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Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on …
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Computer Science Faculty Research & Creative Works
UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …
Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das
Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das
Computer Science Faculty Research & Creative Works
Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to …
An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
Computer Science Faculty Research & Creative Works
An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a …
Compressed Sensing Based Low-Power Multi-View Video Coding And Transmission In Wireless Multi-Path Multi-Hop Networks, Nan Cen, Zhangyu Guan, Tommaso Melodia
Compressed Sensing Based Low-Power Multi-View Video Coding And Transmission In Wireless Multi-Path Multi-Hop Networks, Nan Cen, Zhangyu Guan, Tommaso Melodia
Computer Science Faculty Research & Creative Works
Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and …
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …