Learning automata with changing number of actions

Computers & Electrical Engineering

2011

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: Variable Action Set Learning Automatamentioning

confidence: 99%

Section: Learning Automatamentioning

confidence: 99%

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

Computers & Electrical Engineering

2011

“…A learning automaton [11,12,13] is an adaptive decision-making unit that improves its performance by learning how to choose the optimal action from a finite set of allowed actions through repeated interactions with a random environment. The action is chosen at random based on a probability distribution kept over the action-set and at each instant the given action is served as the input to the random environment.…”

Section: Learning Automata Theorymentioning

confidence: 99%

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

2011

Cluster Comput

In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad-hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata-based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobilitybased clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.

“…A learning automaton [17][18][19] is an adaptive decision-making unit that improves its performance by learning how to choose the optimal action from a finite set of allowed actions through repeated interactions with a random environment. The action is chosen at random based on a probability distribution kept over the action-set and at each instant the given action is served as the input to the random environment.…”

Section: Learning Automata Theorymentioning

confidence: 99%

A link stability-based multicast routing protocol for wireless mobile ad hoc networks

Journal of Network and Computer Applications

2011

Recently, several studies have been conducted to design mobility-based multicast routing protocols for wireless mobile ad hoc networks (MANET). These protocols assume that the mobility parameters of the network are fixed, and so they cannot perform well under real MANET scenarios in which the mobility parameters of the hosts vary over time at random. Finding the optimal solution to the multicast routing problem is incredibly hard, if the mobility parameters are assumed to be random variables. This becomes more difficult when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a weighted multicast routing algorithm for MANET in which the mobility parameters are supposed to be random variables with unknown distribution. In this method, the multicast routing problem is first transformed into an equivalent stochastic Steiner tree problem in which the random weight associated with a communication link is its expected duration time. Then, a learning automata-based algorithm is proposed for solving the proxy Steiner tree problem. The aim of the proposed algorithm is to find the most stable multicast route (with the maximum duration) against the host mobility. Experimental results confirm the superiority of the proposed method over the best existing mobility-based multicast routing protocols in terms of the packet delivery ratio, multicast route lifetime, control message overhead, and end-to-end delay.