Joint mission and communication aware node placement problem in mission-specific mobile sensor networks

Ouchitachen, Hicham; Haïr, Abdellatif; Idrissi, Najlae

doi:10.1109/wcccs.2014.7107905

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…It is also remarkable the work of Ouchitachen et al [16], where a genetic algorithm based on two criteria (cost and communication quality) are used to optimize the energy usage in mobile WSNs.…”

Section: Multi-objective Optimization For Rn Placementmentioning

confidence: 98%

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A Dynamical Relay node placement solution for MANETs

Magán-Carrión

Camacho

García-Teodoro

et al. 2017

Computer Communications

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Section: Multi-objective Optimization For Rn Placementmentioning

confidence: 98%

“…In particular, PSO has been selected due to its wide usage by the research community for RN placement problems [8,9,10,14]. However, Genetic Algorithms (GAs) are a close variants of evolutionary solutions that could be suitable for this approach as well [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Dynamical Relay node placement solution for MANETs

Magán-Carrión

Camacho

García-Teodoro

et al. 2017

Computer Communications

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“…The works established in these critical cases are so rare. Our previous papers [40] and [41] are two recent proposals in this context, where we proposed some new algorithms that accurately find the best locations of sensors and BS while minimizing the average energy consumed in the network.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Weighted Clustering Algorithm Minimizing Jointly the Costs of Mission and Communication in Wireless Sensor Network

Ouchitachen¹,

Haïr²,

Idrissi³

2015

IJCA

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Wireless sensor networks (WSNs) have recently gained the attention of researchers in many challenging aspects. The energy conservation is one of the most important issues in these networks. Due to the limited access to the nodes, both the network structure and the manner of communication between the nodes decide the energy expenditure in WSNs. One of the best solutions, in this context, is to cluster the network. This paper presents a new clustering algorithm for solving the energetic constraint in WSNs. More precisely, a critical network is considered, where each sensor satisfies its own missions depending on its locations. In addition to fulfill their mission, the sensor tries to maintain a good neighboring nodes quality. First, the mission and communication costs of sensors are minimized jointly using Sensor's Genetic Algorithm (SGA), then the Multi-Objective Weighted Clustering Algorithm (MOWCA) is developed. It aims at dividing a network into different clusters and at selecting the best performing sensors in terms of power to communicate with the Base Station (BS). MOWCA is based on tree critical parameters. : Degree Difference of sensor i, : Sum of distances between sensor i and its neighbors and : Mission distance of sensor i. Later on and in order to balance energy consumed in different formed clusters, the Base Station Genetic Algorithm (BGA) is established. Simulation results demonstrate that the proposed algorithms are very advantageous in terms of convergence to the appropriate locations and are so efficient in regards to energy conservation in WSNs.

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