Paper Info
Title
QoS routing based on parallel elite clonal quantum evolution for multimedia wireless sensor networks
Abstract
Quality of Service (QoS) routing is one of the key enabling techniques for multimedia wireless sensor networks (WSNs). However, the multi-constraints QoS routing problem is an NP-hard problem, and the computational complexity of an exhaustive search over all the paths is too high for large scale multimedia WSNs. In this paper, a novel parallel elite clonal quantum evolutionary algorithm is proposed to solve the multi-constraints QoS routing problem. The proposed algorithm minimizes the energy consumption, while guaranteeing QoS performance, including delay, bandwidth, delay jitter and packet loss rate, in multimedia WSNs. The algorithm is tested by extensive simulations and its performance is compared with the genetic algorithm and ant colony optimization. Simulation results demonstrate that the proposed algorithm achieves lower energy consumption at a faster convergence rate than the other two evolutionary algorithms.
Year
DOI
Venue
2014
10.1109/WCNC.2014.6952781
WCNC
Keywords
Field
DocType
parallel elite clonal quantum quantum evolutionary algorithm,ant colony optimisation,qos routing,quality of service,jitter,communication complexity,packet loss rate,ant colony optimization,telecommunication power management,computational complexity,delay jitter,genetic algorithm,genetic algorithms,energy consumption,wireless sensor networks,telecommunication network routing,unicast routing,multimedia wireless sensor networks,quantum evolutionary algorithm,bandwidth,statistics,sociology,routing
Key distribution in wireless sensor networks,Link-state routing protocol,Dynamic Source Routing,Computer science,Static routing,Policy-based routing,Computer network,Wireless Routing Protocol,Geographic routing,Wireless sensor network,Distributed computing
Conference
ISSN
Citations 
PageRank 
1525-3511
1
0.35
References 
Authors
13
5
Name
Order
Citations
PageRank
Jie Zhou1232.92
Eryk Dutkiewicz2891122.78
Ren Ping Liu349862.73
Gengfa Fang412824.24
Y. Liu5578102.76