Title
A cooperative coevolutionary algorithm for the design of wireless sensor networks: track name: bio-inspired solutions for wireless sensor networks
Abstract
This work proposes a cooperative coevolutionary algorithm for the design of a wireless sensor network considering complex network metrics. It is proposed an heuristic based on cooperative coevolution to find a network configuration such that its communication structure presents a small value for the average shortest path length and a high cluster coefficient. This configuration considers a cluster based network, where the cluster heads have two communication radii. The mathematical model of the cluster head location problem was developed to determine the nodes which will be configured as cluster heads. This model was adopted within the coevolutionary algorithm. We describe how the problem can be partitioned and how the fitness computation can be divided such that the cooperative coevolution model is feasible. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with two specifics complex network measurements allowing the reduction of energy consumption and the data transmission delay.
Year
DOI
Venue
2011
10.1145/2001858.2002056
GECCO (Companion)
Keywords
Field
DocType
cooperative coevolution model,cluster head location problem,specifics complex network measurement,track name,complex network metrics,network configuration,cluster head,wireless sensor network,bio-inspired solution,cooperative coevolutionary algorithm,cooperative coevolution,high cluster coefficient,genetic algorithm,wireless sensor networks,shortest path,data transmission,complex network,network design,community structure,mathematical model,clustering coefficient,complex networks
Data transmission,Computer science,Cooperative coevolution,Complex network,Artificial intelligence,Distributed computing,Key distribution in wireless sensor networks,Heuristic,Network planning and design,Shortest path problem,Algorithm,Wireless sensor network,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
3