OQ‐IICA: Optimal QoS‐aware intra‐inter cluster data aggregation technique for IoT‐assisted WSNs using hybrid optimization techniques

Abstract

SummaryThe wireless sensor network (WSN) contains sensor nodes for understanding, communicating, and storing battery capacity data. Data collection can be defined as the process that base stations use to eliminate unwanted transmissions and provide mixed information. This improves energy efficiency and extends the life of low‐energy WSNs enabled by IoT (IoT‐WSNs). In this article, we propose an optimal QoS‐aware intra‐inter cluster data aggregation technique for WSNs using hybrid optimization techniques (OQ‐IICA). First, we introduce a modified bowerbird optimization (MBO) algorithm for balanced clustering which improves energy efficiency. Second, we develop a multi‐objective seagull optimization‐based decision‐making (MSO‐DM) algorithm to estimate the CH of clusters in the network. Next, we introduce a teacher‐inspired cappuccino search algorithm to ensure the quality of data transfer between nodes by learning through internal and cluster routing. Finally, the proposed OQ‐IICA algorithm compares the latest technologies in ICA, Leach, and Leach‐C power consumption, latency, throughput, latency, number of live nodes, routing, and grid length.