Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Multicluster, mobile, multimedia radio network
Wireless Networks
A Distributed Algorithm for Minimum-Weight Spanning Trees
ACM Transactions on Programming Languages and Systems (TOPLAS)
Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
Localized Protocols for Ad Hoc Clustering and Backbone Formation: A Performance Comparison
IEEE Transactions on Parallel and Distributed Systems
Clustering wireless ad hoc networks with weakly connected dominating set
Journal of Parallel and Distributed Computing
Mitigating the impact of node mobility on ad hoc clustering
Wireless Communications & Mobile Computing - Resources and Mobility Management in Wireless Networks
Approximating the Minimum Connected Dominating Set in Stochastic Graphs Based on Learning Automata
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
A distributed energy-efficient clustering protocol for wireless sensor networks
Computers and Electrical Engineering
Clustering the wireless Ad Hoc networks: A distributed learning automata approach
Journal of Parallel and Distributed Computing
Clustering the wireless Ad Hoc networks: A distributed learning automata approach
Journal of Parallel and Distributed Computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Journal of Network and Computer Applications
A Learning Automata-Based Cognitive Radio for Clustered Wireless Ad-Hoc Networks
Journal of Network and Systems Management
A cellular learning automata-based algorithm for solving the vertex coloring problem
Expert Systems with Applications: An International Journal
Learning automata-based algorithms for solving stochastic minimum spanning tree problem
Applied Soft Computing
A link stability-based multicast routing protocol for wireless mobile ad hoc networks
Journal of Network and Computer Applications
Wireless Personal Communications: An International Journal
The Journal of Supercomputing
The capacity of wireless networks
IEEE Transactions on Information Theory
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
The Design and Simulation of a Mobile Radio Network with Distributed Control
IEEE Journal on Selected Areas in Communications
Computers and Electrical Engineering
An adaptive backbone formation algorithm for wireless sensor networks
Computer Communications
Mobility prediction in mobile wireless networks
Journal of Network and Computer Applications
A distributed resource discovery algorithm for P2P grids
Journal of Network and Computer Applications
An adaptive learning automata-based ranking function discovery algorithm
Journal of Intelligent Information Systems
An adaptive learning to rank algorithm: Learning automata approach
Decision Support Systems
LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-Hoc Networks
Wireless Personal Communications: An International Journal
The evolution game analysis of clustering for asymmetrical multi-factors in WSNs
Computers and Electrical Engineering
The placement-configuration problem for intrusion detection nodes in wireless sensor networks
Computers and Electrical Engineering
Energy efficient and QoS aware routing protocol for Clustered Wireless Sensor Network
Computers and Electrical Engineering
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Performance of ad hoc networks dramatically declines as network grows. Cluster formation in which the network hosts are hierarchically partitioned into several autonomous non-overlapping groups, based on proximity, is a promising approach to alleviate the scalability problem of ad hoc networks. In this paper, we propose a localized learning automata-based clustering algorithm for wireless ad hoc networks. The proposed clustering method is a fully distributed algorithm in which each host chooses its cluster-head based solely on local information received from neighboring hosts. The proposed algorithm can be independently localized at each host. This results in a significantly reduction in message overhead of algorithm, and allows cluster maintenance can be locally performed only where it is required. To show the performance of proposed algorithm, obtained results are compared with those of several existing clustering methods in terms of the number of clusters, control message overhead, clustering time, and load standard deviation.