Reinforcement learning based effective communication strategies for energy harvested WBAN

Roy, Moumita; Biswas, Dipanjana; Aslam, Nauman; Chowdhury, Chandreyee

doi:10.1016/j.adhoc.2022.102880

Cited by 13 publications

(1 citation statement)

References 34 publications

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“…where re E and represent the remaining energy and initial of node n, respectively. ae E donates the average energy of the node group composed of adjacent nodes n [16].…”

Section: Design Of the Reward In Dqn Networkmentioning

confidence: 99%

A Data Transmission Path Optimization Protocol for Heterogeneous Wireless Sensor Networks Based on Deep Reinforcement Learning

Song,

Liu,

2023

JCC

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Wireless sensor networks had become a hot research topic in Information science because of their ability to collect and process target information periodically in a harsh or remote environment. However, wireless sensor networks were inherently limited in various software and hardware resources, especially the lack of energy resources, which is the biggest bottleneck restricting their further development. A large amount of research had been conducted to implement various optimization techniques for the problem of data transmission path selection in homogeneous wireless sensor networks. However, there is still great room for improvement in the optimization of data transmission path selection in heterogeneous wireless sensor networks (HWSNs). This paper proposes a data transmission path selection (HDQNs) protocol based on Deep reinforcement learning. In order to solve the energy consumption balance problem of heterogeneous nodes in the data transmission path selection process of HWSNs and shorten the communication distance from nodes to convergence, the protocol proposes a data collection algorithm based on Deep reinforcement learning DQN. The algorithm uses energy heterogeneous super nodes as AGent to take a series of actions against different states of HWSNs and obtain corresponding rewards to find the best data collection route. Simulation analysis shows that the HDQN protocol outperforms mainstream HWSN data transmission path selection protocols such as DEEC and SEP in key performance indicators such as overall energy efficiency, network lifetime, and system robustness.

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“…where re E and represent the remaining energy and initial of node n, respectively. ae E donates the average energy of the node group composed of adjacent nodes n [16].…”

Section: Design Of the Reward In Dqn Networkmentioning

confidence: 99%

A Data Transmission Path Optimization Protocol for Heterogeneous Wireless Sensor Networks Based on Deep Reinforcement Learning

Song,

Liu,

2023

JCC

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Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

Menaga,

Vanithamani

2024

Int J Communication

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SummaryWireless body area network (WBAN), a popular radio communication technique, tracks patients' health conditions remotely using small, low‐power, portable sensors positioned on the body to sense vital signs and send data to a control node. To sustain long‐term health monitoring, WBANs require significant energy from the nodes. This research proposes an energy efficient and reliable data transmission using multivariate gradient divergence African buffalo optimization (EEMGDABO) to minimize the delay of critical node data transmission by identifying the optimal path to the control node. The protocol comprises two key processes: winsorized correlative segmented symbolic regression with reinforcement learning (WCS‐SRRL) and the walrus optimization algorithm (WaOA) for accurate priority‐based classification of health data (normal, abnormal, and critical). The second process involves transferring these priority‐based data with reduced energy consumption and delay while increasing throughput by determining the best path from the critical node to the destination (hospital/doctor) using EEMGDABO, which calculates node distance and energy fitness value. Implemented in MATLAB with a health monitoring WBAN dataset, the proposed protocol demonstrates 23% less energy consumption, 12.33% less delay, and 23.44% higher network lifetime compared to existing optimization approaches.

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IoT-Based Human Activity Recognition for Smart Living

Saha

Roy

Chowdhury

2023

EAI/Springer Innovations in Communication and Computing

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Reinforcement learning based effective communication strategies for energy harvested WBAN

Cited by 13 publications

References 34 publications

A Data Transmission Path Optimization Protocol for Heterogeneous Wireless Sensor Networks Based on Deep Reinforcement Learning

A Data Transmission Path Optimization Protocol for Heterogeneous Wireless Sensor Networks Based on Deep Reinforcement Learning

Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

IoT-Based Human Activity Recognition for Smart Living

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