Parent selection based on link quality estimation in WSN

Abstract

Wireless sensor network (WSN) is a dynamic and infra-structure less communication system. These are used for any specific target monitoring. The meaningful data returned by the sensor nodes are used for significant decision making. So the quality of data plays a significant role in correctness of decision. Resource scarcity of WSN makes it vulnerable and prone to faults. Faults in WSN are unintended errors, which may lead to wrong decision making. Moreover, due to faults, the reliability and performance of the WSN may get affected; hence, fault tolerance is necessary over here. This paper aims to design a built-in learning-based link quality estimation approach for WSN. This will enable each sensor node to select an appropriate parent through a good-quality link for forwarding data to the base station (BS). Parameters for link quality estimation over here mainly include received signal strength (RSS), signal to interference plus noise ratio (SINR), and packet reception rate (PRR). Simulation results along with discussion are also presented here.