Link Quality Estimation using Soft Computing Technique

Wireless sensor nodes deployed in RF industrial environment for monitoring machineries and environment for controlling and maintenance purpose is challenging due to exchange of huge critical data via degraded link. The quality of link in industrial environment is highly influenced by metallic infrastructure, multipath distortion, EM noise, wide tolerance range of device and coexisting technology. The retransmission which occurs as a result of packet loss due to degraded link will in turn reduce the network lifetime by increasing the energy spent for communication. Therefore accurate link quality estimation technique suitable for resource constrained sensor node is essential for fine classification of link in order to perform reliable data transmission by preventing the packet loss over degraded link. This work proposes Kalman filter and fuzzy based link quality estimation technique which best utilize off the self low cost hardware metrics RSSI and LQI for accurate estimation of link quality. The original received signal strength (RSS) is extracted from raw nonlinear RSSI using Kalman filter and its average along with average LQI is given as input to fuzzy system to estimate the link quality. Here two different types of RSSI and LQI data sets are given as input to the estimation technique and classified into good/poor quality link by comparing the fuzzy output with predetermined threshold. This WSN appropriate methodology with low computation and cost extends network lifetime and prevents packet loss through accurate link classification.