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Sushmita Ghosh

Bio: Sushmita Ghosh is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Energy consumption & Computer science. The author has an hindex of 3, co-authored 6 publications receiving 14 citations. Previous affiliations of Sushmita Ghosh include National Institute of Technology Agartala.

Papers
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Proceedings ArticleDOI
01 Sep 2020
TL;DR: The developed AAPMD system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India.
Abstract: We have designed a 5G-capable environmental sensing network (ESN) node prototype, called Advanced Air Pollution Monitoring Device (AAPMD). The developed prototype system measures concentrations of NO 2 , Ozone, carbon monoxide, and sulphur dioxide using semiconductor sensors. Further, the system gathers other environmental parameters like temperature, humidity, PM 1 , PM 2 . 5 , and PM 10 . The prototype is equipped with a GPS sub-system for accurate geo-tagging. The board communicates through Wi-Fi and NB-IoT. AAPMD is also implemented with energy harvesting power management, and is powered through solar energy and battery backup. Compared to the conventional designs with Wi-Fi-based connectivity, the developed system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India. The system can provide updated measurements of pollutant levels with controllable time granularity.

13 citations

Proceedings ArticleDOI
01 Jan 2020
TL;DR: The overall objective has been demonstrating the ability of beyond state of the art circuits and system design for IoT communications, wherein context specific intelligence is applied at the at the node.
Abstract: In a smart IoT system, multi-sensing at a field node is a typical scenario. The examples considered in this study are pollution monitoring and smart energy metering. In such applications, energy sustainability and communication and storage resource usage optimization are two of the key issues of interest. In this study, on one hand it is intended to develop indigenous beyond state of the art multi-sensing boards with the inherent smartness in energy replenishment and sensing/communication activities. On the other hand, smart data collection and processing at the end node (fog node or edge node) is of interest primarily from efficient communication bandwidth usage perspective. On the first exercise towards energy sustainable IoT sensing and communication board design, we have designed a prototype for a 5G capable environmental air pollution monitoring system. The system measures concentrations of NO2, ozone, CO and SO2 using semiconductor sensors. Further, the system gathers other environmental parameters like temperature, humidity, PM1, PM2.5 and PM10. The prototype is equipped with a GPS sub-system for accurate geo-tagging. The board communicates through Wi-Fi and NB-IoT. The board is also equipped with energy harvesting power management, and is powered through solar energy and battery backup. On the second exercise, a working model of a smart IoT device with a data pruning subsystem is designed, where a smart energy meter is considered for an example application. As a proof of concept we plan to demonstrate data compression at the edge to save bandwidth required for data transmission to a remote cloud. At each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information. The Smart Energy Meter is WiFi and NB-IoT enabled. This meter is capable of logging multiple energy consumption parameters. The overall objective has been demonstrating the ability of beyond state of the art circuits and system design for IoT communications, wherein context specific intelligence is applied at the at the node. The broad philosophy in this study can be readily extended to any chosen IoT application.

9 citations

Journal ArticleDOI
TL;DR: A learning-based optimization strategy is developed using Upper Confidence Bound algorithm to select an optimum active sensor set in a measurement cycle based on the cross-correlations among the parameters, energy consumed by the sensors, and the energy available at the node.
Abstract: Wireless sensor nodes equipped with multiple sensors often have limited energy availability. To optimize the energy sustainability of such sensor hubs, in this paper a novel adaptive sensor selection framework is proposed. Multiple sensors monitoring different parameters in the same environment often possess cross-correlation, which makes the system predictive. To this end, a learning-based optimization strategy is developed using Upper Confidence Bound algorithm to select an optimum active sensor set in a measurement cycle based on the cross-correlations among the parameters, energy consumed by the sensors, and the energy available at the node. Further, a Gaussian process regressor-based prediction model is used to predict the parameter values of inactive sensors from the cross-correlated parameters of active sensors. To evaluate the performance of the proposed framework in real-life applications, an air pollution monitoring sensor node consisting of seven sensors is deployed in the campus that collects data at a default high sampling rate. Simulation results validate the efficiency and efficacy of the proposed framework. Compared to the current state-of-the-art the proposed algorithm is 54% more energy efficient, with complexity $\mathcal {O}(2^{P})$ for $P$ sensors in the node, while maintaining an acceptable range of sensing error.

7 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: This review paper explains about various types of controlling method of dc-dc buck converter for closed loop feedback system, which considers linear, nonlinear, voltage control, current control, and switch mode controller for design the closed loop system.
Abstract: Over the last few years, DC/DC buck converters have been the subject of great interest due to its higher utility for different applications. In this review paper, we explain about various types of controlling method of dc-dc buck converter for closed loop feedback system. The study is focused on various specified application such as wireless network, computer server, computer CPU, telephonic system etc. Here we consider about linear, nonlinear, voltage control, current control, and switch mode controller for design the closed loop system. In linear controlling method, we consider about PID controller, its tuning process, and finding other system parameter. But linear systems fails to work properly in real-time applications, hence we have to think about nonlinear controller and due to this reason, we consider switching mode controller, Hysteretic current and voltage mode controller etc.

6 citations

Journal ArticleDOI
TL;DR: A novel edge intelligence-based data-driven priority-aware sensing and transmission framework that saves up to 41% energy and 32% bandwidth with 68% data accuracy compared to the existing competitive frameworks for non-real-time systems.
Abstract: Owing to the limited storage capacity, battery-powered wireless sensor nodes often suffer from energy sustainability. To optimize the energy consumption of a multi-parameter sensor hub, a novel edge intelligence-based data-driven priority-aware sensing and transmission framework is proposed in this paper. The proposed framework jointly exploits the cross-correlation among the sensing parameters and temporal correlation of the individual sensing signals to find an optimal active sensor set and optimal sampling instants of the sensors in the next measurement cycle. The length of measurement cycle is dynamically decided based on the change in cross-correlation among the parameters and the system state. A discounted upper confidence bound algorithm-based optimization function is formulated to find the optimal active sensor set by solving the trade-off among cross-correlation, energy consumption, and length of measurement cycle. The proposed framework uses Gaussian process regressor-based prediction models to estimate the temporal and cross-correlated parameters of the active and inactive sensor set, respectively. The sampling interval of each active sensor is dynamically adapted based on the temporal prediction error. Extensive simulations are performed on air pollution monitoring dataset to validate the efficacy of the proposed framework in both real-time and non-real-time applications. The proposed algorithm saves up to 41% energy and 32% bandwidth with 68% data accuracy compared to the existing competitive frameworks for non-real-time systems. The proposed framework also identifies the time-critical sensing scenarios with 98% accuracy.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques and identify open research challenges and present directions for future research.
Abstract: The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

58 citations

Posted Content
TL;DR: The rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques is surveyed and open research challenges are identified and present directions for future research.
Abstract: The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

54 citations

Proceedings ArticleDOI
01 Sep 2020
TL;DR: The developed AAPMD system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India.
Abstract: We have designed a 5G-capable environmental sensing network (ESN) node prototype, called Advanced Air Pollution Monitoring Device (AAPMD). The developed prototype system measures concentrations of NO 2 , Ozone, carbon monoxide, and sulphur dioxide using semiconductor sensors. Further, the system gathers other environmental parameters like temperature, humidity, PM 1 , PM 2 . 5 , and PM 10 . The prototype is equipped with a GPS sub-system for accurate geo-tagging. The board communicates through Wi-Fi and NB-IoT. AAPMD is also implemented with energy harvesting power management, and is powered through solar energy and battery backup. Compared to the conventional designs with Wi-Fi-based connectivity, the developed system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India. The system can provide updated measurements of pollutant levels with controllable time granularity.

13 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: The design, simulation and realtime implementation of a digital PID controller for DC-DC buck converter is presented to demonstrate the effectiveness of the developed controller in terms of overshoot limitations and accuracy.
Abstract: This paper presents the design, simulation and realtime implementation of a digital PID controller for DC-DC buck converter. The developed controller is capable to drive the output voltage of the buck converter to track a desired voltage reference regardless of input voltage or load variations. The parameters of the digital PID controller are calculated based on the transfer function of the buck converter and Matlab PIDTOOl. An Arduino Uno is used to implement the developed controller. Numerical simulation and experimental validation are presented to demonstrate the effectiveness of the developed controller in terms of overshoot limitations and accuracy.

11 citations

Journal ArticleDOI
26 May 2021
TL;DR: A critical overview of significant contributions and SEM research, which include monitoring the quality of air, water pollution, radiation pollution, and agricultural systems and thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.
Abstract: Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

8 citations