Cluster Optimization Based on Metaheuristic Algorithms in Wireless Sensor Networks

Mekonnen, Melaku Tamene; Rao, K. Nageswara

doi:10.1007/s11277-017-4627-z

Cited by 22 publications

(6 citation statements)

References 22 publications

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“…Looking at the distance reduction, however, the provided approach produced higher average distance reduction compared to [18] in most cases and CHs percentage is less. Comparing the presented SimE approach to other metaheuristics, SimE is about 8% better than PSO and SA algorithms presented in [23], which utilized mostly the same configuration and assumptions that were presented in this work.…”

Section: B Network Lifetimementioning

confidence: 95%

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Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Alsayyari¹

2018

ijacsa

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Energy consumption minimization is crucial for the constrained sensors in wireless sensor networks (WSNs). Partitioning WSNs into optimal set of clusters is a promising technique utilized to minimize energy consumption and to increase the lifetime of the network. However, optimizing the network into optimal set of clusters is a non-polynomial (NP) hard problem, and the time needed to solve such problem increases exponentially as the number of sensors increases. In this paper, simulated evolution (SimE) algorithm is engineered to tackle the problem of cluster optimization in WSNs. A goodness measure is developed to measure the accuracy of assigning nodes to clusters and to evaluate the clustering quality of the overall network. SimE was developed such that the number of clusters and cluster heads are adaptive to number of alive nodes in the network. In fact, extensive simulation results demonstrate that SimE provides near optimal clustering and improves the lifetime of the network by about 21% compared to the traditional LEACH-C protocol.

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Section: B Network Lifetimementioning

confidence: 95%

“…Furthermore, the study presented in [23] provided WSNs clustering algorithms based on simulated annealing (SA) and PSO algorithms. Their approach was presented to provide better clustering when compared to LEACH protocol.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Alsayyari¹

2018

ijacsa

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“…Kuila and Jana [22], a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. WSN polynomial temporal clustering algorithms named common scrambling algorithm (CSA) and chaotic particle swarm optimization (CPSO) relying respectively on simulated annealing (SA) and particle swarm optimization (PSO) have been discussed in [23], [24].…”

Section: Overviewmentioning

confidence: 99%

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Diakhate

Niang²,

Kora³

et al. 2023

IJEECS

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Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.

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“…For choosing the appropriate mechanisms, various factors have been considered such as problem type, time constraint, availability of resources, accuracy desired. To achieve the better performance of the network, the researchers have used many approaches in nature inspired mechanisms such as, classical approaches [28][29][30][31][32] and swarm intelligence-based approaches [33][34][35][36][37][38][39][40][41][42][43]. There are various types of mechanisms present in the literature focused on the different types of problem faced by the wireless sensor networks named as Optimal Scope, Data Aggregation, Power Efficient Clustering, Power Efficient Routing mechanisms, Sensor lateralization [75][76].…”

Section: Wsn and Optimizationsmentioning

confidence: 99%

Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm

Mishra

Yadav

2023

Wireless Pers Commun

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WSN consist of tiny sensors which are distributed over the entire network having capabilities of sensing the data, processing it and convey it from one node to another node. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, the nature inspired mechanisms are used to minimize the power utilization of the network. In the proposed study Butterfly Optimization Mechanism is used to choose the optimum quantity of CH from the dense nodes. The parameter is to be considered for the choosing of the CH is the remaining power of the node, interspace from the other nodes in the network, interspace from the BS, node centrality and node degree. The PSO is used to form the CH by choosing certain parameters such as interspace from the CH and the BS. The path is choosing by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the interspace, node degree and the choose remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the BS and the overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, EDEEC [50-53] and also correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, FLION. This review will help investigators to discover the applications in this arena.

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Cluster Optimization Based on Metaheuristic Algorithms in Wireless Sensor Networks

Cited by 22 publications

References 22 publications

Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm

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