Sushmita Ghosh

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.

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.

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.

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.

Abstract: Recent advances in wireless communication technology and the Internet of Things (IoT) have provided an opportunity for mass deployment of low cost sensor nodes to measure air pollution in real-time over a large geographical area. This article presents the design of a low cost, innovative Air Pollution Monitoring Device (APMD) along with the evaluation of its advanced features. An on-board Particulate Matter (PM) sensor is designed to measure PM2.5 and PM10. APMD additionally has electrochemical sensors to measure carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone, besides temperature and humidity sensors. The node is equipped with a solar energy harvesting unit and a rechargeable battery as a backup to power up the module. By utilizing an on-board GPS subsystem, APMD packs all these gathered air quality data in a frame with physical location, time, and date, and sends them to a cloud server. The node can communicate through WiFi and NB-IoT connectivity. For validating the quality of sensing, the developed APMD was co-located with an accurate reference sensor node and a series of field data were collected over seven days. In a fully ON state, the on-board PM sensor saves up to 94% energy while maintaining root mean square error (RMSE) of 0.58 for PM2.5 and 2.5 for PM10. A power control mechanism is also applied on the PM sensor to control the speed of the fan by applying a pulse width modulated (PWM) signal at the switch connected to the power supply of fan. At 100 ms switching period with 30% duty cycle, the on-board PM sensor is 97% energy efficient compared to the commercial sensor, while maintaining sensing error (RMSE) as low as 0.7 for PM2.5 and 2.7 for PM10. Our outdoor deployment studies demonstrate that the designed APMD is 90.8% more power efficient than the reference setup with significantly higher coverage range, while maintaining an acceptable range of sensing error.

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.

Abstract: With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency‐sensitive and real‐time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client‐server model to edge computing along with their objectives and limitations. A state‐of‐the‐art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping‐stone in the field of emerging computing paradigms.

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.

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.

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.