LEACH-based hierarchical energy efficient routing in wireless sensor networks

Kandris, Dionisis; Evangelakos, Emmanouil Andreas; Rountos, Dimitrios; Tselikis, George S.; Anastasiadis, Eleftherios

doi:10.1016/j.aeue.2023.154758

Cited by 9 publications

(3 citation statements)

References 48 publications

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“…where xi, yi, and zi=the coordinates of device Si and xj, yj, and zj are the coordinates of device Sj. The estimated hop count is computed as (10):…”

Section: 𝑄(𝑠 𝑎) = 𝑄(𝑠 𝑎) + 𝛼(𝑟 + 𝛾 𝑚𝑎𝑥(𝑄(𝑠′ 𝑎′)) − 𝑄(𝑠 𝑎))mentioning

confidence: 99%

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Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Bhimshetty,

Victor Ikechukwu

2024

IJEECS

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<div align="center"><span>The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.</span></div>

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“…where xi, yi, and zi=the coordinates of device Si and xj, yj, and zj are the coordinates of device Sj. The estimated hop count is computed as (10):…”

Section: 𝑄(𝑠 𝑎) = 𝑄(𝑠 𝑎) + 𝛼(𝑟 + 𝛾 𝑚𝑎𝑥(𝑄(𝑠′ 𝑎′)) − 𝑄(𝑠 𝑎))mentioning

confidence: 99%

“…Devices in the IoT include smartphones, wireless sensors, and RFID tags, among others [5], [6]. A typical sensor has parts for power, sensing, processing, and communicating [7] [10]. While the power part supplies energy to others, the other parts use very little energy [11] [13].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Bhimshetty,

Victor Ikechukwu

2024

IJEECS

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“…However, its disadvantages deteriate the network performance, including random selection of CHs without considering parameters such as residual energy and node degree, single-hop data forwarding between CH and BS, and CM joining the nearest CH to form cluster based on received signal strength. Therefore, a good many of improved clustering and routing protocols have been proposed to enhance the overall performance of the network [32,33]. However, as a well known NP-hard problem, traditional clustering and routing protocol is inefficient or even impossible to solve this non-polynomial issue, not to mention the uncertain, dynamic and self-organizing nature of the network [2,6].…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Clustering and Routing Protocol With Rules Tuned by Improved Particle Swarm Optimization for Wireless Sensor Networks

Yuebo,

Haitao,

Hongyan

et al. 2023

IEEE Access

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Prognostic‐Based Active Battery Degradation Management in Wireless Sensor Networks for Group Replacement

Wu,

Zhou,

et al. 2024

Quality & Reliability Eng

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Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio.

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LEACH-based hierarchical energy efficient routing in wireless sensor networks

Cited by 9 publications

References 48 publications

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Fuzzy Clustering and Routing Protocol With Rules Tuned by Improved Particle Swarm Optimization for Wireless Sensor Networks

Prognostic‐Based Active Battery Degradation Management in Wireless Sensor Networks for Group Replacement

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