Geographic Routing for 3-D Wireless Sensor Networks with Stochastic Learning Automata

Abstract

This paper introduces β-BGR, a novel geographic routing protocol for 3-D wireless sensor networks with β-type learning automata. In our protocol, the data packets are forwarded toward the destination, and nodes which hear the packet compete for becoming the next hop. A new recovery strategy with β-type learning automata is presented for the case of empty forwarding area. The β-type learning automata are performed to coordinate adaptively the forwarding area, which is oriented toward the destination location, and its dimension ensures that all nodes within it can mutually communicate with each other sensor node. Then, the efficiency of the β-BGR is shown through several simulation results under some 3-D environments.

Conditional Optimality of Learning Automata with 2-state Bayesian Estimators

“…However, compared with the β-type LA and the conventional LAs, the β-type one deteriorates from the viewpoint of memory usage and other resources. For example, since computational and energy resources of sensor node are limited in the wireless sensor networks(WSNs), reducing memory usage and performance optimization are very important issues [8], [9].…”

Learning Automata with 2-State Bayesian Estimators

“…However, compared with the β-type LA and the conventional LAs, the β-type one deteriorates from the viewpoint of memory usage and other resources. For example, since computational and energy resources of sensor nodes are limited in the wireless sensor networks(WSNs), reducing memory footprint and performance optimization are very important issues [8], [9]. So, in this study, we propose the β-type LA with minimum resources, 2-state Bayesian estimators.…”