Energy Balanced Clustering and Data Gathering for Large-Scale Wireless Sensor Networks

Mamalis, Basilis; Perlitis, Marios

doi:10.1145/3139367.3139425

Cited by 1 publication

(2 citation statements)

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“…If we assume that A i stands as the agent i, every item x i d (t) associates the www.ijacsa.thesai.org relevant cluster-head to some gateway and 1 ≤ i ≤ N A , whereas also 1 ≤ d ≤ D (note that D equals to c). So, each agent may be denoted in the following form [17,33], whereas our modified GSA protocol should then run as follows. The optimal CHs associations to gateway-nodes…”

Section: B Second Level Gsa-based Clusteringmentioning

confidence: 99%

“…The remaining energy of the sensors is chosen as the basic factor while building the clusters, so a sufficiently balanced cluster hierarchy is built, the energyhole problem (near to the cluster-heads) is effectively handled, and eventually the operation of the network is prolonged. A detailed description of the relevant procedure is given in [33].…”

Section: A First Level Energy-balanced Clusteringmentioning

confidence: 99%

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Energy Balanced Two-level Clustering for Large-scale Wireless Sensor Networks based on the Gravitational Search Algorithm

Mamalis¹,

Perlitis²

2019

IJACSA

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Organizing sensor nodes in clusters is an effective method for energy preservation in a Wireless Sensor Network (WSN). Throughout this research work we present a novel hybrid clustering scheme that combines a typical gradient clustering protocol with an evolutionary optimization method that is mainly based on the Gravitational Search Algorithm (GSA). The proposed scheme aims at improved performance over large in size networks, where classical schemes in most cases lead to non-efficient solutions. It first creates suitably balanced multihop clusters, in which the sensors energy gets larger as coming closer to the cluster head (CH). In the next phase of the proposed scheme a suitable protocol based on the GSA runs to associate sets of cluster heads to specific gateway nodes for the eventual relaying of data to the base station (BS). The fitness function was appropriately chosen considering both the distance from the cluster heads to the gateway nodes and the remaining energy of the gateway nodes, and it was further optimized in order to gain more accurate results for large instances. Extended experimental measurements demonstrate the efficiency and scalability of the presented approach over very large WSNs, as well as its superiority over other known clustering approaches presented in the literature.

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Section: B Second Level Gsa-based Clusteringmentioning

confidence: 99%

Section: A First Level Energy-balanced Clusteringmentioning

confidence: 99%