EE‐hHHSS: Energy‐efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk‐salp swarm optimization algorithm

Abstract

Wireless sensor network (WSN) includes power-efficient sensor nodes to convey information to the base station (BS). This network comprises a number of sensor nodes that perform sensing, processing, and wireless communication abilities to monitor a specified sensing field. Therefore, it is necessary to prolong WSN lifetime using energy efficient optimization methods, because the sensor nodes are operated by battery. Also, it is difficult to replace the battery of the nodes located in harsh environments. Thus, energy-efficient routing is a crucial phenomenon in WSN. In this article, energy-efficient WSN with mobile sink (MS) strategy is proposed using hybrid Harris hawk and salp swarm (Hybrid HH-SS) optimization algorithm. In order to achieve energy efficiency, finding an optimal route for MS is a critical task. The MS discovers an optimal path to interconnect with the cluster heads (CHs) by adaptive ant colony optimization (AACO) algorithm. Hence, the proposed hybrid algorithm minimizes the energy consumption (EC), packet loss rate (PLR), and end-to-end (E2E) delay and enhances the lifetime of the network. The proposed work is implemented in JAVA platform, and simulation outcomes show that the proposed approach enhances the wireless sensor network performances. Simulation results show enhancement in energy efficiency in terms of network lifetime, packet delivery rate, average throughput, packet loss rate, energy efficiency, and end-to-end delay. K E Y W O R D S cluster heads (CHs), energy consumption, lifetime, mobile sink, WSN 1 | INTRODUCTION Recently, a well-organized design of WSN has turned into the main zone of research. 1,2 The main features of WSN are environment friendly, appropriate deployment, and self-organizing. For this reason, WSNs are widely useful in fields such as smart health care, environment detection, smart home, and industrial manufacturing. As the sensor nodes are powered using energy-limited batteries, it seems complicated to replace the batteries due to its high cost and a large number of sensors. Hence, it is necessary to adopt an energy efficient WSN with a mobile sink. A device used to identify some sort of input from natural or else physical conditions is called a sensor. The outcome of the sensor is an electrical sign that is transmitted to a controller for further process.

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“…Srinivas, M. and Amgoth, T [21] presented the HHH-SS algorithm for selecting the CH whereas the clustering over the network was performed by using the k-medoid clustering method. Next, an adaptive ant colony optimization was developed to discover the routing path from the source to the destination.…”

“…The performance of the HOA-IoT-WSN method is evaluated using the four different metrics such as PDR, PLR and AEED and network overhead. Here, the proposed HOA-IoT-WSN method is compared with the ESR [16], HHH-SS [21] and LWTS [22] to shows its efficiency. In that, the existing methods such as ESR [16], HHH-SS [21] and LWTS [22] are compared based on the parameters available in its own analysis.…”

“…Here, the proposed HOA-IoT-WSN method is compared with the ESR [16], HHH-SS [21] and LWTS [22] to shows its efficiency. In that, the existing methods such as ESR [16], HHH-SS [21] and LWTS [22] are compared based on the parameters available in its own analysis.…”

“…𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 × 100 (21) The PDR comparison for the HOA-IoT-WSN method and ESR [16] is shown in Fig. 2.…”

“…2. Additionally, the Table 2 shows the comparison of PDR for the HOA-IoT-WSN method with ESR [16], HHH-SS [21] and LWTS [22], where the term NA indicates the parameters which are Not Available for the respective existing methods. From the analysis, it is concluded that the HOA-IoT-WSN method achieves high PDR compared to the ESR [16], HHH-SS [21] and LWTS [22].…”