A Routing Protocol for Data Transferring in Wireless Sensor Networks Using Predictive Fuzzy Inference System and Neural Node
Sohrab Khanmohammadi and Mohammad Samadi Gharajeh
Wireless sensor networks (WSNs) are a community of large-scale, low-power, low-cost wireless sensor nodes. This paper proposes a new fuzzy-neural based routing protocol, called Routing Protocol using Fuzzy system and Neural node, RPFN. Data packets are transferred from sensor nodes to a desired base station by hop-to-hop delivery. When a sensor node has a new sensed data or a data packet has been received from its neighbors, it selects an appropriate neighbor called candidate node by a fuzzy inference system and a neural node. The proposed Perceptron-based neural node uses four essential parameters including remaining energy, distance to the base station, available buffer, and link quality to choose the best candidate node according to local information. Moreover, parameter “link quality” is determined by the proposed fuzzy system based on distance to neighbor node and response rate. Simulation results demonstrate that RPFN surpasses some existing routing protocols in terms of packet delivery ratio and network lifetime.
Keywords: Wireless sensor networks (WSNs), routing protocol, fuzzy theory, neural network, perceptron algorithm, packet delivery ratio, network lifetime.