A hierarchical system of learning automata that can learn the globally optimal path
Information Sciences: an International Journal
Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Discrete Mathematics - Topics on domination
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
Topology control and routing in ad hoc networks: a survey
ACM SIGACT News
Routing in Ad Hoc Networks Using a Spine
IC3N '97 Proceedings of the 6th International Conference on Computer Communications and Networks
Impact of interferences on connectivity in ad hoc networks
IEEE/ACM Transactions on Networking (TON)
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
Solving the Minimum Spanning Tree Problem in Stochastic Graphs Using Learning Automata
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Computer Networks: The International Journal of Computer and Telecommunications Networking
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
The capacity of wireless networks
IEEE Transactions on Information Theory
Adaptive downlink scheduling and rate selection: a cross-layer design
IEEE Journal on Selected Areas in Communications
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
LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks
Computers and Electrical Engineering
Wireless Personal Communications: An International Journal
The Journal of Supercomputing
A scatternet formation algorithm for Bluetooth networks with a non-uniform distribution of devices
Journal of Network and Computer Applications
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In Ad Hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering by which the nodes are hierarchically organized on the basis of the proximity relieves this performance degradation. Finding the weakly connected dominating set (WCDS) is a promising approach for clustering the wireless Ad Hoc networks. Finding the minimum WCDS in the unit disk graph is an NP-Hard problem, and a host of approximation algorithms has been proposed. In this article, we first proposed a centralized approximation algorithm called DLA-CC based on distributed learning automata (DLA) for finding a near optimal solution to the minimum WCDS problem. Then, we propose a DLA-based clustering algorithm called DLA-DC for clustering the wireless Ad Hoc networks. The proposed cluster formation algorithm is a distributed implementation of DLA-CC, in which the dominator nodes and their closed neighbors assume the role of the cluster-heads and cluster members, respectively. In this article, we compute the worst case running time and message complexity of the clustering algorithm for finding a near optimal cluster-head set. We argue that by a proper choice of the learning rate of the clustering algorithm, a trade-off between the running time and message complexity of algorithm with the cluster-head set size (clustering optimality) can be made. The simulation results show the superiority of the proposed algorithms over the existing methods.