Fuzzy Logic Optimization Method for Energy Efficiency Improvement of CFFS using GA in WSN

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.