Open AccessGAFS: GENETIC ALGORITHM-BASED FILTERING SCHEME FOR IMPROVING DETECTION POWER IN SENSOR NETWORKS
Author(s) -
Tae Ho Cho,
Su Man Nam,
Khan Muhammad
Publication year - 2015
Publication title -
international journal of research - granthaalayah
Language(s) - English
Resource type - Journals
eISSN - 2394-3629
pISSN - 2350-0530
DOI - 10.29121/granthaalayah.v3.i12.2015.2894
Subject(s) - probabilistic logic , wireless sensor network , computer science , genetic algorithm , scheme (mathematics) , energy (signal processing) , power (physics) , algorithm , artificial intelligence , computer network , mathematics , machine learning , mathematical analysis , statistics , physics , quantum mechanics
Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic voting-based filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real reports with false votes. Genetic algorithm-based filtering scheme (GAFS) uses a genetic algorithm with a fuzzy rule-based system that considers remaining energy and number of filtered votes in addition to the distance. The analysis results of the current study demonstrate the effectiveness of our scheme against these attacks in comparison with PVFS. The results show increased detection power achieved through effective verification while maintaining energy consumption.