Research Article Stochastic Bat Optimization Model for Secured WSN with Energy-Aware Quantized Indexive Clustering J. Paruvathavardhini and B. Sargunam Department of Electronics and Communication Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India Correspondence should be addressed to J. Paruvathavardhini; vardhini.jpv@gmail.com Received 23 January 2023; Revised 11 May 2023; Accepted 13 May 2023; Published 26 May 2023 Academic Editor: Giovanni Diraco Copyright © 2023 J. Paruvathavardhini and B. Sargunam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The wireless sensor networks (WSNs) with dynamic topology communication among the sensor nodes is vulnerable to numerous attacks. As they have limited power, there arises a conflict between the complex security scheme and the consumption of energy which are inversely proportional to each other. Hence, a trade-off should be accomplished between the implemented scheme and energy. A novel secure and energy-aware routing technique quantized indexive energy-aware clustering-based combinatorial stochastic sampled bat optimization (QIEAC-CSSBO) is proposed which consists of clustering, optimal route path identification, and route maintenance. The clustering process and selection of cluster head (CH) with high residual energy is done using the quantized Schutz indexive Linde–Buzo–Gray algorithm (QIEAC). Optimal route identification is done using CSSBO (combinatorial stochastic sampled Prevosti’s bat optimization), and fitness of every bat is measured on combinatorial functions, namely, distance, energy, trust, and link stability among nodes. Stochastic universal sampling selection procedure is applied to select the global best optimal path for secure data transmission. Lastly, route maintenance process is performed to identify alternative route while link failure occurs among nodes. Experimental assessment is performed using various performance metrics, namely, energy consumption, packet delivery ratio, packet drop rate, throughput, and delay. The proposed method QIEAC-CSSBO enhances the performance of packet delivery ratio by 4%, throughput by 26%, and packet drop rate by 27% and reduces energy consumption by 11%, as well as delay by 16% as compared to existing techniques. 1. Introduction Wireless sensor networks (WSN) have imprinted its inevita- ble need in all the fields like environmental monitoring, health monitoring, precision agriculture, industrial monitor- ing, smart home (IoT-based WSN), traffic monitoring, and military applications. WSN is a network area which includes large number of nodes and ability of wireless transmission, but it has inadequate battery power and minimal storage capacity. A sensor node in WSN monitors and collects the data from the various environments. The collected data is sent from one location to another by means of wireless spon- taneous connectivity [1, 2]. These nodes comprise of small components like transceivers, microprocessor, and control- ler to process and communicate the sensed data to the desired destination, and there is a battery to support all these operations. Hence, the battery plays a vital role in the perfor- mance of a sensor network as its lifetime is based on the same as it is very difficult to change the battery in the case of deployed sensors. The performance of a WSN diminishes as the nodes die without energy, so it is remarkably impor- tant to extend the lifetime of the nodes by efficiently using the energy [3–5]. Several works were proposed to improve the lifetime of the network, and most of the proposed works focus on improved routing protocols based on the cluster. The performance of the network requires a trade-off between the energy constraint and the resource limitations of the sensors. However, when there are many nodes in the network, the standard direct routing uses more energy and may significantly shorten the network lifetime [6]. In WSN, the whole network is divided into subnetworks called clusters controlled by a node within that family called cluster Hindawi Journal of Sensors Volume 2023, Article ID 4237198, 16 pages https://doi.org/10.1155/2023/4237198