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Genetics Based Compact Fuzzy System for Visual Sensor Network

Usama Abdur Rahman1,*, C. Jayakumar2, Deepak Dahiya3, C.R. Rene Robin4
1 Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, 600100, India
2 Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Chennai, Tamil Nadu, 602117, India
3 College of Computer and Information Sciences (CCIS), Majmaah University, Majmaah, 11952, Kingdom of Saudi Arabia
4 Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, 600044, India
* Corresponding Author: Usama Abdur Rahman. Email:

Computer Systems Science and Engineering 2023, 45(1), 409-426.

Received 05 January 2022; Accepted 09 March 2022; Issue published 16 August 2022


As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).


Visual sensor network; fuzzy system; genetic based machine learning; mobile sink; efficient energy; life of network

Cite This Article

U. A. Rahman, C. Jayakumar, D. Dahiya and C. R. Robin, "Genetics based compact fuzzy system for visual sensor network," Computer Systems Science and Engineering, vol. 45, no.1, pp. 409–426, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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