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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
engineering
Computational intelligence in wireless sensor networks: A survey
IEEE Communications Surveys and Tutorials, Volume 13, No. 1, Article 5473889, Year 2011
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Description
Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs. © 2005 IEEE.
Authors & Co-Authors
Förster, Anna
Switzerland, Manno
Scuola Universitaria Professionale Della Svizzera Italiana
Venayagamoorthy, Ganesh Kumar
United States, Rolla
Missouri University of Science and Technology
Statistics
Citations: 698
Authors: 2
Affiliations: 2
Identifiers
Doi:
10.1109/SURV.2011.040310.00002
ISSN:
1553877X
Study Design
Cross Sectional Study
Study Approach
Quantitative