Weighted Steiner Connected Dominating Set and its Application to Multicast Routing in Wireless MANETs

Computers & Electrical Engineering

2011

Self Cite

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.

Section: Learning Automatamentioning

confidence: 99%

LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks

Computers & Electrical Engineering

2011

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“…A group of learning automata can cooperate to cope with many hardto-solve problems. To name just a few, learning automata have a wide variety of applications in combinatorial optimization problems [53,55], computer networks [54,[56][57][58][59]66], queuing theory [61], signal processing [62], information retrieval [63], adaptive control [64], and pattern recognition [65].…”

Section: Learning Automatamentioning

confidence: 99%

A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs

2010

J Supercomput

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During the last decades, a host of efficient algorithms have been developed for solving the minimum spanning tree problem in deterministic graphs, where the weight associated with the graph edges is assumed to be fixed. Though it is clear that the edge weight varies with time in realistic applications and such an assumption is wrong, finding the minimum spanning tree of a stochastic graph has not received the attention it merits. This is due to the fact that the minimum spanning tree problem becomes incredibly hard to solve when the edge weight is assumed to be a random variable. This becomes more difficult if we assume that the probability distribution function of the edge weight is unknown. In this paper, we propose a learning automata-based heuristic algorithm to solve the minimum spanning tree problem in stochastic graphs wherein the probability distribution function of the edge weight is unknown. The proposed algorithm taking advantage of learning automata determines the edges that must be sampled at each stage. As the presented algorithm proceeds, the sampling process is concentrated on the edges that constitute the spanning tree with the minimum expected weight. The proposed learning automata-based sampling method decreases the number of samples that need to be taken from the graph by reducing the rate of unnecessary samples. Experimental results show the superiority of the proposed algorithm over the well-known existing methods both in terms of the number of samples and the running time of algorithm.

“…A group of learning automata can cooperate to cope with many hard-to-solve problems. To name just a few, learning automata have a wide variety of applications in combinatorial optimization problems [34][35][36], computer networks [37][38][39][40][41], queuing theory [42], signal processing [43], information retrieval [44], adaptive control [45], and pattern recognition [46]. The environment can be described by a triple E : {a, b, c}, where a : {a 1 , a 2 , …, a r } represents the finite set of the inputs, b : {b 1 , b 2 , …, b m } denotes the set of the values that can be taken by the reinforcement signal, and c : {c 1 , c 2 , …, c r } denotes the set of the penalty probabilities, where the element c i is associated with the given action a i .…”

Section: Learning Automatamentioning

confidence: 99%

A Learning Automata-Based Cognitive Radio for Clustered Wireless Ad-Hoc Networks

2010

J Netw Syst Manage

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In current wireless networks, the radio systems are regulated by a fixed spectrum assignment strategy. This policy partitions the whole radio spectrum into a fixed number of radio ranges, each exclusively assigned to a specific user. Such a spectrum assignment strategy leads to an undesirable condition under which some systems only use a small portion of the allocated spectrum while the others have very serious spectrum insufficiency. The learning automata-based cognitive radio which is proposed in this paper is a highly potential technology to address the spectrum scarcity challenges in wireless ad hoc networks. This paper proposes a learning automata-based dynamic frame length TDMA scheme for slot assignment in clustered wireless ad-hoc networks with unknown traffic parameters, where the intra-cluster communications are scheduled by a TDMA scheme, and a CDMA scheme is overlaid on the TDMA to handle an interference-free inter-cluster communication. In this method, each cluster-head is responsible for a collision-free slot assignment within the cluster and determines the input traffic parameters of its own cluster members. It then takes these traffic parameters into consideration for an optimal channel access scheduling in the cluster. The medium access control layer in each cluster is based on a time division multiple access (TDMA) scheme, in which each host is assigned a fraction of the TDMA frame proportional to its traffic load. The simulation experiments show the superiority of our proposed slot