Wireless sensor network-assisted fuzzy sink based model

Wasswa Shafik

Energy, 2025, vol. 333, issue C

Abstract: The exponential growth of technology has increased device connectivity in the Internet of Things (IoT), enabling sophisticated networks of spatially distributed sensors to monitor and record environmental conditions. These sensors collect high-quality data and transmit it via a wireless sensor network (WSN) for processing and analysis, aiding in detecting injection attacks. However, the high computational demands lead to excessive power consumption and rapid battery depletion. This study proposes a novel fuzzy mobile sink model to optimize WSN energy usage. A real-time fuzzy decision-making framework dynamically determines the optimal mobile sink placement based on network conditions, enhancing data representation, prediction accuracy, and energy efficiency compared to traditional WSN models. The intelligent system autonomously analyzes IoT and WSN conditions, extending network lifespan through adaptive clustering and sink mobility. It mitigates fabricated attacks and sensor energy depletion while introducing a new statistical distribution to model WSN lifespan. The proposed model was evaluated against existing approaches, including the expectation-maximization (EEM) algorithm and the K-Conid probabilistic distributed clustering scheme. Integrating fuzzy logic with clustering reduces rule complexity and improves decision-making transparency. NS-3 simulations demonstrated that the fuzzy mobile sink model significantly outperforms existing methods in extending network life expectancy.

Keywords: Fuzzy mobile sink; Network scalability; Internet of things; Wireless sensor network (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225031548
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031548

DOI: 10.1016/j.energy.2025.137512

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().