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
LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks
Computers and Electrical Engineering
Wireless Personal Communications: An International Journal
Finding minimum weight connected dominating set in stochastic graph based on learning automata
Information Sciences: an International Journal
Hi-index | 0.00 |
The minimum connected dominating set (MCDS) of a given graph G is the smallest sub-graph of G such that every vertex in G belongs either to the sub-graph or is adjacent to a vertex of the sub-graph. Finding the MCDS in an arbitrary graph is a NP-Hard problem, and several approximation algorithms have been proposed for solving this problem in deterministic graphs, but to the best of our knowledge no work has been done on finding the MCDS in stochastic graphs. In this paper, the MCDS problem in the stochastic graphs is first introduced, and then a learning automata-based approximation algorithm called SCDS is proposed for solving this problem when the probability distribution function of the vertex weight is unknown. It is shown that by a proper choice of the parameters of the proposed algorithm, the probability with which the proposed algorithm find the MCDS is close enough to unity. The simulation results show the efficiency of the proposed algorithm in terms of the number of samplings.