Shuffled Shepherd Squirrel Optimization and Fractional LMS Model for In-Network Aggregation in Wireless Sensor Network

Shuffled Shepherd Squirrel Optimization and Fractional LMS Model for In-Network Aggregation in Wireless Sensor Network

Rajesh L., Mohan H. S.
DOI: 10.4018/IJBDCN.309412
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Abstract

The wireless sensor network (WSN) is commonly based on small node collaboration. These nodes are specified by wireless communication, low price, and energy consumption. Moreover, the WSN can be utilized to compute pressure, temperature, as well as monitoring health, military supervision, and so on. A variety of WSN applications need to gather data from sensor nodes based on sink. In this paper, shuffled shepherd squirrel optimization (SSSOA) technique is devised for in-network aggregation in WSN. Here, the path is formulated from source node to destination through routing process, and source node broadcasts a packet concurrently to destination. The WSN is initiated, and the suitable cluster head (CH) is selected from all nodes. Consequently, CH is selected based on the developed shuffled shepherd squirrel optimization (SSSOA) method.
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1. Introduction

Normally, WSN is based on assistance of small nodes. These small nodes are mainly precised with low cost, less energy utilization and wireless communications. The WSN is utilized to measure health monitoring, surveillance (Alfonso Marino, 2019), pressure, and temperature, military and so on (Bharat Bhushan and Gadadhar Sahoo, 2020). However, it has various limitations in energy, power (Gayathri Devi K.S, 2019), security and memory. The energy and trust are most important factors for assessment process because of natural limitations of computing power and source of sensor nodes. The assurance of security in WSN directs to more energy saving and various beneficial problems (Zahedi and Parma, 2019). In addition, WSN collects various information of locality by constant monitoring and it is employed in Body Area Network (BAN), because it is inexpensive and establishment process is simple (Negra, et al., 2016; Mahesh and Vijayachitra, 2019). Besides BAN, WSN is applied for military tracking, fire monitoring etc (Bharat Bhushan, and Gadadhar Sahoo, 2018). WSN acquires short range sensors, which is engaged in environment monitoring (John and Rodrigues, 2019). The sensor nodesare normally included with several parameters, such as storage capacity, energy constraints and insufficient computation. In WSN, every routing protocols change with application through alternate goals (Negra, et al., 2016; Mahesh and Vijayachitra, 2019). Normally, WSN performs with sensor nodes, which works on battery and hence, energy is a main limitation of WSN (Satish Chand, Samayveer Singh and Bijendra Kumar, 2014). The battery operated sensors is more dependable for the lifetime of network (Hammoudeh and Newman, 2013; Mahesh and Vijayachitra, 2019). Moreover, clustering is a process, which controls energy expenditure of sensor nodes through creating groups, termed as clusters. The clusters are generated along with cluster controller (Neenavath Veeraiah and Dr.B.T.Krishna, 2018), CH, while another node is Cluster Members (CMs). The sensor node belongs to particular cluster and it employed them selves for transferring gathered data to CH (Bharat Bhushan, and Gadadhar Sahoo, 2017). Furthermore, CH is communicated to Base Station (BS) by multi hop or single hop communication (Lee and Cheng, 2012; Mahesh and Vijayachitra, 2019).

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