GKFCR: An Improved Clustering Routing Algorithm for Wireless Sensor Networks

Li, Hongbing; Ou, Jun; Cui, Hao; Zhao, Shangfei; Zeng, Die; Wang, Yuning

doi:10.1109/sdpc55702.2022.9915965

Cited by 2 publications

(2 citation statements)

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“…2) Remaining Energy: Indicates the remaining energy level of the current node. As shown in Figure 4, by including these additional fields in the header, the data packet incorporates important node-specific information that aids in the efficient execution of reinforcement learning algorithms while ensuring effective communication among nodes [19].…”

Section: Dqn-based Algorism For Hwsns Routingmentioning

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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Section: Dqn-based Algorism For Hwsns Routingmentioning

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 Network (WSN) [1,2] is a type of large-scale ad-hoc network that has small devices, sensors, and modern computing components. This network is properly monitored and controlled with the use of sensors, many wireless nodes, and low power computing devices.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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GKFCR: An Improved Clustering Routing Algorithm for Wireless Sensor Networks

Cited by 2 publications

References 0 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

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

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