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
Modeling a student-classroom interaction in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Modeling a domain in a tutorial-like system using learning automata
Acta Cybernetica
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)
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
Adaptive step searching for solving stochastic point location problem
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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This paper presents a new efficient solution to the Dynamic Shortest Path Routing Problem, using the principles of Generalized Pursuit Learning. It proposes an efficient algorithm for maintaining shortest path routing trees in networks that undergo stochastic updates in their structure. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link-costs. In vast, rapidly changing telecommunications (wired or wireless) networks, where links go up and down continuously and rapidly, and where there are simultaneous random updates in link costs, the existing algorithms are inefficient. In such cases, shortest paths need to be computed within a very short time (often in the order of microseconds) by scanning and processing the minimal number of nodes and links. The proposed algorithm, referred to as the Generalized Pursuit Shortest Path Algorithm (GPSPA), will be very useful in this regard, because after convergence, it seems to be the best algorithm to-date for this purpose. Indeed, it has the advantage that it can be used to find the shortest path within the ‘statistical’ average network, which converges irrespective of whether there are new changes in link-costs or not. Existing algorithms are not characterized by such a behaviour inasmuch as they would recalculate the affected shortest paths after each link-cost update. The algorithm has been rigorously evaluated experimentally, and it has been found to be a few orders of magnitude superior to the algorithms available in the literature. Copyright © 2004 John Wiley & Sons, Ltd.