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Paper Information

Journal:   ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY   FALL 2017 , Volume 5 , Number 3 ; Page(s) 31 To 49.
 
Paper: 

WIRELESS SENSOR NETWORKS DEPLOYMENT USING A CONSTRAINED MULTIOBJECTIVE EVOLUTIONARY APPROACH BASED ON DECOMPOSITION

 
 
Author(s):  KHALESIAN MINA, DELAVAR MOHMAOUD REZA*
 
* GIS DEPARTMENT, SCHOOL OF SURVEYING AND GEOSPATIAL ENGINEERING, COLLEGE OF ENGINEERING, UNIVERSITY OF TEHRAN, TEHRAN, IRAN
 
Abstract: 

Wireless sensors deployment is considered as one of the major and fundamental steps of wireless sensor networks (WSNs) design. One of the main challenges of sensors deployment is to find a trade-off between conflicting and competing objectives of the WSN including network coverage and lifetime under connectivity constraints. Besides, decomposition is a basic method in traditional multi-objective optimization and in recent decades, it has also been used for optimizing multi-objective evolutionary problems. In this paper, a constrained Pareto-based multi-objective evolutionary approach based on decomposition (CPMEA/D) is proposed for solving the sensors optimal deployment problem in a WSN. The aim of this approach is to decompose the multi-objective optimization problem into a number of scalar optimization subproblems and then to optimize them simultaneously for finding the Pareto optimal layouts in which the network coverage is maximized and the sensors energy consumption is minimized while the connectivity between each sensor node and the high energy communication node (i.e. sink) is maintained. In this paper, the comparison of the common performance metrics indicates that the proposed approach has made significant improvements on the overall performance of the CPMEA. Moreover, the simulation results on a WSN test instance have shown the superiority of the proposed approach (i.e. CPMEA/D) over the CPMEA and a diverse set of high quality designed networks has been provided to facilitate decision maker’s choices.

 
Keyword(s): WIRELESS SENSOR NETWORK, DEPLOYMENT, CONSTRAINED PARETO-BASED MULTI-OBJECTIVE EVOLUTIONARY APPROACH, OPTIMIZATION
 
References: 
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