Authors:
Xing-Jia Lu
1
;
Zhi-Rong Chen
1
;
Lin Guo
2
and
Yong-Sheng Ding
2
Affiliations:
1
Ningbo University of Technology, China
;
2
Ningbo University of Technology and Donghua University, China
Keyword(s):
Wireless sensor networks, Quality of service, Multi-objective immune co-evolutionary algorithm.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Software Agents and Internet Computing
;
Telecommunications
;
Wireless and Mobile Computing
;
Wireless and Mobile Technologies
;
Wireless Information Networks and Systems
Abstract:
Quality of Service(QoS) is the the performance level of a service offered by the wireless sensor networks (WSNs) to user, which is an important topic of WSNs. The goal of QoS is to achieve a more deterministic network behavior. QoS of WSNs is an extension of the multi-objective optimization problem, which is modelled as a optimal model with constraint of network connection. The QoS must satisfy the multi-objectives such as energy consumption, bandwidth, delay jitter, packer loss rate. In order to search the optimal solution of the QoS of WSNs, we propose a multi-objective immune co-evolutionary algorithm (MOICEA) for QoS of WSNs. The MOICEA is inspired from the biological mechanisms of immune systems including clonal proliferation, hypermutation, co-evolution, immune elimination, and memory mechanism. The affinity between antibody and antigen is used to measure the optimal set of QoS, and the affinity between antibodies and antibodies is used to evaluate the diversity of population a
nd to instruct the population evolution process. In order to examine the effectiveness of the MOICEA, we compare its performance with that of genetic algorithm
(GA) in terms of four objectives while maintaining network connectivity. The experiment results show that the MOICEA could obtain promising performance in efficiently searching optimal solution by comparing with other approaches.
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