Fuzzy Logic Optimization Method for Energy Efficiency Improvement of CFFS using GA in WSN
Jung-sub Ahn1, Tae-ho Cho2
1Jung-sub Ahn, Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
2Tae-ho Cho*, Department of Computer Science and Engineering, Sungkyunkwan, University, Suwon, Republic of Korea.
Manuscript received on August 07, 2020. | Revised Manuscript received on August 15, 2020. | Manuscript published on August 30, 2020. | PP: 474-480 | Volume-9 Issue-6, August 2020. | Retrieval Number: F1524089620/2020©BEIESP | DOI: 10.35940/ijeat.F1524.089620
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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: Recently, the range of applications for wireless sensor networks has grown. In industrial applications using data-driven approaches, data reliability is particularly important. However, deployed sensor nodes can be easily damaged due to physical damage or node acquisition factors caused by attackers, and false report injection attacks may occur. CFFS with collaborative verification has been proposed to filter out false reports. The proposed CFFS reduces the probability of a successful attack by separating sensor nodes into clusters. The false report filtering performance in the existing scheme is determined according to the pre-security strength setting. Unfortunately, with CFFS, it is impossible to secure each cluster because multiple attacks in a region are not considered. DCFFS uses fuzzy logic to enable security management for each cluster in consideration of the network environment and the geographical arrangement of the nodes. It is necessary for a network administrator to adjust the scope of the membership function parameter to fit the network environment to ensure that the output has an appropriate security strength value for the environment; however, this is difficult to know because it has dissimilar optimum ranges for each application. This paper introduces a fuzzy optimization method that can be adapted to various environments using a genetic algorithm in CFFS. The energy efficiency of nodes is increased by correcting the scope of the membership function in the proposed method. We used experiments to verify that the energy efficiency of the proposed scheme is increased, as compared to the existing scheme.
Keywords: Genetic Algorithm, Fuzzy Optimization, Network Lifetime Extension, Fuzzy Logic, WSN Security Protocol.