Deep-q-Networks-Based Adaptive Dual-Mode Energy-Efficient Routing in Rechargeable Wireless Sensor Networks

Guo, Haobo; Wu, Runze; Qi, Bing; Chen, Xu

doi:10.1109/jsen.2022.3163368

Cited by 15 publications

(2 citation statements)

References 31 publications

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“…It is noteworthy that DRL has been widely applied to address optimization problems under uncertainty [ 15 , 16 ]. For instance, the remarkable success of AlphaGO in recent years [ 17 ] and the superior performance of reinforcement learning in electronic games [ 18 ] have sparked interest in utilizing DRL to solve optimization problems in WSN.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Yuan,

Peng,

Yan

et al. 2024

Sensors

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The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Yuan,

Peng,

Yan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…To increase network longevity and reduce resource management complexity currently present in WSNs, novel network topologies are needed [1]. The combination of Direct upload routing and multi-hop routing into a suggested Router with adaptive dual-mode energy efficiency [2]. an enhanced ant colony optimizationbased routing technique for directing traffic between the main station and the cluster's heads [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Path Selector in Wireless Sensor Networks

B.Harish Goud

2024

jes

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The Sensor Network is a system with a more number of sensor nodes that can maintain itself. The dispersed nodes sensor converses with one another. the node sensors gather data and transmit it to the base station. The deployment of the WSN spans many different applications, such secure military software, healthcare systems, and monitoring software. Numerous studies have concentrated on boosting communication efficiency and increasing network lifetime. In The conventional style Base station, sensor nodes, and cluster heads (CH) make up the typical WSN architecture. Traditional sensor node communication methods use a lot of energy, cause delays, and have poor network performance. To solve the system's restriction in its current state in this paper, it was suggested to implement Path selector a special node which contains all clusters information in WSN and their sensor energy information of all sensor nodes so that directly we can send data to destination sensor node fast without any delay, energy consumption. There by we can improve packet delivery ratio, throughput decrease delay.

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BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control

Mutneja,

Harkut,

Thakar

2024

Lecture Notes in Networks and Systems

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Deep-q-Networks-Based Adaptive Dual-Mode Energy-Efficient Routing in Rechargeable Wireless Sensor Networks

Cited by 15 publications

References 31 publications

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Novel Path Selector in Wireless Sensor Networks

BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control

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