Interval type 2 fuzzy unequal clustering and sleep scheduling for IoT-based WSNs
[EN] Wireless Sensor Networks (WSNs) are a primary means of collecting data in Internet of Things (IoT) systems. Clustering is a highly effective strategy to reduce energy consumption in IoT-based WSNs. In multi-hop clustering, individual sensor nodes transmit their data to designated cluster heads...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/221511 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/221511 |
| Access Level: | Acceso aberto |
| Palavra-chave: | WSN IoT Unequal clustering Sleep scheduling Interval type-2 fuzzy sets |
| Resumo: | [EN] Wireless Sensor Networks (WSNs) are a primary means of collecting data in Internet of Things (IoT) systems. Clustering is a highly effective strategy to reduce energy consumption in IoT-based WSNs. In multi-hop clustering, individual sensor nodes transmit their data to designated cluster heads (CHs), which aggregate the data from their member nodes and forward it to the base station (BS) via other CHs. However, a significant challenge in such networks is the hot-spot problem, where CHs located closer to the BS handle increased traffic, leading to faster energy depletion. To address this, the present paper proposes the Interval Type-2 Fuzzy Unequal Clustering and Sleep Scheduling (IT2FUSS) method, which uniquely integrates Interval Type-2 Fuzzy Sets (IT2FS) to model uncertainties in residual energy (RE), node density (ND), and relative distance to the BS (RDBS), dynamic unequal clustering that adjusts cluster sizes in real time to balance CH workloads, and adaptive sleep scheduling to minimize idle energy consumption. Unlike Type-1 fuzzy systems, IT2FUSS leverages the Footprint of Uncertainty (FOU) to more robustly handle sensor data variability, while the co-design of unequal clustering and sleep scheduling helps mitigate hot-spot effects. A fuzzy inference system generates outputs to optimize CH selection, determine cluster sizes, and improve energy efficiency. Simulation results demonstrate that IT2FUSS achieves superior performance in balancing energy consumption and enhancing overall network longevity compared to existing clustering algorithms. |
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