Downtime Detection For UPS and Behaviour Analysis}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2025}}
This paper presents a low-cost, Original Equipment Manufacturer (OEM) agnostic Internet of Things (IoT)-based system for monitoring the behaviour of Uninterruptible Power Supply (UPS) units during power outages and recovery. Frequent outages in developing regions cause equipment damage, operational downtime, and data loss. While UPS units provide backup power, affordable options for monitoring their performance remain limited. Commercial solutions such as Simple Network Management Protocol (SNMP) cards are expensive, manufacturer-specific, and reliant on network infrastructure, restricting their use in cost-sensitive or remote installations. The proposed system continuously records input and output currents to detect outages, switchovers, and UPS behaviour during these events, operating independently of mains power to ensure uninterrupted data capture. Deployed across 5 UPS installations, it collected over 4 million data points and automatically identified 61 outage events. Results demonstrate consistent and accurate detection with real-time visualisation via a web dashboard.
Evaluation of Low-Cost Pressure Sensors for IoT and ML-based Water Flow Estimation
Maulesh Tejas Gandhi,Tanmay Himanshu Bhatt,Sachin Chaudhari,Anuradha Vattem
India Council International Conference, INDICON, 2025
@inproceedings{bib_Eval_2025, AUTHOR = {Gandhi, Maulesh Tejas and Bhatt, Tanmay Himanshu and Chaudhari, Sachin and Vattem, Anuradha }, TITLE = {Evaluation of Low-Cost Pressure Sensors for IoT and ML-based Water Flow Estimation}, BOOKTITLE = {India Council International Conference}. YEAR = {2025}}
Water scarcity and inefficient resource management present critical challenges, with conventional flow measurement techniques often relying on expensive and invasive methods. This work evaluates the performance of three low-cost pressure sensors (HK3022, MBS3000, and Series 21Y) for internet of things (IoT) and machine learning (ML) based water flow estimation. Tests were conducted in a closed-loop experimental setup across pressure ranges from 1.0 to 2.0 bar, generating over 8,640 pressure readings and 2,160 flow measurements. Sensor performance was assessed through pressure measurement accuracy metrics (RMSE and CV), and flow estimation reliability was assessed using five ML algorithms. Results demonstrate that all sensors achieve comparable flow estimation accuracy. While calibration improves absolute pressure measurement accuracy, it provides minimal advantage for flow estimation tasks. This low-cost, non-invasive approach offers a promising water monitoring solution that is accessible and economically viable.
AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
Om Rajendra Kathalkar,Nitin Nilesh,Sachin Chaudhari,Anoop Namboodiri
Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP, 2025
Abs | | bib Tex
@inproceedings{bib_AQIF_2025, AUTHOR = {Kathalkar, Om Rajendra and Nilesh, Nitin and Chaudhari, Sachin and Namboodiri, Anoop }, TITLE = {AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification}, BOOKTITLE = {Indian Conference on Computer Vision, Graphics and Image Processing}. YEAR = {2025}}
Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints. Image-based air quality estimation has emerged as a promising alternative, leveraging the visual characteristics of atmospheric pollutants in traffic scenes. However, existing methods suffer from limited cross-city generalization and inadequate exploitation of multi-view perspectives. We present AQIFormer, a novel transformer-based ensemble architecture that addresses these fundamental limitations through innovative dual-view integration, weather-aware attention mechanisms, and comprehensive multi-task learning. Our approach uniquely combines front and rear traffic imagery with meteorological parameters to achieve robust air quality classification across diverse urban environments. Extensive evaluation on a dataset of 26,678 synchronized front–rear image pairs demonstrates 89.96% accuracy, representing a 14.96% improvement over state-of-the-art methods. Most importantly, our model maintains exceptional cross-city generalization capabilities, achieving 81.67% accuracy on an independent dataset collected in Nagpur, India, with only 8.29% performance degradation using few-shot adaptation and minimal training samples.
IoT-based Multimodal Borewell Monitoring
Tanmay Himanshu Bhatt,Jagan Mohan Reddy,Tarun Pragada,Sachin Chaudhari,Kalpana Ramesh
International Conference on Environment Pollution and Prevention, ICEPP, 2025
@inproceedings{bib_IoT-_2025, AUTHOR = {Bhatt, Tanmay Himanshu and Reddy, Jagan Mohan and Pragada, Tarun and Chaudhari, Sachin and Ramesh, Kalpana }, TITLE = {IoT-based Multimodal Borewell Monitoring}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2025}}
Excessive groundwater extraction has led to a significant decline
in water tables, highlighting the need for continuous monitoring. This study
presents an Internet of Things (IoT)-based approach for real-time estimation
of groundwater levels in borewells. In addition to water level measurements,
parameters such as motor current and rainfall are tracked to evaluate their influence on groundwater dynamics. To assess practical feasibility, five monitoring
nodes were deployed across a small educational campus in Hyderabad, India.
Data collected over a two-month period indicate that the proposed low-cost system is both reliable and effective in real-world conditions, while also revealing
interesting correlations between groundwater level variations and the monitored
parameters.
IoT-based Water Disaggregation in IWS System
using ML and DL Techniques
Sahil Umesh Padole,P. S. Reddy,Ritik Yelekar,Nitin Nilesh,Sachin Chaudhari
International Conference on Communication Systems & Networks, COMSNETS, 2025
@inproceedings{bib_IoT-_2025, AUTHOR = {Padole, Sahil Umesh and Reddy, P. S. and Yelekar, Ritik and Nilesh, Nitin and Chaudhari, Sachin }, TITLE = {IoT-based Water Disaggregation in IWS System
using ML and DL Techniques}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2025}}
This paper addresses the challenge of water dis-
aggregation in intermediate water supply (IWS) systems using
water level data. The water level node, in conjunction with
four automatic labelling nodes, was deployed to collect data on
appliance usage, no activity periods, and tank filling. Data is
collected and transmitted to the cloud at two-second intervals
via Wi-Fi networks. The collected water level data are used to
train various machine learning (ML) and deep learning (DL)
models for disaggregation in filling, appliance use such as geyser,
flush, washing machine, and periods of no activity. These models
are then evaluated to determine the most effective approach for
water disaggregation, with performance assessed using accuracy
and F1 score metrics. Out of the tested models, the Long Short-
Term Memory (LSTM) model emerged as the best-performing
algorithm, achieving an accuracy of 94.09% and an F1 score of
0.88.
IoT-based Water Flow Estimation using Multi-Modal Data
Sannidhya Gupta,Maulesh Tejas Gandhi,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2025
@inproceedings{bib_IoT-_2025, AUTHOR = {Gupta, Sannidhya and Gandhi, Maulesh Tejas and Chaudhari, Sachin }, TITLE = {IoT-based Water Flow Estimation using Multi-Modal Data}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2025}}
This paper presents an Internet of Things (IoT) enabled system that utilizes machine learning (ML) techniques to estimate the water flow in pipelines based on multi-modal sensor data. Traditional methods of water flow monitoring, mainly digital water flow meters, are prohibitively expensive, making largescale deployments challenging. To address this, an alternative has been proposed that leverages the correlation between water flow rate, motor current consumption, and pipeline pressure. Current consumption data of water motors has been integrated with pipeline pressure data to develop ML-based solutions that estimate the value of water flow without directly measuring it. The proposed approach enables digital flow monitoring at a fraction of the cost of conventional digital flow meters while offering high time granularity and accuracy, making it a scalable and practical solution for smart water management in diverse settings. The system’s robustness has been demonstrated by testing the model against erasures in the data.
Iot Based Self-cleaning Water Quality Monitoring System for Sewage Treatment Plant
Amit Sutradhar,Mummidivarapu Satish Kumar,Sachin Chaudhari,Rehna Shaik
International Conference, Asia Oceania Geosciences Society, AOGS, 2025
@inproceedings{bib_Iot__2025, AUTHOR = {Sutradhar, Amit and Kumar, Mummidivarapu Satish and Chaudhari, Sachin and Shaik, Rehna }, TITLE = {Iot Based Self-cleaning Water Quality Monitoring System for Sewage Treatment Plant}, BOOKTITLE = {International Conference, Asia Oceania Geosciences Society}. YEAR = {2025}}
Sewage Treatment Plants (STPs) are critical for public health and environmental sustainability. However, Traditional water quality monitoring in globally heavily relies on labour-intensive, lab-based methods, which pose significant challenges but in recent years, IoT-based sensors have emerged as a promising alternative. However, these sensors face challenges including sensor fouling and frequent manual maintenance, which compromise long-term accuracy and operational efficiency. This study presents an innovative IoT enabled Self-Cleaning Water Quality Monitoring System (SC-WQMS) to address these challenges through automation and precision. The proposed system integrates advanced features, including self-cleaning mechanisms. Key water quality parameter, such as Total Dissolved Solids (TDS) is analysed in real-time by using IoT-based sensors. The proposed IoT-enabled SC-WQMS demonstrates significantly better accuracy over traditional non-cleaning devices. Lab report data over a two-month period reveals that the SC-WQMS maintains an average accuracy of 95%-98.5% for TDS measurements, compared to 68%-75% accuracy observed in traditional IoT-based non cleaning devices due to issues like algae formation and particle buildup over the same period. This substantial difference highlights the SC-WQMS's effectiveness in ensuring reliable data transmission to a cloud server. By mitigating the challenges faced by traditional methods. The proposed SC-WQMS represents a scalable, efficient, and sustainable solution for modern water treatment facilities, addressing the operational challenges of traditional systems while promoting better resource management and environmental protection.
Enhancing Air Quality Monitoring in India through Dense IoT Deployments (AirIoT): A Multi-faceted Approach
Ayush Kumar Dwivedi,Ayu Parmar,Sachin Chaudhari
European Geosciences Union General Assembly, EGUGA, 2024
Abs | | bib Tex
@inproceedings{bib_Enha_2024, AUTHOR = {Dwivedi, Ayush Kumar and Parmar, Ayu and Chaudhari, Sachin }, TITLE = {Enhancing Air Quality Monitoring in India through Dense IoT Deployments (AirIoT): A Multi-faceted Approach}, BOOKTITLE = {European Geosciences Union General Assembly}. YEAR = {2024}}
Air pollution, primarily driven by particulate matter (PM), significantly threatens public health. India, with three cities ranking among the world's top ten most polluted and with PM concentrations exceeding WHO guidelines by almost 11 times, urgent measures are needed to address this escalating crisis. AirIoT, a densely deployed IoT-based air quality monitoring network in Hyderabad, India, is an evidence-based approach to bringing awareness and increasing public participation by alleviating data scarcity.
Analyzing 6G Satellite-IoT architecture using stochastic geometry: A Meta-distribution approach
B Naganjani,Ayush Kumar Dwivedi,Sachin Chaudhari,Taneli Riihonen
IEEE Globecom Communications Conference Workshops, Globecom -W, 2024
Abs | | bib Tex
@inproceedings{bib_Anal_2024, AUTHOR = {Naganjani, B and Dwivedi, Ayush Kumar and Chaudhari, Sachin and Riihonen, Taneli }, TITLE = {Analyzing 6G Satellite-IoT architecture using stochastic geometry: A Meta-distribution approach}, BOOKTITLE = {IEEE Globecom Communications Conference Workshops}. YEAR = {2024}}
This paper uses 3D stochastic geometry to analyse
the coverage and meta-distribution (MD) performance of a
low Earth orbit (LEO) satellite-based Internet-of-Things (IoT)
network. The satellites are considered to be distributed at a fixed
altitude around Earth following a Binomial point process. An IoT
device broadcasts its sensed information to all the satellites with
a line-of-sight within the visibility region and they act as selective
decode-and-forward gateways. The ground server coherently
combines the information received from multiple satellites. The
analytically derived performance measures are verified through
Monte Carlo simulations. The results demonstrate the impact of
mega-LEO constellations on coverage and reliability, guiding 6G
architecture design to improve connectivity and data offloading
in smart cities and dense urban environments.
System and method for implementing an experiment remotely and determining an output using a computer vision model
Sachin Chaudhari,Venkatesh Choppella,Om Rajendra Kathalkar,Kandala Savitha Viswanadh,Nitin Nilesh
United States Patent, Us patent, 2024
@inproceedings{bib_Syst_2024, AUTHOR = {Chaudhari, Sachin and Choppella, Venkatesh and Kathalkar, Om Rajendra and Viswanadh, Kandala Savitha and Nilesh, Nitin }, TITLE = {System and method for implementing an experiment remotely and determining an output using a computer vision model}, BOOKTITLE = {United States Patent}. YEAR = {2024}}
A system and method for implementing an experiment remotely and determining an output using a computer-vision model is provided. The system includes an image capturing device, an experiment setup, a microcontroller, a user device, and a relay unit. The microcontroller (i) receives the input of the experiment from the image capturing device,(ii) extracts one or more frames from the input data,(iii) pre-process the one or more frames to obtain a binary image,(iv) obtain a closed curve around the binary image to locate the experiment,(v) determine the coordinates of the experiment to track the experiment in each frame,(vi) determine an output of the experiment from every two consecutive frames of the one or more frames, and (vii) optimize the determined output of the experiment using a linear regression model.
The Engineering End-to-End Remote Labs using IoT-based Retrofitting
Akshit Gureja,Aftab M. Hussain,Kandala Savitha Viswanadh,Nagesh Laxman Walchatwar,Rishabh Anup Agrawal,Shiven Sinha,Sachin Chaudhari,Karthik Vaidhyanathan,Venkatesh Choppella,Prabhakar Bhimalapuram,Harikumar Kandath
IEEE Access, ACCESS, 2024
@inproceedings{bib_The__2024, AUTHOR = {Gureja, Akshit and Hussain, Aftab M. and Viswanadh, Kandala Savitha and Walchatwar, Nagesh Laxman and Agrawal, Rishabh Anup and Sinha, Shiven and Chaudhari, Sachin and Vaidhyanathan, Karthik and Choppella, Venkatesh and Bhimalapuram, Prabhakar and Kandath, Harikumar }, TITLE = {The Engineering End-to-End Remote Labs using IoT-based Retrofitting}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}
Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.
Exposing Privacy Risks in Indoor Air Pollution Monitoring Systems
Krishna,Shreyash Narendra Gujar,Sachin Chaudhari,Ponnurangam Kumaraguru
International Conference on Environment Pollution and Prevention, ICEPP, 2024
@inproceedings{bib_Expo_2024, AUTHOR = {Krishna, and Gujar, Shreyash Narendra and Chaudhari, Sachin and Kumaraguru, Ponnurangam }, TITLE = {Exposing Privacy Risks in Indoor Air Pollution Monitoring Systems}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2024}}
Indoor air pollution monitoring has been a region of interest in recent times. Multiple Internet of Things (IoT) enabled devices are available for this purpose. With the growing number of sensors in our daily environment, huge amounts of data are being collected and pushed to the servers through the Internet. This study aims to reveal that seemingly trivial indoor air pollution data containing particulate matter, carbon dioxide, and temperature can reveal complex insights about an individual’s lifestyle. Data was collected over a period of four months in a real-world environment. The study demonstrates the inference of cooking activities by using machine learning and deep learning techniques. The study further demonstrates that different food items and culinary practices have different air pollution signatures, which can be identified and distinguished with great accuracy (>90%). In the practice of inferential analysis, it is not necessary to rely on data characterised by high frequency or granularity. Less detailed data like hourly averages, can be used to make meaningful conclusions that might intrude on an individual’s privacy. With the rapid advancement in machine learning and deep learning, a proactive approach to privacy is needed to ensure that the collected data and its usage do not intentionally or unintentionally breach individual privacy.
Citywide Mobile Air Quality Monitoring using GPS-Enabled Low-Cost IoT Sensors
Shreyash Narendra Gujar,Hitesh Pamireddy,Naga Saiteja Maradani,Sara Spanddhana,Sachin Chaudhari,Krishnan Sundara Rajan
International Conference on Environment Pollution and Prevention, ICEPP, 2024
Abs | | bib Tex
@inproceedings{bib_City_2024, AUTHOR = {Gujar, Shreyash Narendra and Pamireddy, Hitesh and Maradani, Naga Saiteja and Spanddhana, Sara and Chaudhari, Sachin and Rajan, Krishnan Sundara }, TITLE = {Citywide Mobile Air Quality Monitoring using GPS-Enabled Low-Cost IoT Sensors}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2024}}
Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.
Spatio-Temporal PM Analysis for Event Detection using Low-Cost IoT Sensors
Shreyash Narendra Gujar,Sara Spanddhana,Ayu Parmar,Sachin Chaudhari,Krishnan Sundara Rajan
Future Internet of Things and Cloud, FiCloud, 2024
Abs | | bib Tex
@inproceedings{bib_Spat_2024, AUTHOR = {Gujar, Shreyash Narendra and Spanddhana, Sara and Parmar, Ayu and Chaudhari, Sachin and Rajan, Krishnan Sundara }, TITLE = {Spatio-Temporal PM Analysis for Event Detection using Low-Cost IoT Sensors}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2024}}
Local activities have a significant impact on the air quality in every region. A study for two consecutive years (2021-2022) has been conducted in the Gachibowli region of Hyderabad, India, which employs spatially distributed low-cost IoT-based air quality monitors across residential and at traffic junctions and main roads measuring particulate matter (PM), temperature, and humidity. The study shows the observations during three seasons, aiming to establish correlations between the PM spikes and specific events that triggered these spikes. Detailed discussions focus on the variations in PM levels linked to traffic patterns over six months and the Diwali festival in 2021 and 2022. PM concentrations increased 2-3 times the normal range during Diwali and decreased post-Diwali. In the case of traffic-related pollution, 1.5 times higher PM levels were observed during morning and evening peak traffic hours, with significant reductions in afternoons across all months with variations in range depending on the season.
TRAQID - Traffic-Related Air Quality Image Dataset
Om Rajendra Kathalkar,Nitin Nilesh,Sachin Chaudhari,Anoop Namboodiri
Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP, 2024
@inproceedings{bib_TRAQ_2024, AUTHOR = {Kathalkar, Om Rajendra and Nilesh, Nitin and Chaudhari, Sachin and Namboodiri, Anoop }, TITLE = {TRAQID - Traffic-Related Air Quality Image Dataset}, BOOKTITLE = {Indian Conference on Computer Vision, Graphics and Image Processing}. YEAR = {2024}}
Air quality estimation through sensor-based methods is widely used. Nevertheless, their frequent failures and maintenance challenges constrain the scalability of air pollution monitoring efforts. Recently, it has been demonstrated that air quality estimation can be done using image-based methods. These methods offer several advantages including ease of use, scalability, and low cost. However, the accuracy of these methods hinges significantly on the diversity and magnitude of the dataset utilized. The advancement of air quality estimation through image analysis has been limited due to the lack of available datasets. Addressing this gap, we present TRAQID - Traffic-Related Air Quality Image Dataset, a novel dataset capturing 26,678 front and rear images of traffic alongside co-located weather parameters, multiple levels of Particulate Matters (PM) and Air Quality Index (AQI) values. Spanning over multiple seasons, with over 70 hours of data collection in the twin cities of Hyderabad and Secunderabad, India, the TRAQID offers diverse day and night imagery amid unstructured traffic conditions, encompassing six AQI categories ranging from “Good” to “Severe”. State-of-the-art air quality estimation techniques, which were trained on a smaller and less-diverse dataset, showed poor results on the dataset presented in this paper. TRAQID models various uncertainty types, including seasonal changes, unstructured traffic patterns, and lighting conditions. The information from the two views (front and rear) of the traffic can be combined to improve the estimation performance in such challenging conditions. As such, the TRAQID serves as a benchmark for image-based air quality estimation tasks and AQI prediction, given its diversity and magnitude.
Behavioural Analysis of Water Consumption using IoT-based Smart Retrofit Meter
Ayush Kumar Lall,Aakash Terala,Archit Goyal,Shailesh Singh Chouhan,Sachin Chaudhari
IEEE Access, ACCESS, 2024
@inproceedings{bib_Beha_2024, AUTHOR = {Lall, Ayush Kumar and Terala, Aakash and Goyal, Archit and Chouhan, Shailesh Singh and Chaudhari, Sachin }, TITLE = {Behavioural Analysis of Water Consumption using IoT-based Smart Retrofit Meter}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}
Monitoring water flow helps to identify leaks and wastage, leading to better management of
water resources and conservation of this precious resource. To address this challenge, there is a need for an
efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based
solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic
components. Real-time data collection and analysis are performed through edge computation, which locally
processes water meter images captured by the camera and extracts water meter readings. These readings
are transmitted to the cloud for storage and further analysis. Various strategies have been implemented
to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in
adverse environmental conditions. To streamline the firmware update process for multiple connected devices,
a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings.
To assess the reliability and performance of developed solution, field deployment is conducted over several
months, enabling the characterization of water usage patterns across different locations. Integrating energy
harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices.
Overall, this solution offers an effective and comprehensive approach towards achieving efficient and
sustainable water management.
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
Ayu Parmar,Sara Spanddhana,Ayush Kumar Dwivedi,Chinthalapani Rajashekar Reddy,Ishan Patwardhan,Bijjam Sai Dinesh,Sachin Chaudhari,Krishnan Sundara Rajan,KAVITA VEMURI
frontiers in the internet of things, FIT, 2024
@inproceedings{bib_Deve_2024, AUTHOR = {Parmar, Ayu and Spanddhana, Sara and Dwivedi, Ayush Kumar and Reddy, Chinthalapani Rajashekar and Patwardhan, Ishan and Dinesh, Bijjam Sai and Chaudhari, Sachin and Rajan, Krishnan Sundara and VEMURI, KAVITA }, TITLE = {Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study}, BOOKTITLE = {frontiers in the internet of things}. YEAR = {2024}}
Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.
Security Analysis of IoT-based Remote Labs
Nagesh Laxman Walchatwar,Akshit Gureja,Gangavarapu Vigneswara Ihita,Adhishree Ojha,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2024
@inproceedings{bib_Secu_2024, AUTHOR = {Walchatwar, Nagesh Laxman and Gureja, Akshit and Ihita, Gangavarapu Vigneswara and Ojha, Adhishree and Chaudhari, Sachin }, TITLE = {Security Analysis of IoT-based Remote Labs}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2024}}
Remote labs are online laboratories that allow users to access and interact with experimental hardware setups remotely. They are helpful in providing practical learning experiences of theoretical concepts from anywhere in the world with an internet connection. Ensuring the security of these remote labs is essential for protecting the confidentiality, integrity, and availability (CIA) of data and services. This paper presents a comprehensive security analysis of the hardware, communication interfaces, and platform of the IoT-based Remote Labs (RLabs) deployed at IIIT Hyderabad, India. We conducted a detailed vulnerability assessment of potential threats using industry-standard tools and performed four targeted attacks to exploit identified weaknesses. The attacks include remote physical hardware manipulation, creating unavailability of experiments for legitimate users, unauthorized access to the platform and data sniffing between the components. To address these vulnerabilities, we propose mitigation strategies to enhance the experience of user experimentation in remote labs.
IoT-Enabled Water Monitoring in Smart Cities With Retrofit and Solar-Based Energy Harvesting
Bawankar Nilesh Kundanrao,Ankit Kriti,Shailesh Singh Chouhan,Sachin Chaudhari
IEEE Access, ACCESS, 2024
@inproceedings{bib_IoT-_2024, AUTHOR = {Kundanrao, Bawankar Nilesh and Kriti, Ankit and Chouhan, Shailesh Singh and Chaudhari, Sachin }, TITLE = {IoT-Enabled Water Monitoring in Smart Cities With Retrofit and Solar-Based Energy Harvesting}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}
Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.
Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference
Ayush Kumar Dwivedi,Sachin Chaudhari,Neeraj Varshney,Pramod K. Varshney
IEEE Internet of Things Journal, IOT, 2024
@inproceedings{bib_Perf_2024, AUTHOR = {Dwivedi, Ayush Kumar and Chaudhari, Sachin and Varshney, Neeraj and Varshney, Pramod K. }, TITLE = {Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference}, BOOKTITLE = {IEEE Internet of Things Journal}. YEAR = {2024}}
This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probabil- ity (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis gen- eral and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a tar- get OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Index Terms—Amplify-and-forward, LEO satellites, outage probability, satellite-based IoT, stochastic geometry
Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading
Parihar Nikhilsingh Pradipsingh,Praful Mankar,Sachin Chaudhari
Vehicular Technology Conference, VTC, 2024
@inproceedings{bib_Maxi_2024, AUTHOR = {Pradipsingh, Parihar Nikhilsingh and Mankar, Praful and Chaudhari, Sachin }, TITLE = {Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2024}}
Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfig- urable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED’s receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS. Index Terms—Reconfigurable Intelligent Surfaces, Spectrum Sensing, Maximum Eigenvalue Detector, Correlated Fading, e
Security for oneM2M-Based Smart City Network:
An OM2M Implementation
Gangavarapu Vigneswara Ihita,Vybhav K Acharya,Kanigolla Naga Venkata Bala Likhith,Sachin Chaudhari,Thierry Monteil
International Conference on Communication Systems & Networks, COMSNETS, 2023
@inproceedings{bib_Secu_2023, AUTHOR = {Ihita, Gangavarapu Vigneswara and Acharya, Vybhav K and Likhith, Kanigolla Naga Venkata Bala and Chaudhari, Sachin and Monteil, Thierry }, TITLE = {Security for oneM2M-Based Smart City Network:
An OM2M Implementation}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2023}}
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency,
especially with the rise of Internet of Things (IoT) devices.
Smart cities use these innovations to manage resources better
and enhance resident’s quality of life. However, implementing
smart city initiatives comes with challenges like monitoring,
maintaining, and testing urban infrastructure. Digital Twin
(DT) entails the connection of physical facilities or devices
with their digital counterparts, facilitating real-time monitoring,
manipulation, and predictive analysis of their behavior. This
concept offers a virtual replica of assets, processes, and systems,
enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in
planning maintenance activities and conducting comprehensive
testing, thereby enhancing the resilience and efficiency of smart
city systems. Particularly in the context of managing water
networks, DT technology holds significant promise. Visualization
capabilities provide intuitive insights into the system’s behavior,
facilitating informed decision-making. This visualization, coupled
with actuation capabilities, enables control actions based on
predictive analytics and optimization algorithms, allowing for
proactive management of water resources and infrastructure. To
this end, in this paper, we present the architecture of WaterTwin,
a DT developed for water quality networks in smart city systems.
We demonstrate our approach through the use of a water quality
network at the smart city living lab, IIIT Hyderabad campus.
IoT-based Smart Water Level Monitoring
Ritik Yelekar,T. David Tency,Sachin Chaudhari,S. Madbushi
India Council International Conference, INDICON, 2023
@inproceedings{bib_IoT-_2023, AUTHOR = {Yelekar, Ritik and Tency, T. David and Chaudhari, Sachin and Madbushi, S. }, TITLE = {IoT-based Smart Water Level Monitoring}, BOOKTITLE = {India Council International Conference}. YEAR = {2023}}
This paper aims to address the limitations of current water level monitoring systems, which are bulky, expensive, and difficult to maintain, resulting in limited deployment. To overcome these challenges, the study utilises Internet of Things (IoT)-enabled low-cost sensor nodes for water level monitoring. In this study, an ultrasonic sensor-based water level node is developed to send data to the cloud through GPRS (2G). Five such nodes were deployed to monitor water levels in overhead tanks and sumps on the campus of IIIT Hyderabad, India. The collected water level data were analysed for behavioural patterns and detecting faulty float switches. In addition, a deep learning algorithm was employed, which can predict future water needs. Thus, the proposed IoT-based smart water level meter offers a more accessible and cost-effective approach to water level monitoring.
Sensible Flow: IoT-based Smart Retrofit Water Flow Meter for Taps
Josh Elias Joy,T. David Tency,Sachin Chaudhari
India Council International Conference, INDICON, 2023
@inproceedings{bib_Sens_2023, AUTHOR = {Joy, Josh Elias and Tency, T. David and Chaudhari, Sachin }, TITLE = {Sensible Flow: IoT-based Smart Retrofit Water Flow Meter for Taps}, BOOKTITLE = {India Council International Conference}. YEAR = {2023}}
This paper presents a novel approach to estimate water usage without directly interfacing with the water line. Instead, it utilizes a device that indirectly gauges water consumption by monitoring the position of the tap handle after it is rotated or moved. The setup process for this device is straightforward, as it doesn’t require any modifications to the existing water infrastructure. The system, known as ”Sensible Flow,” employs a sensor node consisting of an inertial Measurement Unit (IMU) to track and analyze the movement and position of the tap handle. To ensure accurate readings, the device is initially calibrated using a flow rate sensor or through manual measurement with a known-volume beaker. Once the flow rate is inferred based on the tap handle position, the data is transmitted via Bluetooth Low Energy (BLE) to a base gateway unit. This base unit provides real-time updates on water consumption and can also be configured to send the collected data to the cloud via WiFi, enabling further analysis and monitoring. Extensive testing has been conducted to evaluate the device’s accuracy and performance. The Sensible Flow system provides a cost-effective and non-intrusive solution for estimating water usage, with potential applications in water conservation and management.
Assessing the Impact of Air Pollution on Physiology: Implications and Prospects
Bhumika Sahu,Ayush Kumar Dwivedi,Kavita Vemuri,Sachin Chaudhari
India Council International Conference, INDICON, 2023
@inproceedings{bib_Asse_2023, AUTHOR = {Sahu, Bhumika and Dwivedi, Ayush Kumar and Vemuri, Kavita and Chaudhari, Sachin }, TITLE = {Assessing the Impact of Air Pollution on Physiology: Implications and Prospects}, BOOKTITLE = {India Council International Conference}. YEAR = {2023}}
Air pollution due to industrial activity, vehicles, and construction has shown immediate and long-term effects on human health. While respiratory condition manifests directly, long-term conditions like heart diseases, cancer, lung damage, and the impact on fetal health have been established. This study is aimed to explore the effects of air pollution on the physiological health of humans using a smart wearable watch. A total of 8 healthy security guards posted at the institute gate close to a very busy traffic junction for 12 hours a day were recruited. The physiological indicators such as heart rate (HR), body temperature (BT), and SPO2 levels were recorded by the wearable device for changes during the day/night for two seasons (summer and winter). Air pollution monitoring sensors were also deployed at the same site to record PM2.5, PM10, relative humidity (RH), and environmental temperature (ENT). A survey of 43 questions under three different sections: demographic, medical condition, and quality of life (QoL) were also filled by each participant. Descriptive statistics and inferential statistical analysis (Wilcoxon, Correlation) have been presented based on the collected data. The research shows an association between short-term fluctuations in physiological parameters due to air pollution. Still, the findings need to be weighted with the accuracy and consistency of the wearable devices.
Comparative Analysis of Construction-Related Air Pollution in Indoor and Outdoor Environment
Rishikesh Bose,Shreyash Narendra Gujar,Ayush Kumar Dwivedi,KAVITA VEMURI,Sachin Chaudhari
International Conference on Environment Pollution and Prevention, ICEPP, 2023
Abs | | bib Tex
@inproceedings{bib_Comp_2023, AUTHOR = {Bose, Rishikesh and Gujar, Shreyash Narendra and Dwivedi, Ayush Kumar and VEMURI, KAVITA and Chaudhari, Sachin }, TITLE = {Comparative Analysis of Construction-Related Air Pollution in Indoor and Outdoor Environment}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2023}}
String Tension based Borewell Water Level Monitoring Using IoT
Tanmay Himanshu Bhatt,Ritik Yelekar,Thomas David Tency,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2023
@inproceedings{bib_Stri_2023, AUTHOR = {Bhatt, Tanmay Himanshu and Yelekar, Ritik and Tency, Thomas David and Chaudhari, Sachin }, TITLE = {String Tension based Borewell Water Level Monitoring Using IoT}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2023}}
The high rate of groundwater usage has resulted in a rapid decline in groundwater levels, which has necessitated its monitoring. This paper focuses on estimating the groundwater level inside borewells remotely and in real-time using the Internet of Things (IoT). For this, a solution is proposed which is based on string tension and does not require any electrical components to be lowered into the borewell. The solution provides a completely automated, real-time, reliable, and IoT-enabled alternative to existing methods for borewell water monitoring. The proposed approach is compared with an ultrasonic sensor-based approach in a controlled environment as well as in a tank. Additionally, four nodes were deployed in a small educational campus in the Indian city of Hyderabad to ascertain its usability and reliability in practical situations. The observations from the data collected over a month show that the proposed low-cost solution is reliable and has a good performance in the field. Index Terms—Borewell, Real-time, IoT, String tension, Water-level.
IoT and ML-based Water Flow Estimation using Pressure Sensor
Maulesh Tejas Gandhi,Ajai Mathew,Sachin Chaudhari,Rehana Shaik,Anuradha Vattem
India Council International Conference, INDICON, 2023
@inproceedings{bib_IoT__2023, AUTHOR = {Gandhi, Maulesh Tejas and Mathew, Ajai and Chaudhari, Sachin and Shaik, Rehana and Vattem, Anuradha }, TITLE = {IoT and ML-based Water Flow Estimation using Pressure Sensor}, BOOKTITLE = {India Council International Conference}. YEAR = {2023}}
This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.
DoA Estimation using Cascaded Neural Networks and Angle Classification for Coherent Signals
Jigyasu Khandelwal,M. Madhuri Latha,Nitin Nilesh,Sachin Chaudhari
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2023
@inproceedings{bib_DoA__2023, AUTHOR = {Khandelwal, Jigyasu and Latha, M. Madhuri and Nilesh, Nitin and Chaudhari, Sachin }, TITLE = {DoA Estimation using Cascaded Neural Networks and Angle Classification for Coherent Signals}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2023}}
This paper focuses on the direction of arrival (DoA) estimation for two coherent sources using a uniform linear array (ULA). It is demonstrated that the DoA estimation error increases as the angle of the incoming signal moves away from the center in the range (-90°, 90°) for existing schemes. In addition, this paper also focuses on improving the DoA estimation performance in low signal to noise ratio (SNR). Therefore a cascaded neural network (CaNN) is proposed to improve the DoA estimation consisting of two stages of neural networks (NNs). The first NN is the enhanced SNR (ESNR) classifier, which is used to improve performance for different SNRs. The second NN is the angle estimator, which improves the performance for different angle ranges. For overcoming the issue of coherent signals, a spatially smoothened auto covariance matrix is fed to the SNR classifier and angle estimator blocks. A performance comparison with an existing scheme such as spatial smoothing- multiple signal classification (SS-MUSIC) and ESNR CaNN shows that the proposed CaNN for different angles and SNR ranges performs better than the existing schemes.
Software Architecture for Multi-User Multiplexing to Enhance Scalability in IoT-Based Remote Labs
Akshit Gureja,Rishabh Anup Agrawal,Sachin Chaudhari,Karthik Vaidhyanathan,Venkatesh Choppella
World Forum on Internet of Things, WF-IoT, 2023
@inproceedings{bib_Soft_2023, AUTHOR = {Gureja, Akshit and Agrawal, Rishabh Anup and Chaudhari, Sachin and Vaidhyanathan, Karthik and Choppella, Venkatesh }, TITLE = {Software Architecture for Multi-User Multiplexing to Enhance Scalability in IoT-Based Remote Labs}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2023}}
Remote Labs refer to an end-to-end system, including hardware and software built to access scientific equipment and resources remotely. The software platform built for such purposes needs to be robust enough to handle the communication of inputs and outputs between the client and the hardware nodes with minimal latency and simultaneously provide a seamless user experience. This paper highlights the importance of scalability in Remote Labs and presents multi-user multiplexing as a solution, to essentially provide users with concurrent access to the hardware node for experiments which can generate outputs instantaneously. The paper discusses the inefficiency of existing web-based Remote Labs with 4-layered architectures and proposes the use of WebSocket with a 3-layer software architecture to enhance user experience, accelerate input-output communication and implement multi-user multiplexing. To showcase the effectiveness of the proposed architecture over existing implementations using Blynk IoT platform as the middleware, a comprehensive communication pipeline was developed from scratch to perform Kirchhoff’s Voltage Law (KVL) experiment remotely.
Using Miniature Setups and Partial Streams for Scalable Remote Labs
Animesh Das,Kandala Savitha Viswanadh,Rishabh Anup Agrawal,Akshit Gureja,Nitin Nilesh,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2023
@inproceedings{bib_Usin_2023, AUTHOR = {Das, Animesh and Viswanadh, Kandala Savitha and Agrawal, Rishabh Anup and Gureja, Akshit and Nilesh, Nitin and Chaudhari, Sachin }, TITLE = {Using Miniature Setups and Partial Streams for Scalable Remote Labs}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2023}}
Remote labs allow students from anywhere in the world to access and conduct experiments without the need to physically be present in a lab at anytime. This is extremely important for students with little to no access to proper science labs because of a lack of infrastructure or a pandemic. There is a potential for scaling up the process so that queue delay can be removed and people are able to access dedicated experiment setups. This paper proposes a way for cost-effective scaling of Remote Labs by miniaturization of setups, image processing techniques, and partial streaming (using single camera to stream multiple experiments). For this, a use-case of the Vanishing Glass Rods experiment is considered. An end-to-end remote lab is created, including hardware setups and a dashboard to demonstrate the efficacy of the proposed approach in comparison to the existing approach of using lab-scale setups and one camera per experiment.
The application of mobile sensing to detect CO and NO2 emission spikes in polluted cities
SPANDDHANA SARA,ANDREW REBEIRO-HARGRAVE,Ayu Parmar,PAK LUN FUNG,Ishan Patwardhan,SAMU VARJONEN,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,SASU TARKOMA
IEEE Access, ACCESS, 2023
@inproceedings{bib_The__2023, AUTHOR = {SARA, SPANDDHANA and REBEIRO-HARGRAVE, ANDREW and Parmar, Ayu and FUNG, PAK LUN and Patwardhan, Ishan and VARJONEN, SAMU and Reddy, Chinthalapani Rajashekar and Chaudhari, Sachin and TARKOMA, SASU }, TITLE = {The application of mobile sensing to detect CO and NO2 emission spikes in polluted cities}, BOOKTITLE = {IEEE Access}. YEAR = {2023}}
Carbon monoxide (CO) and Nitrogen dioxide (NO 2 ) are major air pollutants that have the potential to affect human health adversely. There is a lack of useful information regarding the spatial distribution and temporal variability of CO and NO 2 emissions in major metropolitan areas. The primary goal of this research is to provide a geospatial data methodology for detecting emission spikes of CO and NO 2 in polluted urban environments employing portable low-cost sensors. We propose that ephemeral identification of harmful gas concentrations can be achieved using different IoT device types mounted on a mobile platform. We propose that persistent identification of the CO and NO 2 emission spikes can be attained by driving through the city on different days. We applied this approach to Hyderabad, India, by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating
Protocol for hunting PM2.5 emission hot spots in cities
Sara Spanddhana,Andrew Rebeiro-Hargrave,Shreyash Narendra Gujar,Om Rajendra Kathalkar,Samu Varjonen,Sachin Chaudhari,Sasu Tarkoma
International Workshop on Advances in Environmental Sensing Systems for Smart Cities, EnvSys, 2023
@inproceedings{bib_Prot_2023, AUTHOR = {Spanddhana, Sara and Rebeiro-Hargrave, Andrew and Gujar, Shreyash Narendra and Kathalkar, Om Rajendra and Varjonen, Samu and Chaudhari, Sachin and Tarkoma, Sasu }, TITLE = {Protocol for hunting PM2.5 emission hot spots in cities}, BOOKTITLE = {International Workshop on Advances in Environmental Sensing Systems for Smart Cities}. YEAR = {2023}}
ABSTRACT Particulate Matter (PM) is a major air pollutant that has the potential for adversely affecting human health. Actionable data on the spatial distribution of temporal variability of PM2.5 emission hot spots in large cities are sparse. The main objective of this research is to provide a protocol for using search agents to hunt for PM2.5 emission hot spots in urban environments. We propose short range identification of variability of harmful PM2.5 concentrations can be achieved using IoT devices mounted on a mobile platform. We propose that long range identification the PM2.5 emission hot spots can attained by searching through the city on different days. We applied this approach to Hyderabad, India by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices. The spatial variability of PM2.5 harmful emission hot spots at industrial settings and congested roads were identified. The temporal variability based on image processing shows a weak correlation between PM2.5 concentrations and number of vehicles, and PM2.5 and visibility. The Hyderabad PM2.5 emission hot spots findings demonstrate a clear need to inform people with heart and lung conditions when it is unhealthy to be outside; and when it is unhealthy for children and elderly people to be outside for prolonged periods. Our emission hunting approach can be applied to any mobile platform carried by people walking, cycling or by drones and robots in any city
Security for oneM2M-Based Smart City Network: An OM2M Implementation
Gangavarapu Vigneswara Ihita,Vybhav.K. Acharya,Likhith Kanigolla,Sachin Chaudhari,Thierry Monteil
International Conference on Communication Systems & Networks, COMSNETS, 2023
@inproceedings{bib_Secu_2023, AUTHOR = {Ihita, Gangavarapu Vigneswara and Acharya, Vybhav.K. and Kanigolla, Likhith and Chaudhari, Sachin and Monteil, Thierry }, TITLE = {Security for oneM2M-Based Smart City Network: An OM2M Implementation}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2023}}
Integrating scalability, interoperability, and security has become crucial with the widespread adoption of Internet of Things (IoT)-enabled smart city solutions. In this context, the oneM2M provides promising technical specifications for an interoperable and secure IoT/M2M system. This paper focuses on the potential threats and their impact on oneM2M standardbased smart city deployments. Further, configurations for baseline security of the oneM2M open-source implementation, called eclipse OM2M, are presented. Recommendations are proposed based on actual on-ground tests conducted on OM2M-based smart city deployment of IIIT Hyderabad (IIIT-H) in India. The tests cover passive eavesdropping, performing replay attacks, brute force on credentials, denial of service, and analysis of access control policies. Index Terms—Eclipse OM2M, Internet of Things (IoT) security, IoT security standardisation, oneM2M, smart city
IoT-based AQI Estimation using Image Processing and Learning Methods
Nitin Nilesh,Ishan Patwardhan,Jayati Narang,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_IoT-_2022, AUTHOR = {Nilesh, Nitin and Patwardhan, Ishan and Narang, Jayati and Chaudhari, Sachin }, TITLE = {IoT-based AQI Estimation using Image Processing and Learning Methods}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
Air pollution is a concern to the health of all living beings. It is essential to check on the quality of air in the surroundings. This article presents an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors on Indian rods. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) method along with image processing techniques were used to extract image features while an Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction. Index Terms—Air Quality, CNN, Edge computing, Machine Learning.
Comparative Evaluation of Low-Cost CO2 Sensors for Indoor Air Pollution Monitoring
Rishikesh Bose,Ayu Parmar,Narla Harsha Vardhan,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_Comp_2022, AUTHOR = {Bose, Rishikesh and Parmar, Ayu and Vardhan, Narla Harsha and Chaudhari, Sachin }, TITLE = {Comparative Evaluation of Low-Cost CO2 Sensors for Indoor Air Pollution Monitoring}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
In this paper, four low-cost CO2 sensors are evaluated for IoT-based indoor air pollution monitoring. Specifically, CO2 sensors SCD30, Prana Air, MHZ14, and T6713 are evaluated against a standard reference Aeroqual S-500 device. The experiment was carried out in an indoor environment inside one of the labs in IIIT Hyderabad, India. It is shown that calibration is needed for some of these low-cost devices locally even though the sensors may be factory calibrated. For calibration, simple and widely-used machine learning algorithms are employed such as linear regression, least absolute deviation, random forest, support vector regression, and Gaussian regression. The parameters considered to assess the performance of these sensors are coefficient of determination (R 2 ), coefficient of variability (Cv), and root mean square error (RMSE). After calibration with a reference sensor, it is observed that these low-cost sensors operate well. Index Terms—IoT, Determination coefficient, Low-cost CO2 sensor, Coefficient of variability, Root mean square error.
IoT and ML-based AQI Estimation using Real-time Traffic Data
Nitin Nilesh,Jayati Narang,Ayu Parmar,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_IoT__2022, AUTHOR = {Nilesh, Nitin and Narang, Jayati and Parmar, Ayu and Chaudhari, Sachin }, TITLE = {IoT and ML-based AQI Estimation using Real-time Traffic Data}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
This paper proposes an IoT and machine learning (ML)-based novel method to estimate the air quality index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different ML models, namely random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), are trained on this dataset to predict the AQI category into five levels. The experimental results show an accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models were also trained on individual node datasets, and the behavior of AQI levels was observed. Index Terms—AQI Estimation, Traffic Data, Machine Learning, IoT
Performance Analysis of Selective Decode-and-Forward Relaying for Satellite-IoT
Nikhil Lamba,Ayush Kumar Dwivedi,Sachin Chaudhari
IEEE Globecom Communications Conference Workshops, Globecom -W, 2022
@inproceedings{bib_Perf_2022, AUTHOR = {Lamba, Nikhil and Dwivedi, Ayush Kumar and Chaudhari, Sachin }, TITLE = {Performance Analysis of Selective Decode-and-Forward Relaying for Satellite-IoT}, BOOKTITLE = {IEEE Globecom Communications Conference Workshops}. YEAR = {2022}}
This paper considers a low-earth-orbit (LEO) satellite-based topology for an internet-of-things (IoT) network, where multiple IoT devices broadcast the information to all the visible satellites over a shared channel using slotted ALOHA. The satellites selectively decode-and-forward (DF) the information from the IoT devices over orthogonal channels to the ground station (GS), which does maximal ratio combining (MRC). For decoding at the satellites, capture and successive interference cancellation (SIC) schemes are considered. For the considered topology, the closed-form expressions are derived for the end-to-end outage probability (OP) for an arbitrary number of IoT devices and satellites in the capture model and for the two-device, two satellite case in the case of the SIC model. The expressions are derived for both independent and non-identically distributed (inid) and independent and identically distributed (iid) uplink channels. The OP is analyzed as a function of the parameters like the number of satellites, the number of devices, and the desired data rate. The results demonstrate that the proposed approach leverages the benefits of mega-LEO satellites to make the topology feasible and attractive for low-powered and low-complexity IoT networks.
Improving IoT-based Smart Retrofit Model for Analog Water Meters using DL based Algorithm
Ayush Kumar Lall,Ansh Khandelwal,Nitin Nilesh,Sachin Chaudhari
International Conference on Future Internet of Things and Cloud, Fi Cloud, 2022
Abs | | bib Tex
@inproceedings{bib_Impr_2022, AUTHOR = {Lall, Ayush Kumar and Khandelwal, Ansh and Nilesh, Nitin and Chaudhari, Sachin }, TITLE = {Improving IoT-based Smart Retrofit Model for Analog Water Meters using DL based Algorithm}, BOOKTITLE = {International Conference on Future Internet of Things and Cloud}. YEAR = {2022}}
This paper proposes a deep learning (DL)-based algorithm which is used for improving the performance of digit detection from internet-of-things (IoT)-based analog water meters. The DL algorithm is trained on a rich dataset of over 160,000 images collected from six water nodes deployed at locations with different environmental conditions. A detailed comparison between the proposed DL and machine learning (ML) algorithm is made based on detection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is more generalized in terms of feature extraction, which makes the algorithm robust.
CV and IoT-based Remote Triggered Labs: Use Case of Conservation of Mechanical Energy
Kandala Savitha Viswanadh,Om Rajendra Kathalkar,Om. K,P. Vinzey,Nitin Nilesh,Sachin Chaudhari,Venkatesh Choppella
Future Internet of Things and Cloud, FiCloud, 2022
@inproceedings{bib_CV_a_2022, AUTHOR = {Viswanadh, Kandala Savitha and Kathalkar, Om Rajendra and K, Om. and Vinzey, P. and Nilesh, Nitin and Chaudhari, Sachin and Choppella, Venkatesh }, TITLE = {CV and IoT-based Remote Triggered Labs: Use Case of Conservation of Mechanical Energy}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2022}}
Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.
SPATIAL FACTOR ANALYSIS OF MOBILE IOT DATA: A CASE STUDY ON PM ACROSS INDIA
Souradeep Deb,Ayush Kumar Dwivedi,Sachin Chaudhari,Krishnan Sundara Rajan
International Geoscience and Remote Sensing Symposium, IGARSS, 2022
@inproceedings{bib_SPAT_2022, AUTHOR = {Deb, Souradeep and Dwivedi, Ayush Kumar and Chaudhari, Sachin and Rajan, Krishnan Sundara }, TITLE = {SPATIAL FACTOR ANALYSIS OF MOBILE IOT DATA: A CASE STUDY ON PM ACROSS INDIA}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2022}}
This paper proposes a novel methodology of analyzing mobile Internet of Things (IoT) data by performing spatial and anthropogenic factor-based thematic interactions with it to retrieve interesting patterns that account for the data variation. In order to test out this methodology, a study is conducted by collecting Particulate Matter (PM) data across India using a mobile IoT node, and look into the neighbouring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. By performing the spatial factor analysis on the mobile IoT data, we evaluated the influence of human activities on PM10 levels, most significantly observed for 0
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
Ayu Parmar,Spanddhana Sara,Ayush Kumar Dwivedi,Chinthalapani Rajashekar Reddy,Ishan Patwardhan,Sai Dinesh Bijjam,Sachin Chaudhari,Krishnan Sundara Rajan,Kavita Vemuri
Technical Report, arXiv, 2022
@inproceedings{bib_Deve_2022, AUTHOR = {Parmar, Ayu and Sara, Spanddhana and Dwivedi, Ayush Kumar and Reddy, Chinthalapani Rajashekar and Patwardhan, Ishan and Bijjam, Sai Dinesh and Chaudhari, Sachin and Rajan, Krishnan Sundara and Vemuri, Kavita }, TITLE = {Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study}, BOOKTITLE = {Technical Report}. YEAR = {2022}}
Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatiotemporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.
5D-IoT, a Semantic Web Based Framework for Assessing IoT Data Quality
Shubham Mante,Nathalie Hernandez,Aftab M. Hussain,Sachin Chaudhari,Deepak Gangadharan,Thierry Monteil
ACM Symposium on Applied Computing, SAC, 2022
@inproceedings{bib_5D-I_2022, AUTHOR = {Mante, Shubham and Hernandez, Nathalie and Hussain, Aftab M. and Chaudhari, Sachin and Gangadharan, Deepak and Monteil, Thierry }, TITLE = {5D-IoT, a Semantic Web Based Framework for Assessing IoT Data Quality}, BOOKTITLE = {ACM Symposium on Applied Computing}. YEAR = {2022}}
Due to the increasing number of Internet of Things (IoT) devices, a large amount of data is being generated. However, factors such as hardware malfunctions, network failures, or cyber-attacks affect data quality and result in inaccurate data generation. Therefore, to facilitate the data usage, we propose a novel 5D-IoT framework for heterogeneous IoT systems that provides uniform data quality assessment with meaningful data descriptions. Based on the quality assessment result, a data consumer can directly access data from any IoT source, which ultimately speeds up the analysis process and helps gain important insights in less time. The framework relies on semantic descriptions of sensor observations and SHACL shapes assessing the quality of such data. Evaluations carried out on real-time data show the added value of such a framework.
A survey on rural internet connectivity in India
Shruthi Koratagere Anantha Kumar,Gangavarapu Vigneswara Ihita,Sachin Chaudhari,Paventhan Arumugam
International Conference on Communication Systems & Networks, COMSNETS, 2022
@inproceedings{bib_A_su_2022, AUTHOR = {Kumar, Shruthi Koratagere Anantha and Ihita, Gangavarapu Vigneswara and Chaudhari, Sachin and Arumugam, Paventhan }, TITLE = {A survey on rural internet connectivity in India}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2022}}
—Rural connectivity has been a widely researched topic for several years. In India, around 50% of the population have poor or no connectivity to access digital services. Numerous technological solutions are being tested around the world, as well as in India. The key driving factor for reducing the digital divide is to lower the cost of network deployments and improve service adoption rate by exploring different technological and economical solutions. This survey aims to study rural connectivity and create awareness about the use-cases, state of the art projects and initiatives, challenges, and technologies to improve digital connectivity in rural parts of India. The strengths and weaknesses of different technologies tested for rural connectivity are analysed. The study includes a brief discussion of rural connectivity trials performed in India and around the world. We also explore the rural use-case of the 6G communication system, which would suit the rural Indian scenario
Techno-Economic Study of 5G Network Slicing to Improve Rural Connectivity in India
Shruthi Koratagere Anantha Kumar,Robert W. Stewart,David Crawford,Sachin Chaudhari
IEEE Open Journal of the Communications Society, OJ-COMS, 2021
Abs | | bib Tex
@inproceedings{bib_Tech_2021, AUTHOR = {Kumar, Shruthi Koratagere Anantha and Stewart, Robert W. and Crawford, David and Chaudhari, Sachin }, TITLE = {Techno-Economic Study of 5G Network Slicing to Improve Rural Connectivity in India}, BOOKTITLE = {IEEE Open Journal of the Communications Society}. YEAR = {2021}}
Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a
Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network
Chinthalapani Rajashekar Reddy,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2021
Abs | | bib Tex
@inproceedings{bib_Hier_2021, AUTHOR = {Reddy, Chinthalapani Rajashekar and Chaudhari, Sachin }, TITLE = {Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2021}}
For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.
Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network
M. Madhuri Latha,Dara Sai Krishna Charan,Sachin Chaudhari,Neeraj Varshney
Vehicular Technology Conference, VTC, 2021
@inproceedings{bib_Beam_2021, AUTHOR = {Latha, M. Madhuri and Charan, Dara Sai Krishna and Chaudhari, Sachin and Varshney, Neeraj }, TITLE = {Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2021}}
In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the NeymanPearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems. Index Terms—Beamforming, direction of arrival (DoA), energy detection, mmWave, spectrum sensing.
IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown
Souradeep Deb,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara
International Conference on Electronics, Computing and Communication Technologies, CONECCT, 2021
@inproceedings{bib_IoT__2021, AUTHOR = {Deb, Souradeep and Reddy, Chinthalapani Rajashekar and Chaudhari, Sachin and Vemuri, Kavita and Sundara, Rajan Krishnan }, TITLE = {IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown}, BOOKTITLE = {International Conference on Electronics, Computing and Communication Technologies}. YEAR = {2021}}
During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms
Techno-economic Study of 5G Network Slicing to Improve Rural Connectivity in India
SHRUTHI KORATAGERE ANANTHA KUMAR,Robert Stewart,David Crawford,Sachin Chaudhari
IEEE Open Journal of the Communications Society, OJ-COMS, 2021
@inproceedings{bib_Tech_2021, AUTHOR = {KUMAR, SHRUTHI KORATAGERE ANANTHA and Stewart, Robert and Crawford, David and Chaudhari, Sachin }, TITLE = {Techno-economic Study of 5G Network Slicing to Improve Rural Connectivity in India}, BOOKTITLE = {IEEE Open Journal of the Communications Society}. YEAR = {2021}}
Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.
Comparative evaluation of new low-cost particulate matter sensors
Ishan Patwardhan,Sara Spanddhana,Sachin Chaudhari
International Conference on Future Internet of Things and Cloud, Fi Cloud, 2021
@inproceedings{bib_Comp_2021, AUTHOR = {Patwardhan, Ishan and Spanddhana, Sara and Chaudhari, Sachin }, TITLE = {Comparative evaluation of new low-cost particulate matter sensors}, BOOKTITLE = {International Conference on Future Internet of Things and Cloud}. YEAR = {2021}}
In recent times, a few new low-cost sensors have been introduced to the global market for monitoring particulate matter (PM). In this paper, the performance of three such low-cost PM sensors, namely SDS011, Prana Air, and SPS30, for measuring PM2.5 and PM10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 µg/cm3 to 600 µg/cm3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R 2 ), coefficient of variation (Cv) and root mean square error (RMSE). Evaluation results show that these low-cost sensors have good performance after calibration with a reference sensor.
IoT Network-Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown
Souradeep Deb,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,KAVITA VEMURI,K.S. Rajan
International Conference on Electronics, Computing and Communication Technologies, CONECCT, 2021
@inproceedings{bib_IoT__2021, AUTHOR = {Deb, Souradeep and Reddy, Chinthalapani Rajashekar and Chaudhari, Sachin and VEMURI, KAVITA and Rajan, K.S. }, TITLE = {IoT Network-Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown}, BOOKTITLE = {International Conference on Electronics, Computing and Communication Technologies}. YEAR = {2021}}
During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown
Making Analog Water Meter Smart using ML and IoT-based Low-Cost Retrofitting
Ayush Kumar Lall,Ansh Khandelwal,Rishi Bose,Bawankar Nilesh Kundanrao,Nitin Nilesh,Ayush Kumar Dwivedi,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2021
@inproceedings{bib_Maki_2021, AUTHOR = {Lall, Ayush Kumar and Khandelwal, Ansh and Bose, Rishi and Kundanrao, Bawankar Nilesh and Nilesh, Nitin and Dwivedi, Ayush Kumar and Chaudhari, Sachin }, TITLE = {Making Analog Water Meter Smart using ML and IoT-based Low-Cost Retrofitting}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2021}}
—This paper introduces an internet-of-things (IoT) based economic retrofitting setup for digitising the analog water meters to make them smart. The setup contains a Raspberry-Pi microcontroller and a Pi-camera mounted on top of the analog water meter to take its images. The captured images are then preprocessed to estimate readings using a machine learning (ML) model. The employed ML algorithm is trained on a rich dataset that includes digits from the images of water meters captured by the hardware setup for ten days. The readings are posted on a cloud server in real-time using Raspberry-Pi. High temporal resolution plots of flow rate and volume are generated to derive inferences. The collected data can be used for deriving water consumption patterns and fault detection for efficient water management.
Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network
Chinthalapani Rajashekar Reddy,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2021
@inproceedings{bib_Hier_2021, AUTHOR = {Reddy, Chinthalapani Rajashekar and Chaudhari, Sachin }, TITLE = {Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2021}}
For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.
Maximum Frequency Based Adaptive Sensing for Particulate Matter Nodes in IoT Network
Chinthalapani Rajashekar Reddy,S. De,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2021
@inproceedings{bib_Maxi_2021, AUTHOR = {Reddy, Chinthalapani Rajashekar and De, S. and Chaudhari, Sachin }, TITLE = {Maximum Frequency Based Adaptive Sensing for Particulate Matter Nodes in IoT Network }, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2021}}
Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India
G V Ihita,Kandala Savitha Viswanadh,Sudhansh Yelishetty,Sachin Chaudhari,SAGAR GAUR
World Forum on Internet of Things, WF-IoT, 2021
@inproceedings{bib_Secu_2021, AUTHOR = {Ihita, G V and Viswanadh, Kandala Savitha and Yelishetty, Sudhansh and Chaudhari, Sachin and GAUR, SAGAR }, TITLE = {Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2021}}
Business model for rural connectivity using multi-tenancy 5G network slicing
Shruthi Koratagere Anantha Kumar,Anantha Kumar,Robert Stewart,David Crawford,Sachin Chaudhari
h International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HO, HONET, 2020
@inproceedings{bib_Busi_2020, AUTHOR = {Kumar, Shruthi Koratagere Anantha and Kumar, Anantha and Stewart, Robert and Crawford, David and Chaudhari, Sachin }, TITLE = {Business model for rural connectivity using multi-tenancy 5G network slicing }, BOOKTITLE = {h International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HO}. YEAR = {2020}}
Rural areas are often neglected while deploying newer mobile technologies. Hence, these place are digitally disconnected from the world. To overcome this challenge, 5G network slicing supporting multi-tenancy, also known as neutral host network, is studied to improve rural connectivity. The infrastructure provider (InP) deploys the rural 5G network and mobile network operators (MNOs) lease the slices from InP to serve their end-users. This aims to study the value network configuration (VNC) for the 5G network slicing architecture to understand the possible business model. As a result, three configurations are defined driven by micro-operator, MNO and community end-users respectively. The business models are constructed using SWOT analysis and business canvas models. The revenue streams for the proposed rural network are analyzed.
Beamformed Sensing using Dominant DoA in Cognitive mmWave Network
M. Madhuri Latha,Dara Sai Krishna Charan,Sachin Chaudhari
International Conference on Advanced Networks and Telecommunications Systems, ANTS, 2020
@inproceedings{bib_Beam_2020, AUTHOR = {Latha, M. Madhuri and Charan, Dara Sai Krishna and Chaudhari, Sachin }, TITLE = {Beamformed Sensing using Dominant DoA in Cognitive mmWave Network}, BOOKTITLE = {International Conference on Advanced Networks and Telecommunications Systems}. YEAR = {2020}}
In this paper, we propose spectrum sensing schemes for a secondary user (SU) with multiple antennas to detect a primary user (PU) transmission in a cognitive mmWave network. The channel model considered at mmWave carrier frequencies is the clustered Rician fading channel, which has few multipaths. For the considered scenario, we propose three beamformed energy detection (BFED) schemes where beamforming is done in the dominant direction of arrival (DoA) at the SU and then energy detection (ED) is applied. The three schemes differ in the amount of information assumed about the DoAs at the SU. The performance of these schemes has been compared with the traditional ED and maximal ratio combining (MRC) schemes for multiantenna systems. It is shown through simulations that the proposed BFED approaches provide significant performance gains over the ED and negligible loss as compared to the MRC, which makes an impractical assumption of the channel between the PU and the SU to be exactly known.
Copula-Based Cooperative Sensing of OFDM Signals in Cognitive Radios
Akhil Singh,Chokkarapu Sai Praneeth,Sachin Chaudhari,Pramod K. Varshney
International Conference on Communication Systems & Networks, COMSNETS, 2020
@inproceedings{bib_Copu_2020, AUTHOR = {Singh, Akhil and Praneeth, Chokkarapu Sai and Chaudhari, Sachin and Varshney, Pramod K. }, TITLE = {Copula-Based Cooperative Sensing of OFDM Signals in Cognitive Radios}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2020}}
This paper proposes the use of copula theory for cooperative spectrum sensing (CSS) of orthogonal frequency-division multiplexing (OFDM) based primary user (PU). A distributed detection model is assumed where secondary users (SUs) employ autocorrelation detectors (ADs) for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as -copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing.
Opportunistic Use of Successive Interference Cancellation in Reverse TDD HetNets
GORREPATI RAKESH,Sachin Chaudhari,Taneli Riihonen
Wireless Communications Letters, WCL, 2020
@inproceedings{bib_Oppo_2020, AUTHOR = {RAKESH, GORREPATI and Chaudhari, Sachin and Riihonen, Taneli }, TITLE = {Opportunistic Use of Successive Interference Cancellation in Reverse TDD HetNets}, BOOKTITLE = {Wireless Communications Letters}. YEAR = {2020}}
Cross-tier interference management is one of the major challenges in heterogeneous cellular networks (HetNets). Though the network throughput increases due to a better area spectral efficiency of a HetNet, there is possibility that high interference will make few link capacities close to zero when users regard interference as noise (IAN). In this letter, successive interference cancellation (SIC) is used to cancel the cross-tier interference in a reverse time division duplexing (RTDD) scheme. We demonstrate that by opportunistic use of SIC, a minimum guarantee on the sum link capacity can be ensured for an RTDD HetNet. This minimum sum link capacity is later on proved to be the maximum that can be achieved by orthogonal resource allocation schemes. Through system-level simulations for random allocation, it is shown that the proposed scheme is better than using SIC and IAN alone. To further improve the overall system capacity, an optimization problem for selecting co-channel users is formulated, and the Hungarian algorithm is employed to solve it.
Embedded Machine Learning-Based Data Reduction in Application-Specific Constrained IoT Networks, Czech Republic
Adarsh Pal Singh,Sachin Chaudhari
ACM Symposium on Applied Computing, SAC, 2020
@inproceedings{bib_Embe_2020, AUTHOR = {Singh, Adarsh Pal and Chaudhari, Sachin }, TITLE = {Embedded Machine Learning-Based Data Reduction in Application-Specific Constrained IoT Networks, Czech Republic}, BOOKTITLE = {ACM Symposium on Applied Computing}. YEAR = {2020}}
Reducing the amount of wireless data transmissions in constrainedbattery-powered sensor nodes is an effective way of prolongingtheir lifetime. In this paper, we present a machine learning-baseddata transmission reduction scheme for application-specific IoTnetworks. Though many error thresholding-based data predictionschemes have been explored in the past, this is the first work toincorporate machine learning in constrained sensor nodes to reducedata transmissions. We also provide a generic overview and com-parison of five traditional supervised machine learning algorithmsin the context of offloading trained models to memory and computa-tionally constrained microcontrollers. The proposed data reductionscheme is validated on an occupancy estimation testbed deployed inour lab. Experimental results demonstrate 99.91% overall reductionin data transmissions while imparting similar performance and 18to 82 times lesser transmissions than Shewhart change detectionalgorithm.
Performance Analysis of Novel Direct Access Schemes for LEO Satellites Based IoT Network
Ayush Kumar Dwivedi,Chokkarapu Sai Praneeth,Sachin Chaudhari,Neeraj Varshney
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2020
@inproceedings{bib_Perf_2020, AUTHOR = {Dwivedi, Ayush Kumar and Praneeth, Chokkarapu Sai and Chaudhari, Sachin and Varshney, Neeraj }, TITLE = {Performance Analysis of Novel Direct Access Schemes for LEO Satellites Based IoT Network}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2020}}
This paper analyzes the performance of low earth orbit (LEO) satellites based internet-of-things (IoT) network where each IoT node makes use of multiple satellites to communicate with the ground station (GS). In this work, we consider fixed and variable gain amplify-and-forward (AF) relaying protocol at each satellite where the received signal from each IoT node is amplified before transmitting to the terrestrial GS for data processing. To analyze the performance of this novel LEO satellites based direct access architecture, the closed-form expressions for outage probability are derived considering two combining schemes at the GS:(i) selection combining;(ii) maximal ratio combining. Further, to gain more insights for diversity order and coding gain, asymptotic outage probability analysis at high SNR for both schemes is also performed. Finally, simulation results are presented to validate the analytical results derived and also to develop several interesting insights into the system performance.
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
Chinthalapani Rajashekar Reddy,Mukku Tanmai,Ayush Kumar Dwivedi,AUROPRAVA ROUT,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara,Aftab M. Hussain
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2020
@inproceedings{bib_Impr_2020, AUTHOR = {Reddy, Chinthalapani Rajashekar and Tanmai, Mukku and Dwivedi, Ayush Kumar and ROUT, AUROPRAVA and Chaudhari, Sachin and Vemuri, Kavita and Sundara, Rajan Krishnan and Hussain, Aftab M. }, TITLE = {Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2020}}
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking
ANISH SHASTRI,Vivek Jain,Sachin Chaudhari,Shailesh Singh Chouhan,Stefan Werner
World Forum on Internet of Things, WF-IoT, 2019
@inproceedings{bib_Impr_2019, AUTHOR = {SHASTRI, ANISH and Jain, Vivek and Chaudhari, Sachin and Chouhan, Shailesh Singh and Werner, Stefan }, TITLE = {Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2019}}
This paper proposes the use of Shewhart test to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggybacking and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime. Published in: 2019 IEE
Improving the Accuracy of the Shewhart Test-based Data-Reduction Technique using Piggybacking
ANISH SHASTRI,Vivek Jain,Sachin Chaudhari,Shailesh Singh Chouhan,Stefan Werner
World Forum on Internet of Things, WF-IoT, 2019
@inproceedings{bib_Impr_2019, AUTHOR = {SHASTRI, ANISH and Jain, Vivek and Chaudhari, Sachin and Chouhan, Shailesh Singh and Werner, Stefan }, TITLE = {Improving the Accuracy of the Shewhart Test-based Data-Reduction Technique using Piggybacking}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2019}}
This paper proposes the use of Shewharttest to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggy-backing and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime
Rate and Power Throttling for Traffic Asymmetry in Reverse TDD HetNets
GORREPATI RAKESH,Nachiket Ayir,Sachin Chaudhari,Taneli Riihonen
URSI Convention on Radio Science, URSI, 2019
@inproceedings{bib_Rate_2019, AUTHOR = {RAKESH, GORREPATI and Ayir, Nachiket and Chaudhari, Sachin and Riihonen, Taneli }, TITLE = {Rate and Power Throttling for Traffic Asymmetry in Reverse TDD HetNets}, BOOKTITLE = {URSI Convention on Radio Science}. YEAR = {2019}}
In this paper, sum link capacity expressions for successive interference cancellation (SIC) and regarding interference as noise (IAN) in reverse time-division duplexing (RTDD) heterogeneous cellular network are derived. The considered RTDD network always operates in a synchronized fashion such that if the macro tier is in the uplink (UL), then the small tier will be in the downlink (DL) and vice-versa. Rate and power throttling are used in the uplink (UL) for both IAN and SIC to consider an asymmetric traffic network (DL≫ UL). Systemlevel simulations are performed to compare the overall system throughput of IAN and SIC for different DL/UL ratios. It is observed that rate or power-throttled SIC performs better than rate-throttled IAN and worse than power-throttled IAN.
Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements
Sachin Chaudhari,Marko Kosunen,Semu Makinen,Chandrasekaran Ramanathan,Jan Oksanen,Markus Laatta,Jussi Ryynanen,Visa Koivunen,Mikko Valkama
IEEE Transactions on Vehicular Technology, TVT, 2018
@inproceedings{bib_Spat_2018, AUTHOR = {Chaudhari, Sachin and Kosunen, Marko and Makinen, Semu and Ramanathan, Chandrasekaran and Oksanen, Jan and Laatta, Markus and Ryynanen, Jussi and Koivunen, Visa and Valkama, Mikko }, TITLE = {Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements}, BOOKTITLE = {IEEE Transactions on Vehicular Technology}. YEAR = {2018}}
The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.
Autocorrelation-Based Spectrum Sensing of FBMC Signal
Sachin Chaudhari,KEESARA UPENDER REDDY
International Conference on Communication Systems & Networks, COMSNETS, 2018
@inproceedings{bib_Auto_2018, AUTHOR = {Chaudhari, Sachin and REDDY, KEESARA UPENDER }, TITLE = {Autocorrelation-Based Spectrum Sensing of FBMC Signal}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2018}}
The focus of this paper is on a feature detector for filter bank multicarrier (FBMC) signal in cognitive radio. In this paper, we first prove that the FBMC signal samples are uncorrelated with each other. However, if the FBMC signal is processed by our proposed method, then the autocorrelation function (ACF) of FBMC signal becomes non-zero at the lag equal to number of subcarriers. On the other hand, additive white Gaussian noise (AWGN) samples after the same proposed processing remain uncorrelated. Using this feature, an autocorrelation based feature detector is proposed to detect FBMC signal in noise. The main advantage of the proposed detector is that, unlike blind detectors, this detector can distinguish between FBMC signal and noise (or interference). Next, the distribution of the test statistic of the proposed detector is derived under noise-only scenario so that the threshold of the Neyman-Pearson detector can be designed to maintain constant false alarm rate while maximizing the probability of detection. Simulation results demonstrate the efficacy of the proposed detector
Cooperative Sensing of OFDM Signals Using Heterogeneous Sensors
Akhil Singh,PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
National Conference on Communications, NCC, 2018
@inproceedings{bib_Coop_2018, AUTHOR = {Singh, Akhil and GOHAIN, PRAKASH BORPATRA and Chaudhari, Sachin }, TITLE = {Cooperative Sensing of OFDM Signals Using Heterogeneous Sensors}, BOOKTITLE = {National Conference on Communications}. YEAR = {2018}}
In this paper, we investigate a distributed and heterogeneous cognitive radio network (CRN), comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the presence or absence of an orthogonal frequency-division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND, and MAJOR- ITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to combine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty.
Low Complexity Two-Stage Sensing Using Energy Detection and Beamforming
M. Madhuri Latha,PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
National Conference on Communications, NCC, 2018
@inproceedings{bib_Low__2018, AUTHOR = {Latha, M. Madhuri and GOHAIN, PRAKASH BORPATRA and Chaudhari, Sachin }, TITLE = {Low Complexity Two-Stage Sensing Using Energy Detection and Beamforming}, BOOKTITLE = {National Conference on Communications}. YEAR = {2018}}
In this paper, we propose two two-stage spectrum sensing schemes for a single secondary user (SU) or cognitive radio (CR) with multiple antennas to detect a primary user (PU) transmission. For both the proposed schemes, the first stage involves low-complexity coarse-sensing using simple energy detection (ED). The second stage for both methods involve high-performance fine-sensing using beamformed energy detection (BFED) in the estimated direction of arrival (DoA) of the PU signal. In the two-stage method, the second stage is conditional and sensing process goes to the second stage only if certain performance criteria is not met in the first stage. The two proposed methods differ in the performance criteria, which decides if the second stage of BFED is needed or not. The first two-stage method is designed to reduce complexity when there is no PU transmission while the second method is designed to reduce complexity when the PU signal is present. It is shown through simulations that the proposed two-stage schemes have significantly lower complexity as compared to only employing single-stage BFED with little or no performance loss.
Cooperative Energy Detection with Heterogeneous Sensors under Noise Uncertainty: SNR Wall and use of Evidence Theory
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari,V. Koivunen
IEEE Transactions on Cognitive Communications and Networking, TCCN, 2018
@inproceedings{bib_Coop_2018, AUTHOR = {GOHAIN, PRAKASH BORPATRA and Chaudhari, Sachin and , V. Koivunen }, TITLE = {Cooperative Energy Detection with Heterogeneous Sensors under Noise Uncertainty: SNR Wall and use of Evidence Theory}, BOOKTITLE = {IEEE Transactions on Cognitive Communications and Networking}. YEAR = {2018}}
The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two inter-related themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized signal-to-noise ratio (SNR) walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster-Shafer theory based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.
Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes
Adarsh Pal Singh,Vivek Jain,Sachin Chaudhari,Frank Alexander Kraemer,Stefan Werner,Vishal Garg
IEEE Global Communications Conference, GLOBECOM, 2018
@inproceedings{bib_Mach_2018, AUTHOR = {Singh, Adarsh Pal and Jain, Vivek and Chaudhari, Sachin and Kraemer, Frank Alexander and Werner, Stefan and Garg, Vishal }, TITLE = {Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes}, BOOKTITLE = {IEEE Global Communications Conference}. YEAR = {2018}}
In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately estimating the number of occupants in a room by leveraging multiple heterogeneous sensor nodes and machine learning models. For this purpose, low-cost and non-intrusive sensors such as CO 2 , temperature, illumination, sound and motion were used. The sensor nodes were deployed in a room in a star configuration and measurements were recorded for a period of four days. A regression based method is proposed for calculating the slope of CO 2 , a new feature derived from real-time CO 2 values. Supervised learning algorithms such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM) and random forest (RF) were used on several different combinations of feature sets. Moreover, multiple performance metrics such as accuracy, F1 score and confusion matrix were used to evaluate the performance of our models. Experimental results demonstrate a maximum accuracy of 98.4% and a high F1 score of 0.953 for estimating the number of occupants in the room. Principal component analysis (PCA) was also applied to evaluate the performance of a dataset with reduced dimensionality.
On the Implementation of LMS-based Algorithm for Increasing the Lifetime of IoT Networks
ANISH SHASTRI,Vivek Jain,RHISHI PRATAP SINGH,Sachin Chaudhari,Shailesh Singh Chouhan
International Conference on Advanced Networks and Telecommunications Systems, ANTS, 2018
@inproceedings{bib_On_t_2018, AUTHOR = {SHASTRI, ANISH and Jain, Vivek and SINGH, RHISHI PRATAP and Chaudhari, Sachin and Chouhan, Shailesh Singh }, TITLE = {On the Implementation of LMS-based Algorithm for Increasing the Lifetime of IoT Networks}, BOOKTITLE = {International Conference on Advanced Networks and Telecommunications Systems}. YEAR = {2018}}
This paper focuses on the customized-wireless sensor node implementation of the classical least mean square (LMS) algorithm for the reduction in data-transmissions from the sensor nodes to the sink in internet of things (IoT) networks. This reduction, in turn, increases the battery life of the sensor node. The system was deployed in outdoor and indoor environments to read the ambient temperature and then perform the prediction of the sensed data in order to minimize the number of data-transmissions to the sink node. The utility of the proposed concept has been demonstrated using the measured data and the battery life is increased 2.64 and 2.53 times in indoor and outdoor environments, respectively.
Distributed spatial modulation with dynamic frequency allocation
S KUNAL SHAM,Sachin Chaudhari,Ramamurthy Garimella
Physical Communications, PC, 2017
@inproceedings{bib_Dist_2017, AUTHOR = {SHAM, S KUNAL and Chaudhari, Sachin and Garimella, Ramamurthy }, TITLE = {Distributed spatial modulation with dynamic frequency allocation}, BOOKTITLE = {Physical Communications}. YEAR = {2017}}
This paper proposes a distributed implementation of spatial modulation (SM) using cognitive radios. In distributed spatial modulation (DSM), multiple relays form a virtual antenna array and assist a source to transmit its information to a destination. The source broadcasts its signal, which is independently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Further, an instantaneous symbol error rate (SER) based selection combining is proposed to incorporate the direct link between the source and destination with existing DSM. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end SER as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.
Improved Estimation of TV White Spaces in India using Terrain Data
GORREPATI RAKESH,Abhignya Eturu,Sachin Chaudhari,Jan Oksanen
National Conference on Communications, NCC, 2017
@inproceedings{bib_Impr_2017, AUTHOR = {RAKESH, GORREPATI and Eturu, Abhignya and Chaudhari, Sachin and Oksanen, Jan }, TITLE = {Improved Estimation of TV White Spaces in India using Terrain Data}, BOOKTITLE = {National Conference on Communications}. YEAR = {2017}}
Cognitive radio offers a novel solution to over-come the problem of spectrum underutilization by providing spectral access to secondary users. The television (TV) bands are of particular interest for secondary usage due to their high penetration power and greater coverage. These licensed bands are occupied only in few regions while in most of the other regions they are unoccupied and are termed asTV white space(TVWS). The estimation of TVWS has mostly been done by usingthe statistical and empirical propagation models. In this paper,terrain data is incorporated into the estimation of TVWS and shown to improve the quantitative estimation of TVWS. Using the relevant transmitter information and terrain data in the Indian state of Telangana, the efficacy of the proposed approach is demonstrated. The performance of the proposed approach is compared to that of widely used H at a propagation model. It is shown that the accuracy in TV coverage estimation increases onan average by 45% while incorporating terrain data as compared to using only empirical propagation model. As area outside theTV coverage is TVWS, the increased accuracy in the estimation of TV coverage directly translates to improved accuracy in the estimation of TVWS, which in turn translates into more efficient use of spectrum and better interference management
Evidence Theory based Cooperative Energy Detection under Noise Uncertainty
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari,Visa Koivunen
IEEE Global Communications Conference, GLOBECOM, 2017
@inproceedings{bib_Evid_2017, AUTHOR = {GOHAIN, PRAKASH BORPATRA and Chaudhari, Sachin and Koivunen, Visa }, TITLE = {Evidence Theory based Cooperative Energy Detection under Noise Uncertainty}, BOOKTITLE = {IEEE Global Communications Conference}. YEAR = {2017}}
Noise power uncertainty is a major issue in detectors for spectrum sensing. Any uncertainty in the noise power leads to significant reduction in the detection performance of the energy detector and also results in a performance limitation in the form of SNR walls. In this paper, we propose an evidence theory (also called Dempster-Shafer theory (DST)) based cooperative energy detection (CED) for spectrum sensing. The noise variance is modeled as a random variable with a known distribution. The analyzed system model is similar to a distributed parallel detection network where each secondary user (SU) evaluates the energy from its received signal samples and sends it to a fusion center (FC), which makes the final decision. However, in the proposed DST-based CED scheme, the SUs sends computed belief-values instead of actual energy value to the FC. Any uncertainty in the noise variance is accounted for by discounting the belief values based on the amount of uncertainty associated with each SU. Finally, the discounted belief values are combined using Dempster rule to reach at a global decision. Simulation results indicate that the proposed DST scheme significantly improves the detection probability at low average signal-to-noise ratio (ASNR) in comparison to the traditional sum fusion rule in the presence of noise uncertainty.
Performance Evaluation of Cyclostationary - Based Cooperative Sensing Using Field Measurements
Sachin Chaudhari,Marko Kosunen,Marko Kosunen,Semu Mäkinen,Jan Oksanen,Markus Laatta,Jaakko Ojaniem,Visa Koivunen,Mikko Valkama
IEEE Transactions on Vehicular Technology, TVT, 2016
@inproceedings{bib_Perf_2016, AUTHOR = {Chaudhari, Sachin and Kosunen, Marko and Kosunen, Marko and Mäkinen, Semu and Oksanen, Jan and Laatta, Markus and Ojaniem, Jaakko and Koivunen, Visa and Valkama, Mikko }, TITLE = {Performance Evaluation of Cyclostationary - Based Cooperative Sensing Using Field Measurements }, BOOKTITLE = {IEEE Transactions on Vehicular Technology}. YEAR = {2016}}
This paper focuses on evaluating the gains obtained through cooperative spectrum sensing in the real world while using cyclostationary-based mobile sensors. In cooperative sensing(CS), different secondary users (SUs) in a geographical neighbor-hood cooperate to detect the presence of a primary user (PU).Compared with single-user sensing, cooperation provides diversity gains in the face of multi path fading and shadowing. The effectiveness of CS is demonstrated by analyzing data acquired in two extensive field measurement campaigns. The first measurement campaign (MC-I) focuses on measurements at fixed locations,whereas the second measurement campaign (MC-II) focuses on a scenario where measurements are taken inside a moving car.These measurements are carried out for DVB-T channels in the Capital Region of Finland, which consists of urban and suburban environments. Hard decision rules such as OR,AND,and MAJOR-ITYand a soft decision rule such as sum of cyclostationary test statistics (SUM) are employed, and their detection performances are compared with a cyclostationary-based single-user detector. A performance parameter of relative increase in probability of detection (RIPD) is used to efficiently demonstrate the cooperation gain obtained relative to local sensing. It is shown that cooperation can significantly improve the performance of a sensor severely affected by fading and shadowing effects. Furthermore, it is shown that increasing the number of collaborating users beyond few users(five to eight) does not, in practice, bring significant improvement in terms of the expected RIPD. The performances of CS schemes evaluated from MC-I are also compared with the corresponding simulated CS results using empirical channel models and terrain data for the same experimental parameters. It is shown that the use of empirical or theoretical models may result in detection errors in practical conditions, and measurements should be used to improve the accuracy in such scenarios.
Detection and Classification of OFDM Waveforms Using Cepstral Analysis
J. Jäntti,Sachin Chaudhari,Visa Koivune
IEEE Transactions on Signal Processing, TSP, 2015
@inproceedings{bib_Dete_2015, AUTHOR = {Jäntti, J. and Chaudhari, Sachin and Koivune, Visa }, TITLE = {Detection and Classification of OFDM Waveforms Using Cepstral Analysis}, BOOKTITLE = {IEEE Transactions on Signal Processing}. YEAR = {2015}}
Cepstral analysis has been widely used in audio and speech processing applications because of its ability to reveal periodicities in a signal. The presence of cyclic prefix (CP) in orthogonal frequency division multiplexing (OFDM) signals induces periodicities. Motivated by this, the paper focuses on cepstral analysis of OFDM signal. The distributions of cepstral coefficients are de-rived for two scenarios of noise only and OFDM signal in noise. Itis shown that the OFDM cepstrum is significantly different from the additive white Gaussian noise (AWGN) cepstrum and can be used to detect OFDM waveforms. It is also shown that the cepstrum of OFDM is rich in features and can be used to estimate OFDM parameters such as number of sub carriers and length of the CPin an OFDM symbol. These OFDM waveform parameters can be used to automatically recognize or classify different OFDM waveforms, which are important for cognitive radios, coexistence of heterogeneous networks and signal intelligence. Two schemes are proposed to detect OFDM based primary user (PU) signalsin cognitive radio systems. The distributions of the test statistics under the two hypotheses are established.Neyman–Pearson detec-tions trategy is employed.Algorithms for estimating the number of subcarrier and the length of the CP are proposed and their performances studied through simulations. Later the proposed schemes are extended to cooperative sensing scenario with multiple secondary users (SUs) and it is shown that the collaboration between them significantly improve the performance of the proposed cepstrum based detection and estimation schemes.
Cooperative Energy Detection using Dempster-Shafer Theory under Noise Uncertainties
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
International Conference on Communication Systems & Networks, COMSNETS, 2014
@inproceedings{bib_Coop_2014, AUTHOR = {GOHAIN, PRAKASH BORPATRA and Chaudhari, Sachin }, TITLE = {Cooperative Energy Detection using Dempster-Shafer Theory under Noise Uncertainties}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2014}}
Cooperative spectrum sensing (CSS) is one of the efficient scheme that helps in improving the spectrum sensing performance of a cognitive radio network. In this paper we propose a Dempster-Shafer theory (DST) based CSS for cognitive radio network. The fusion rule is the D-S combination rule which is well known for its ability to handle uncertainty and has been used in quite a different number of fields. Noise uncertainty, which is unavoidable in practical field, can gravely limit the detection performance of CSS. In this paper we specially investigate sensor nodes undergoing uncertainty in noise power when the detection scheme is based on energy of the received signal and use DS theory to minimize such effects. Simulation results reveal significant improvement in CSS gain as compared to previous traditional methods.
