Low-energy dynamic clustering scheme for multi-layer wireless sensor networks

Liu, Xingchun; Yu, Jingjing; Zhang, Wenqi; Tian, Hui

doi:10.1016/j.compeleceng.2021.107093

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…The evaluation is mainly allocated based on the urgency of the data and the amount of data. For data types with high real-time requirements, we provide higher real-time evaluation to reduce delay; for periodic data with low real-time requirements, we base our calculations on the way the data are set up; the aggregation processing is designed to provide the network with a longer life cycle [26][27][28].…”

Section: Monitoring Objects and Requirementsmentioning

confidence: 99%

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning

Fu,

Peng,

2023

Sensors

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In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

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Section: Monitoring Objects and Requirementsmentioning

confidence: 99%

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning

Fu,

Peng,

2023

Sensors

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“…WSNs 2 with the Internet of Things (IoT) are gradually invading various fields through numerous applications, including military, environmental monitoring, commercial and medical applications. Nevertheless, networking approaches should provide better fault tolerance and QoS performance for newly developed applications 3 . In addition, due to the sensitivity of sensor nodes and the harsh environments where they are deployed, WSNs have some limitations that make them vulnerable to failures.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 9, the proposed solution utilizes the best path from the source to the destination with nodes that do not belong to any other path and in Reference 10, the solution tolerates failure based on several partially disjoint paths. Moreover, cluster‐based routing solutions provide preemptive fault tolerance, as the clustering approach improves network lifetime by minimizing message exchanges 3,11‐13 . In Reference 11, the proposed protocol aims at preserving the network coverage, where in case of failure of a cluster‐head (CH), each member of its cluster joins the closest active CH.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, cluster-based routing solutions provide preemptive fault tolerance, as the clustering approach improves network lifetime by minimizing message exchanges. 3,[11][12][13] In Reference 11, the proposed protocol aims at preserving the network coverage, where in case of failure of a cluster-head (CH), each member of its cluster joins the closest active CH. In Reference 14, the authors apply the minimum vertex algorithm to define the critical nodes whose failure decreases the k-connectivity value.…”

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Fault‐tolerance based on augmenting approach in wireless sensor networks

Belkadi

Lehsaini

Tahraoui

2022

Concurrency and Computation

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A critical node is a sensor whose failure causes loss of connectivity and network fragmentation. In wireless sensor networks, the failure of a critical node, like the failure of all sensor nodes, can be caused by energy depletion or physical failure. To overcome the problem of failure of such nodes, this article proposes a fault-tolerant strategy that allows a routing protocol to tolerate the failure of a critical node. The proposed strategy is carried out in two phases. In the first phase, a hybrid genetic algorithm with a local search heuristic called genetic algorithm-critical node problem (GA-CNP) is used to select the critical nodes and in the second phase, an algorithm called Aug-CNP is involved to deal with the augmentation problem by deploying additional wireless edges to preserve network connectivity in case of critical node failure. Our proposal has been developed using the OMNET++ simulator, evaluated, and compared to the AODV protocol. The simulation results showed that the GA-CNP algorithm selects the critical nodes whose failure can degrade the network lifetime with a rate of 40%. Moreover, the Aug-CNP algorithm applied to the AODV protocol brings improvements in terms of network lifetime which reaches 22% compared to the traditional AODV protocol.

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Energy-Efficient and Reliable Clustering with Optimized Scheduling and Routing for Wireless Sensor Networks

Mishra,

Verma

2024

Multimed Tools Appl

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Low-energy dynamic clustering scheme for multi-layer wireless sensor networks

Cited by 12 publications

References 8 publications

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning

Fault‐tolerance based on augmenting approach in wireless sensor networks

Energy-Efficient and Reliable Clustering with Optimized Scheduling and Routing for Wireless Sensor Networks

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