Energy Aware Fuzzy Logic Secure Data Aggregation (EA-FSDA) technique for Wireless Sensor Networks
Swathi.Y1, Sanjay Chitnis2
1Swathi.Y, Dept. of ISE1, CMR Institute of Technology, Bengaluru. India
2Sanjay Chitnis, Dept. of CSE2, Dayananda Sagar University, India.
Manuscript received on July 30, 2019. | Revised Manuscript received on August 25, 2019. | Manuscript published on August 30, 2019. | PP: 4214-4223 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8882088619/2019©BEIESP | DOI: 10.35940/ijeat.F8882.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Increased demand for wireless communication system has gained huge attraction from various research communities, industries and academic field due to their significant advantages of facilitating efficient communication for real-time applications. With respect to wireless communication, monitoring (i.e. environmental) using WSN is considered as a significant task that has numerous challenging issues such as network deployment, data aggregation and data transmission in hazardous environmental conditions. In this work, we have focused on the data aggregation process in WSN and developed a novel approach to improve the network lifetime using a proposed solution based on the fuzzy logic scheme, which is called as Energy Aware Fuzzy Logic Secure Data Aggregation (EA-FSDA). Furthermore, we present a privacy-aware mechanism for secure data aggregation to improve the system reliability. The privacy-preserving scheme is developed using homomorphic data encryption scheme. Hence, the proposed approach provides a complete solution for efficient and protected data aggregation for WSN that aids in improving the performance of the network. Finally, we present a comparative study which proves that the proposed EA-FSDA technique attains improved network performance in contrast with existing techniques.
Keywords: Network security, wireless sensor network, fuzzy logic, clustering, encryption