Paper Info
Title
Multi-Objective Mobile Agent-Based Sensor Network Routing Using Moea/D
Abstract
Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a Multiobjective Optimization Problem (MOP) using conventional Multi-Objective Evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586431
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Keywords
Field
DocType
accuracy,wireless sensor networks,sensor network,routing,multiobjective optimization,negative affect,path loss,evolutionary computation,mobile agent,wireless sensor network,clustering algorithms,network routing
Evolutionary algorithm,Computer science,Mobile agent,Artificial intelligence,Cluster analysis,Distributed computing,Object detection,Mathematical optimization,Evolutionary computation,Multiobjective optimization problem,Energy consumption,Wireless sensor network,Machine learning
Conference
Citations 
PageRank 
References 
10
0.55
11
Authors
4
Name
Order
Citations
PageRank
Andreas Konstantinidis124812.87
Christoforos Charalambous2504.35
Aimin Zhou3160760.66
Qingfu Zhang47634255.05