Improving Data Accuracy Using Proactive Correlated Fuzzy System in Wireless Sensor Networks

• Barakkath Nisha U,
• Uma Maheswari N,
• Venkatesh R,
• Yasir Abdullah R,

Vol. 9, No. 9, September 29, 2015
10.3837/tiis.2015.09.013, Download Paper (Free):

• Wireless Sensor Networks
• robust fuzzy c- means clustering
• proactive fuzzy system
• data accuracy
• correlation
• combined correlated fuzzy system.

Abstract

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

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