EPO Based Clustering and Secure Trust-Based Enhanced LEACH Routing in WSN

Rajesh, L.; Mohan, H S

doi:10.1007/978-981-16-6605-6_3

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…To address the security issues in routing, Ref. [51] introduced a trust-enabled secure method enhancing LEACH (STELR). Emperor Penguin Optimization (EPO) is utilized to determine the CH for data aggregation.…”

Section: Related Workmentioning

confidence: 99%

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Osamy

Khedr

Alwasel

et al. 2023

Sensors

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Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

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Section: Related Workmentioning

confidence: 99%

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Osamy

Khedr

Alwasel

et al. 2023

Sensors

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TEDCTSSA: Trust Enabled Data Collection Technique Based Sparrow Search Algorithm for WSN-Based Applications

Osamy

Khedr

Salim

et al. 2023

Lecture Notes in Computer Science

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Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Saad

Jaafar

Chellappan

2023

Sensors

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Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

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EPO Based Clustering and Secure Trust-Based Enhanced LEACH Routing in WSN

Cited by 3 publications

References 15 publications

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

TEDCTSSA: Trust Enabled Data Collection Technique Based Sparrow Search Algorithm for WSN-Based Applications

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

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