Hybridizing Biogeography-Based Optimization With Differential Evolution for Optimal Power Allocation in Wireless Sensor Networks

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is...

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Bibliographic Details
Published in:	IEEE transactions on vehicular technology Vol. 60; no. 5; pp. 2347 - 2353
Main Authors:	Boussaid, I, Chatterjee, A, Siarry, P, Ahmed-Nacer, M
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.06.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Allocations Applied sciences Biogeography-based optimization (BBO) algorithm Computer Science Constraints Covariance matrix Detection, estimation, filtering, equalization, prediction differential evolution (DE) algorithm Error probability Evolution Exact sciences and technology Information, signal and communications theory Networks Noise Operations Research optimal power allocation Optimization Optimization algorithms Receivers Resource management Sensors Services and terminals of telecommunications Signal and communications theory Signal, noise Stochasticity Studies Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control wireless sensor network (WSN) Wireless sensor networks Performance evaluation differential evolution (DE) algorithm Error probability Evolutionary algorithm Wireless telecommunication Numerical method Biogeography-based optimization (BBO) algorithm Deterministic signal Stochastic method Power allocation Optimization Remote sensing Case study Optimal allocation wireless sensor network (WSN) Wireless network Optimal planning Sensor array Signal detection Comparative study optimal power allocation
ISSN:	0018-9545, 1939-9359
Online Access:	Get full text
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