Big Data Acquisition in Wireless Sensor Networks Using an AI-Based Interval Type-2 Fuzzy Unequal Clustering and Selective Multi-Hop Routing Framework
DOI:
https://doi.org/10.12928/biste.v8i4.16952Keywords:
Wireless Sensor Networks, Interval Type-2 Fuzzy Logic, Unequal Clustering, Selective Multi-Hop Routing, Big DataAbstract
Wireless Sensor Networks (WSNs) constitute a critical data-acquisition layer for large-scale sensing and big-data analytics; however, limited battery capacity, uneven energy dissipation, hotspot formation, and excessive clustering overhead can interrupt continuous data collection and reduce network lifetime. This paper proposes an Interval Type-2 Fuzzy Unequal Clustering with Selective Multi-Hop and Adaptive Re-clustering protocol (IT2F-UC-SMH-AR) for reliable and energy-efficient WSN data acquisition. The framework combines interval Type-2 fuzzy-based cluster-head selection, unequal cluster formation, load-aware node association, selective relay-based forwarding, and energy-dependent re-clustering. By preserving sensing-node availability, balancing forwarding loads, and sustaining data delivery to the base station, the proposed protocol strengthens the upstream data pipeline required for subsequent storage, processing, and big-data analytics. Its performance is evaluated through MATLAB simulations under three deployment scenarios and compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Cluster Head Election using Fuzzy logic (CHEF), and Gupta fuzzy logic-based scheme (Gupta-FL). The results demonstrate that IT2F-UC-SMH-AR delays early node failure, improves cluster-head stability and energy balance, and maintains competitive packet delivery under different network sizes and communication distances. Its advantages become particularly evident in scenarios with increased routing complexity, where unequal clustering and selective multi-hop transmission reduce long-range communication costs. Although LEACH achieves higher throughput in some compact or dense deployments, the proposed protocol provides a more favorable trade-off between network stability, energy preservation, and reliable data acquisition. These findings establish IT2F-UC-SMH-AR as a promising framework for energy-constrained WSN applications supporting continuous, large-scale data collection.
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