Chain Based Leader Selection using Neural Network in Wireless Sensor Networks protocols

El-Sayed, Hamdy H.; Refaay, Shereen K.; Ali, Shahzad; El-Melegy, Moumen T.

doi:10.1109/jac-ecc54461.2021.9691426

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…PEGASIS-NN [33] Power-Efficient Gathering in Sensor Information Systems Neural Network routing protocol, begins with chain construction using the Greedy algorithm, similar to the PEGASIS protocol. Next, it employs a Neural Network (NN) to determine the chain leader by supplying the energies of all nodes as input to the NN and selecting the node with an output value of "1" as the chain leader.…”

Section: Pegasis-nn Protocol Results For the Proposed Optimized Wsnsmentioning

confidence: 99%

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Refaay,

Ali,

El-Melegy

et al. 2024

CMC

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The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network's lifetime. Many studies have been conducted for homogeneous networks, but few have been performed for heterogeneous wireless sensor networks. This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies. The proposed algorithms lack algorithm-specific parameters and metaphorical connotations. The proposed algorithms examine the search space based on the relations of the population with the best, worst, and randomly assigned solutions. The proposed algorithms can be evaluated using any routing protocol, however, we have chosen the well-known routing protocols in the literature: Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEAGSIS), Partitioned-based Energy-efficient LEACH (PE-LEACH), and the Power-Efficient Gathering in Sensor Information Systems Neural Network (PEAGSIS-NN) recent routing protocol. We compare our optimized method with the Jaya, the Particle Swarm Optimization-based Energy Efficient Clustering (PSO-EEC) protocol, and the hybrid Harmony Search Algorithm and PSO (HSA-PSO) algorithms. The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime (first dead node, half dead nodes, and last dead node), energy consumption, packets to cluster head, and packets to the base station. The experimental results were compared with those obtained using the Jaya optimization algorithm. The proposed algorithms exhibited the best performance. The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol, 51% for the LEACH protocol, 10% for the PE-LEACH protocol, and 73% for the PEGSIS-NN protocol; Moreover, it enhances other criteria such as energy conservation, fitness convergence, packets to cluster head, and packets to the base station.

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Section: Pegasis-nn Protocol Results For the Proposed Optimized Wsnsmentioning

confidence: 99%

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Refaay,

Ali,

El-Melegy

et al. 2024

CMC

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“…Therefore, fault tolerance is critical to maintaining the reliability and performance of WSNs, especially in applications where the data collected is critical to decision-making processes. 3 Extending the lifetime of WSN can be achieved by employing one approach, which involves enhancing routing protocols. This can be achieved by intelligently using all network elements in data transmission through the use of probability functions.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of WSNs, fault tolerance ensures that the system can continue to function even if one or more sensor nodes fail due to power loss or physical damage. Therefore, fault tolerance is critical to maintaining the reliability and performance of WSNs, especially in applications where the data collected is critical to decision‐making processes 3 …”

Section: Introductionmentioning

confidence: 99%

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

Jalili,

Alzubi,

Rezaei

et al. 2024

Concurrency and Computation

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SummaryWireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

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Chain Based Leader Selection using Neural Network in Wireless Sensor Networks protocols

Cited by 2 publications

References 21 publications

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

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