Evolving a clustering algorithm for wireless sensor network using particle swarm optimisation

Solaiman, Basma; Sheta, Alaa

doi:10.1504/ijsi.2016.077433

Cited by 8 publications

(4 citation statements)

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“…In practice, each MN sends the sensed data to its CH, which subsequently sends it to BS through one-hop or next hop based multi-hop transmission. Clustering protocol is thought to be an apt technique for nodes in WSNs to save energy, because clustering structure plays a major role on improving network lifetime due to balanced and efficient node energy usage [13,14,17,24]. Moreover, the clustering structure in WSN has further advantages such as topology management, resource management, data aggregation and routing etc.…”

Section: Introductionmentioning

confidence: 99%

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Krishnan¹,

Raja²,

Divakaran

2023

ITC

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Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).

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Section: Introductionmentioning

confidence: 99%

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Krishnan¹,

Raja²,

Divakaran

2023

ITC

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“…Some of the well-known metaheuristic approaches are Genetic Algorithms [19], Genetic Programming [20], [22], Particle Swarm Optimization [23], [24], Simulated Annealing [25], Artificial Bee Colony (ABC) [26], Cuckoo Search [27][28], crow search algorithm [29].…”

Section: Introductionmentioning

confidence: 99%

Solving economic dispatch and unit commitment problem in smart grid system using eagle strategy based crow search algorithm

Habachi¹,

Touil

Boulal

et al. 2019

IJEECS

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<p><span lang="EN-US">The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve economic dispatch and unit commitment problem in smart grid system.</span></p>

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“…In the past two decades, the literature of metaheuristic search has expanded extensively. Some of the well-known metaheuristic approaches are Genetic Algorithms [17], Genetic Programming [18][19][20], Particle Swarm Optimization [21][22], Simulated Annealing [23], Artificial Bee Colony (ABC) [24], Cuckoo Search [25][26], etc.…”

Section: Introductionmentioning

confidence: 99%

Resolution of economic dispatch problem of the morocco network using crow search algorithm

Habachi

Touil

Boulal

et al. 2019

IJEECS

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The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes use of Crow Search Algorithm (CSA), for solving the economic dispatch of two electricity networks: a testing system 6 units and the morocco network the crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows..The results obtained by CSA are compared with various results obtained in the literature. Simulation results shows that using CSA can lead to finding stable and adequate power generated that can fulfill the need of both the civil and industrial areas.

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Evolving a clustering algorithm for wireless sensor network using particle swarm optimisation

Cited by 8 publications

References 0 publications

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Solving economic dispatch and unit commitment problem in smart grid system using eagle strategy based crow search algorithm

Resolution of economic dispatch problem of the morocco network using crow search algorithm

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