Paper Info
Title
An adaptive learning scheme for load balancing with zone partition in multi-sink wireless sensor network
Abstract
In many researches on load balancing in multi-sink WSN, sensors usually choose the nearest sink as destination for sending data. However, in WSN, events often occur in specific area. If all sensors in this area all follow the nearest-sink strategy, sensors around nearest sink called hotspot will exhaust energy early. It means that this sink is isolated from network early and numbers of routing paths are broken. In this paper, we propose an adaptive learning scheme for load balancing scheme in multi-sink WSN. The agent in a centralized mobile anchor with directional antenna is introduced to adaptively partition the network into several zones according to the residual energy of hotspots around sink nodes. In addition, machine learning is applied to the mobile anchor to make it adaptable to any traffic pattern. Through interactions with the environment, the agent can discovery a near-optimal control policy for movement of mobile anchor. The policy can achieve minimization of residual energy's variance among sinks, which prevent the early isolation of sink and prolong the network lifetime.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.02.119
Expert Syst. Appl.
Keywords
Field
DocType
nearest sink,load balancing,near-optimal control policy,centralized mobile anchor,multi-sink wsn,sensor network,mobile anchor,residual energy,early isolation,multi-sink wireless,machine learning,zone partition,network lifetime,adaptive learning
Airfield traffic pattern,Load balancing (computing),Computer science,Computer network,Minification,Directional antenna,Wireless sensor network,Adaptive learning,Hotspot (Wi-Fi),Sink (computing),Distributed computing
Journal
Volume
Issue
ISSN
39
10
0957-4174
Citations 
PageRank 
References 
14
0.65
7
Authors
2
Name
Order
Citations
PageRank
Sheng-Tzong Cheng129344.23
Tun-Yu Chang2745.76