LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combining finite learning automata with GSAT for the satisfiability problem
Engineering Applications of Artificial Intelligence
Computers & Mathematics with Applications
Computational Optimization and Applications
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 novel neural network method for shortest path tree computation
Applied Soft Computing
A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks
The Journal of Supercomputing
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This paper presents the first Learning Automaton-based solution to the dynamic single source shortest path problem. It involves finding the shortest path in a single-source stochastic graph topology where there are continuous probabilistic updates in the edge-weights. The algorithm is significantly more efficient than the existing solutions, and can be used to find the "statistical" shortest path tree in the "average" graph topology. It converges to this solution irrespective of whether there are new changes in edge-weights taking place or not. In such random settings, the proposed learning automata solution converges to the set of shortest paths. On the other hand, the existing algorithms will fail to exhibit such a behavior, and would recalculate the affected shortest paths after each weight-change. The important contribution of the proposed algorithm is that all the edges in a stochastic graph are not 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 shortest path graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. 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. The algorithm can be applicable in domains ranging from ground transportation to aerospace, from civilian applications to military, from spatial database applications to telecommunications networking.