Region Directed Diffusion in Sensor Network Using Learning Automata: RDDLA

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

Region Directed Diffusion in Sensor Network Using Learning Automata: RDDLA Keywords:

Wireless sensor networks , ad- hoc , directed diffusion , Learning automata , Region Transmitting