Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor ne
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Taylor kernel fuzzy C-means clustering algorithm for trust and energyaware cluster head selection in wireless sensor networks Susan Augustine1 • J. P. Ananth1
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Wireless sensor networks depend on the effective functioning of the nodes in the network, which is concerned regarding the energy that is essential for the extended network life-time. Clustering plays a major role in enabling energy efficiency, which extends the life-time of the network. Thus, the paper introduces a cluster head (CH) selection phenomenon based on the algorithm, Taylor kernel fuzzy C-means (Taylor KFCM), which is the modification of the kernel-based fuzzy c-means (KFCM) algorithm in the Taylor series. The developed algorithm chooses the cluster head using the selection phenomenon, acceptability factor, which is computed using the energy, distance, and trust. In other words, a node acts as a CH when the fitness constraints of minimal distance, maximal trust, and maximal energy are attained. The simulation environment is established using 50, 100, and 200 nodes with 5 and 10 CHs and the effectiveness of the proposed CH selection is revealed through the analysis depending on the metrics, throughput, energy, delay, and the number of alive nodes. The proposed Taylor kernel fuzzy C-means acquired a maximal throughput, energy, and alive nodes of 0.2857, 0.0947, and 31, and minimal delay and routing overhead of 0.1219, 0.0418 respectively. Keywords Wireless sensor networks Cluster head selection Fuzzy C-means clustering Kernel function Taylor series
1 Introduction Wireless sensor networks (WSN) contribute its significant role in applications mainly, Internet of Things (IoT) [1], which engages itself in the collection of a large amount of the physical data in such a way that the collected data is utilized for the applications including the environmental monitoring [2, 3], transportation, intelligent systems [4, 5], and controlling the industrial functions [6, 7]. WSN possesses numerous sensor nodes, inbuilt with the limited energy and above all, these nodes are self-organized in the distributed region. Therefore, the collection of the data from the data is risky in terms of scalability and energy efficiency [8–10]. The nodes in the WSN are nothing but the sensors, which sense the environment, process the sensed result, and communicate the processed data for & Susan Augustine [email protected] 1
Sri Krishna College of Engineering and Technology, Kuniamuthur, Coimbatore, Tamil Nadu 641008, India
multiple applications [1]. The technical advances address WSN needs with smaller sized sensors of low-cost and low-power electronics [11]. However, energy as a major constraint, there is a need to regulate the sensor nodes in the environment that are distributed in the space in order to ensure the prolonged lifetime [12]. While speaking about the energy of the wireless sensors, it is reported that the energy consumption is
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