Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Summary Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

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A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

Pratha

Asanambigai

Mugunthan³

2023

“…It ignored some of the potential factors that introduces the impact during the selection of CHs in the clustering phenomenon Daneshvar et al 26 Gray wolf optimizer-based clustering approach…”

Section: Distributed Approachmentioning

confidence: 99%

Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Kumar,

Subramanyam,

Kumar

2023

SummaryClustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real‐life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade‐off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.

“…For optimal CH selection, this work 23 proposes a strategy based on an expanded version of the bat and artificial bee colony optimization algorithm (IBEABCCR), which has the benefits of both increased variety and a faster convergence rate. The study aimed to reduce energy consumption by developing a superior CH selection method with the use of an enhanced bat optimization algorithm (IBOA).…”

Section: Literature Surveymentioning

confidence: 99%

SMEOR: Sink mobility‐based energy‐optimized routing in energy harvesting‐enabled wireless sensor network

Gupta,

Kumar Rout

2023

SummaryFor accurate environmental monitoring, a wireless sensor network (WSN) uses a collection of autonomous sensor nodes (SNs) that are dispersed at random across a given area. WSNs are used for various applications that require real‐time data. While the SNs in a WSN are limited in their ability to generate energy, the routing process is seen as a vital means of improving the network's ability to conserve resources and prolong its useful life. In this research work, we present sink mobility‐based energy‐optimized routing (SMEOR) in energy harvesting (EH)‐enabled WSN. While following the cluster‐based routing, the election of cluster head (CH) is executed using our proposed extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy‐hole problem, four data‐collection energy‐unlimited nodes are used around the periphery of the network. The sink moves from one EH‐enabled node to another to collect data. Further, security in SMEOR is ensured by encrypting the data using a stream cipher and a stochastically generated security key. It is evident from the simulation analysis that SMEOR delivers network longevity and supreme performance.