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
Joint path and wavelength selection using Q-learning in optical burst switching networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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In this paper, we discuss issues involved in developing autonomic Optical Burst Switched (OBS) networks. We develop an OBS network system, the first of its kind, which is self-aware, self-protecting, and self-optimizing, which are essential requirements of an autonomic network system. We use learning automata to autonomously learn the network state and make intelligent choices of route and wavelength, for burst transmission. We develop, for the first time, a self-protecting mechanism, to guard against contention losses and to adapt to network component (link/node) failures. For each connection (flow), at any point of time, this system either works without protection or chooses from one of many available protection mechanisms, based on the current network conditions and the performance requirements. Further, we develop a self-restoration mechanism based on deflection routing, wherein learning automata are used to identify an efficient alternate route to the destination, when there is a failure on the primary route. We show through extensive simulation studies that our mechanisms significantly improve burst loss probability over their existing counterparts