Learning automata: an introduction
Learning automata: an introduction
GloMoSim: a library for parallel simulation of large-scale wireless networks
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communication capabilities. A number of routing algorithms based on learning Automata technique have been proposed for communication networks. However, there has been little work on the effects of Cellular Learning Automata on the performance of these algorithms. One approach to prolong lifetime of sensor network is to balance energy consumption of different nodes in the network. In this paper, we first introduce the model of cellular learning automata in which learning automata are used to adjust the state transition probabilities of cellular automata. Then a cellular learning automata based routing algorithm is introduced to reduce and balance energy consumption in the network. Simulation results indicate that the proposed algorithm performs well in term of balanced energy and energy consumption of nodes and consequently, lengthening lifetime in mesh networks.