An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Abstract

The advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT-enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy-efficient IoT-enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy-efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service.The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub-master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state-of-the-art clustering models.

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