Region Directed Diffusion in Sensor Network Using Learning Automata:RDDLA

Abstract

One of the main challenges in wireless sensor network is energy problem and life cycle of nodes in networks. Several methods can be used for increasing life cycle of nodes. One of these methods is load balancing in nodes while transmitting data from source to destination. Directed diffusion algorithm is one of declared methods in wireless sensor networks which is data-oriented algorithm. Directed diffusion deals with two fundamental problems. First, in the network the data packets traverse the invalid routes up to the central node in order to make new routes and eliminate the previous ones at a very short period of time. They are dispatched from a platform lacking any central node which itself causes the decrease of data delivery rates. Second, the reconstruction of such routes urges the application of exploration phase which comes along with the distribution of interest packets and exploratory data resulting into a great deal of outputs injected into the network. But with every motion, the application of exploratory phase of central node makes an overflow of outputs. Certainly with intense movement it enjoys high importance. Having more than one sink, network is separated to some regions and the proposed algorithm called Region Directed Diffusion Learning Automata (RDDLA) updates the rout between these sinks and finds interface node with learning automata and sends packet from source to this node and transmits data to sinks with this node. This approach decreased overall network metrics up to 22%.

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