Cellular learning automata with multiple learning automata in each cell and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Adaptive Learning Scheme for Medium Access with Channel Reservation in Wireless Networks
Wireless Personal Communications: An International Journal
Policy controlled self-configuration in unattended wireless sensor networks
Journal of Network and Computer Applications
Localized policy-based target tracking using wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks
The Journal of Supercomputing
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This paper presents a new solution to the dynamic all-pairs shortest-path routing problem using a fast-converging pursuit automata learning approach. The particular instance of the problem that we have investigated concerns finding the all-pairs shortest paths in a stochastic graph, where there are continuous probabilistically based updates in edge-weights. We present the details of the algorithm with an illustrative example. The algorithm can be used to find the all-pairs shortest paths for the ‘statistical’ average graph, and the solution converges irrespective of whether there are new changes in edge-weights or not. On the other hand, the existing popular algorithms will fail to exhibit such a behavior and would recalculate the affected all-pairs shortest paths after each edge-weight update. There are two important contributions of the proposed algorithm. The first contribution is that not all the edges in a stochastic graph are probed and, even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the final list involving all pairs of nodes in the graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. The second contribution is designing a data structure, the elements of which represent the probability that a particular edge in the graph lies in the shortest path between a pair of nodes in the graph. All the algorithms were tested in environments where edge-weights change stochastically, and where the graph topologies undergo multiple simultaneous edge-weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. Copyright © 2008 John Wiley & Sons, Ltd. A preliminary version of this paper was presented at FINA-07, the 2007 International Symposium on Frontiers in Networking with Applications, in Niagara Falls, Canada, in May 2007.