Distributed data analytics for wireless sensor networks (WSNs) using fuzzy logic-based machine learning

Article type: Research Article

Authors: Sharma, Amit^{a; *} | Naga Raju, M.^b | Hema, P.^c | Chaitanuya, Morsa^d | Jagannatha Reddy, M.V.^e | Vignesh, T.^f | Chandanan, Amit Kumar^g | Verma, Santhosh^h

Affiliations: [a] School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India | [b] Department of CSE, GST, GITAM (Deemed to be) University, Bengaluru, Karnataka, India | [c] Department of Mathematics, R.M.K. College of Engineering and Technology, R.S.M. Nagar, Puduvoyal, Thiruvallur District, Tamilnadu, India | [d] Department of Computer Applications, RVR&JC College of Engineering, Guntur, Andhra Pradesh, India | [e] Department of CSE, Gandhi Institute of Technology and Management (GITAM Deemed to be University), Bengaluru Campus, Doddaballapura, Karnataka, India | [f] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [g] Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India | [h] Department of Mathematics, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India

Correspondence: [*] Corresponding author. Dr. Amit Sharma, Professor, School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India. E-mail: amit.25076@lpu.co.in.

Abstract: Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis.

Keywords: Wireless sensor networks, distributed data analytics, fuzzy logic, machine learning, energy efficiency

DOI: 10.3233/JIFS-234007

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024