IJMTES – FAULT DIAGNOSIS IN WSN USING OPTIMIZED NEIGHBORHOOD HIDDEN CONDITIONAL RANDOM FIELD

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : FAULT DIAGNOSIS IN WSN USING OPTIMIZED NEIGHBORHOOD HIDDEN CONDITIONAL RANDOM FIELD 

Author’s Name : Vigneshwari S | Suganya Devi Kunnamed

Volume 04 Issue 01 2017

ISSN no:  2348-3121

Page no: 4-6

Abstract – Wireless Sensor Networks are widely used for system monitoring and control since it is able to collect and transmit information under different environments. Signals are relayed from one senor to another sensor until it reaches the sink node which could either be mobile or a central control unit. Due to low cost and the deployment of sensor nodes, the sensors become faulty sensor nodes. Today faulty sensors diagnosis for reliable WSN is increased. For fault diagnosis, this work proposed an efficient algorithm called Optimized Neighborhood Hidden Conditional Random Fields (ONCHRF). In this work, we proposed fuzzy logic method to cluster the nodes and selects the cluster head by soft or hard clustering. After the formation of cluster group, optimised Neighborhood Hidden Conditional Random Fields technique is implements to detect the faulty nodes. The optimization if done by using firefly algorithm to find the optimal value for θ and ω The experimental results show that our proposed system provides more effective and efficient fault diagnosis of WSN.

Keywords— Sensor nodes,WSN,Fuzzy logic,Hidden Conditional Random Fields

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