Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization

Wang, Zongshan; Ding, Hongwei; Li, Bo; Bao, Liyong; Yang, Zhen; Liu, Qianlin

doi:10.1007/s11277-022-09651-9

Cited by 27 publications

(6 citation statements)

References 40 publications

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“…Cluster-based routing methods have been widely explored and studied in the context of WSN. [19][20][21][22][23][24][25] Several survey papers have been conducted to analyze and summarize the advancements, challenges, and outcomes of cluster-based routing methods in WSNs. These survey papers provide valuable insights into the state-of-the-art techniques and shed light on the strengths and limitations of various cluster-based routing protocols.…”

Section: Literature Reviewmentioning

confidence: 99%

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Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

Verma

Bhatia

Zeadally

et al. 2023

Int J Communication

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SummaryA wireless sensor network (WSN) is a network of tiny sensors deployed to collect data. These sensors are powered with batteries that have limited power. Recharging and/or replacement of these batteries, however, are not always feasible. Over the past few years, WSN applications are being deployed in diverse fields such as military, manufacturing, healthcare, agriculture, and so on. With the ever‐increasing applications of WSNs, improving the energy efficiency of the WSNs still remains to be a challenge. Applying fuzzy logic to the problem of clustering exploits the uncertainty associated with the factors that affect the lifetime of these sensors and enables the development of models that would improve their performance in real‐world applications. We present a comprehensive review of various fuzzy‐based techniques for clustering in WSNs whose main goal is to optimize energy usage in WSNs while simultaneously improving their overall performance.

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Section: Literature Reviewmentioning

confidence: 99%

“…Cluster‐based routing methods have been widely explored and studied in the context of WSN 19–25 . Several survey papers have been conducted to analyze and summarize the advancements, challenges, and outcomes of cluster‐based routing methods in WSNs.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

Verma

Bhatia

Zeadally

et al. 2023

Int J Communication

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“…Usually, the nodes in WSNs were placed in long distance from destiny node must route its data packets through multihop because of the coverage and distance issues. Now, rises the communications for reaching the distant nodes and therefore it requires support of others for routing effectively [9]. Additionally, by not having a centralized control, if all sensors were interacting within themselves for reaching a destiny without an appropriate routing method, the number of messages interchanged would rise that result in the existence of congestion in network [10].…”

Section: Introductionmentioning

confidence: 99%

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Barnwal

Prakash

Yadav

2023

Wireless Pers Commun

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Wireless sensor network (WSN) plays a crucial role in the Internet of Things (IoT), which assist to produce seamless information that have a great impact on the network lifetime. Despite the substantial application of the WSN numerous challenges like energy, load balancing, security, and storage exist. Energy efficacy is regarded as an integral part of the design of WSN; this can be achieved by clustering and multi-hop routing technique using metaheuristic optimization algorithm. This paper concentrates on design of metaheuristics cluster-based routing technique for energy-efficient WSN (MHCRT-EEWSN). The presented MHCRT-EEWSN technique mainly concentrates on the improvements of energy efficiency and lifespan of the WSN via clustering and routing process. For effectual clustering process, the MHCRT-EEWSN model utilizes whale moth flame optimization (WMFO) technique can be utilized by the use of fitness function involving intra-cluster distance, inter-cluster distance, energy, and balancing factor. Besides, the MHCRT-EEWSN model employs improved African buffalo optimization (IABO) based routing technique. To select optimal routes in WSN, the IABO algorithm designs a fitness function comprising multiple parameters like residual energy and distance factor. The experimental validation of the MHCRT-EEWSN model can be tested by making use of a series of simulations. A wide-ranging comparative study shows the promising performances of the MHCRT-EEWSN model than other recent methods.

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“…The ACO algorithm is admired by researchers because of its unique advantages in solving business travel problems. Besides, many excellent nature-inspired swarm intelligent approaches have been validated to be effective in tricky global optimization projects, they include but are not limited to: bat algorithm (BA) ( Yang and Gandomi, 2012 ), krill herd optimization (KHO) ( Gandomi et al, 2012 ), cuckoo search (CS) algorithm ( Gandomi et al, 2013 ), fruit-fly optimization algorithm (FOA) ( Mitić et al, 2015 ), grey wolf optimizer (GWO) ( Mirjalili et al, 2014 ), moth-flame optimization (MFO) ( Mirjalili, 2015 ), grasshopper optimization algorithm (GOA) ( Abualigah and Diabat, 2020 ), whale optimization algorithm (WOA) ( Mirjalili and Lewis, 2016 ), marine predators algorithm (MPA) ( Faramarzi et al, 2020a ), white shark optimizer (WSO) ( Braik et al, 2022 ), starling murmuration optimizer (SMO) ( Zamani et al, 2022 ), harris hawks algorithm ( Heidari et al, 2019 ), squirrel search optimization (SSO) algorithm ( Jain et al, 2019 ), dragonfly algorithm (DA) ( Mirjalili, 2016 ), chimp optimization algorithm (ChOA) ( Khishe and Mosavi, 2020 ), rat swarm algorithm (RSA) ( Dhiman et al, 2021 ), Animal migration optimization (AMO) ( Li et al, 2014 ), butterfly optimization algorithm (BOA) ( Arora and Singh, 2019 ), emperor penguin optimizer (EPO) ( Dhiman and Kumar, 2018 ), tunicate swarm algorithm (TSA) ( Kaur et al, 2020 ), horse herd optimization algorithm (HOA) ( MiarNaeimi et al, 2021 ), monarch butterfly optimization (MBO) ( Wang et al, 2019 ), firefly algorithm ( Fister et al, 2013 ; Wang et al, 2022a ), and seagull optimization algorithm (SOA) ( Dhiman and Kumar, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Wang

Ding

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

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Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization

Cited by 27 publications

References 40 publications

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

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