AN EFFECTIVE FUZZY BASED ALGORITHM TO DETECT FAULTY READINGS IN LONG THIN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) consist of hundreds or thousands tiny nodes called sensor that work together and connected to each other to do some special tasks. It is expected that WSNs will be used widely in many applications in the near future. Localisation and detecting node with faulty readings are the two challenging issues in WSNs, as all the sensors communicate to each other to process and share their data. Existing algorithms use correlation of two nodes to detect faulty readings, which resulted in high computational complexity. Also, algorithms that use inverse of distance as scale of similarity have weaknesses in accuracy of calculations. This paper proposed a new fuzzy based algorithm to overcome the localisation problem and detecting faulty readings in WSNs. Using an effective fuzzy inference system can improve the decision-making algorithm, which use for localisation and detecting faulty readings in WSNs. The experimental results show that the proposed algorithm reduces computational complexity of correlation of two nodes, which resulted in reducing energy consumption of each network’s node. Also, the accuracy of the results is improved when compared to the other algorithms.