Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks

Wang, Tianshu; Zhang, Gongxuan; Yang, Xichen; Vajdi, Ahmadreza

doi:10.1016/j.jss.2018.09.067

Cited by 101 publications

(58 citation statements)

References 33 publications

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“…The performance of OBC-WOA clearly exceeds the performances of the existing method such as FKM-CMA [17], WOA [18], PSO [12], and GSA [20]. Fig.…”

Section: Performance Evaluationmentioning

confidence: 87%

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OBC-WOA: Opposition-Based Chaotic Whale Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Network

Kumar¹,

Chaparala²

2019

IJIES

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A large number of small sensors in the Wireless Sensor Network (WSN) can be an efficient tool for data collection in a range of environments. Each sensor transfers the information to the basic unit that transmits the information to the end user. The objective of clustering is to separate the network into sectors with a cluster head (CH). The job is to collect, aggregate and transmit cluster heads to the base station. Energy Efficient Clustering has been used widely for energy conservation in wireless sensor networks (WSNs) and also preserves the restricted energy resources of their sensors. Cluster heads (CHs) perform a major part and exhaust power faster than other member nodes in a distributed WSN. Opposition-based Chaotic Whales Optimization algorithm (OBC-WOA) is meta-heuristic optimization algorithm which has recently been proposed in opposition. It simulates humpback whales ' social behaviour. OBC-WOA produces randomly its population during exploration and exploitation stages, like other population-based systems, which can produce values far from the optimal alternative or block the development of local optima. The revised algorithm known as Opposition-based Chaotic Whale Optimization Algorithm (OBC-WOA) is designed to increase solution precision and reliability. The OBC-WOA uses a technique based on opposition to improve the efficiency of OBC-WOA. The OBC-WOA is screened with the initial WOA algorithm and other meta-heuristic techniques. The performance of the proposed approach is evaluated in terms of energy consumption, throughput, packet delivery ratio and network life time. When compared with the existing method the proposed method is 12% better than Whale Optimization algorithm (WOA), 25% better than Gravitational Search Algorithm (GSA), 40% better than Particle Swarm Optimisation (PSO) and 8% better than Fuzzy K-Means and Centralized Mid-point Algorithm (FKM-CMA).

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“…The performance of OBC-WOA clearly exceeds the performances of the existing method such as FKM-CMA [17], WOA [18], PSO [12], and GSA [20]. Fig.…”

Section: Performance Evaluationmentioning

confidence: 87%

“…The significant energy usage in a specified round can be a reasonable assessment of the power effectiveness of the algorithm. The cumulative quantity of energy left in the battery at the end of the simulation shall be evaluated by calculating the cumulative quantity (in joules) [19,20]. (Eq.…”

Section: Energy Consumptionmentioning

confidence: 99%

OBC-WOA: Opposition-Based Chaotic Whale Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Network

Kumar¹,

Chaparala²

2019

IJIES

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“…Moreover, they considered the sensor nodes dead rate by utilizing different routing protocols in their research to verify and compare the results. The genetic algorithm-based energy-efficient clustering and routing approach (GECR) for load balancing of WSNs was suggested by Wang et al [34]. To resolve, the load balancing issue in WSNs an energy-efficient scalable routing algorithm (EESRA) was proposed by Elsmany et al [35].…”

Section: Literature Reviewmentioning

confidence: 99%

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

et al. 2020

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Owing to the limited resources of sensor nodes, we propose an efficient hybrid routing scheme using a dynamic cluster-based static routing protocol (DCBSRP), leveraging the ad hoc on-demand distance vector (AODV) routing protocol and low-energy adaptive clustering hierarchy (LEACH) protocol. In the proposed scheme, the cluster head (CH) nodes are formed dynamically for a fixed interval, whereas static routing is applied in the designated clusters by utilizing the AODV routing protocol. The static routing condition of the proposed scheme limits all connected nodes of the cluster for a defined interval of time (T) to share their collected information through a specific CH node. Once the time (T) interval is completed, all ordinary nodes connected with the specific CH are released and they are free to advertise their CH candidateship within the network. Likewise, the node receiving the maximum number of route replies (RREPs) is selected as the next CH node in the vicinity of deployed sensor nodes. However, with the DCBSRP protocol, the recently selected CH node does not advertise its candidateship for five consecutive cycles and acts as an ordinary node. The simulation result shows significant improvement in the lifetime and participation of ordinary nodes in the network until the end-stage of the network. In the proposed scheme, the participation of ordinary nodes in the network is 95.9 %, which not only balances load between participating nodes but also improves the network lifetime in the presence of field-proven schemes. Moreover, the simulation results show an out-performance of rival schemes in terms of communication cost, end to end delay, throughput, packet lost ratio, and energy consumption.

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“…Meanwhile, some shortcomings are also present in LEACH such as selecting nodes with low residual energy as CHs, distributing clusters unevenly, unbalancing energy consumption and increasing overhead of control messages due to its random CH selection and fixed round time. So great efforts have been made to improve the performance of LEACH from different aspects for clustering routing methods and expected results have been achieved since then [6][7][8][9][10]. Clustering routing method usually contains three main phases: clustering, routing, maintaining clusters.…”

Section: Introductionmentioning

confidence: 99%

“…Clustering usually consists of CHs selection and clusters formation. Many approaches have been provided for CHs selection, which can be categorized into probability based [4][5][6][11][12][13][14], weight based [7,[15][16][17] and heuristics based [9,10,[18][19][20] approaches. The nodes are selected as CHs when their threshold values are less than a random assigned real number between 0 and 1 in probability based approaches.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a Chaotic Genetic Algorithm

Wang

Liu

et al. 2020

IEEE Access

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In wireless sensor networks, organizing nodes into clusters, finding routing paths and maintaining the clusters are three critical factors that significantly impact the network lifetime. In this paper, using a chaotic genetic algorithm, a clustering routing protocol combined with these three features called CRCGA is proposed to improve the network energy efficiency and load balancing. In CRCGA, the chaotic genetic algorithm is used to select the best cluster heads (CHs) and to find the optimal routing paths by coding them into a single chromosome simultaneously. Chaotic genetic operators based on a novel fitness function considering minimum energy consumption and load balancing along with new determination conditions make the algorithm converge quickly. Besides, an adaptive round time considering energy and load balancing is presented to maintain the clusters so as to further reduce energy consumption. Simulation results indicate that CRCGA is better than LEACH, GECR, OMPFM and GADA-LEACH in terms of convergence speed, energy efficiency, load balancing, network throughput and lifetime. INDEX TERMS WSNs, Multi-hop routing, Chaotic genetic algorithm, Clustering, Energy and load Balancing.

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Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks

Cited by 101 publications

References 33 publications

OBC-WOA: Opposition-Based Chaotic Whale Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Network

OBC-WOA: Opposition-Based Chaotic Whale Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Network

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a Chaotic Genetic Algorithm

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