Improving Learning Automata-based Routing in Wireless Sensor Networks
Recent research in the field of Wireless Sensor Networks (WSNs) has demonstrated the advantages of using learning automata theory to steer the routing decisions made by the sensors in the network. These advantages include aspects such as energy saving, energy balancing, increased lifetime, the selection of relatively short paths, as well as combinations of these and other goals. In this paper, the authors propose a very simple yet effective technique, which can be easily combined with a learning automaton to dramatically improve the performance of the routing process obtained with the latter. As a proof-of-concept, they focus on a typical learning automata-based routing process, which aims at finding a good trade off between the energy consumed and the number of hops along the paths chosen.