Dynamic sink movement strategy for expedited query processing in Internet of things-based sensor networks

Abstract Wireless sensor networks (WSNs) represent an essential infrastructure that supports the Internet of things (IoT) and enables intelligent data collection from various contexts. In IoT-driven systems, sensor nodes collect real-time data, initiate end-user or application requests, and forward the gathered data to a cloud server. Query processing in WSN aims to obtain accurate sensor data while conserving network resources. However, traditional static sink-based data collection and query processing methods often face challenges related to network lifetime and lengthy delays. To mitigate these drawbacks, this paper proposes a novel dynamic sink-based query processing strategy (DSQPS) for IoT-enabled WSNs. DSQPS first calculates the optimum number of rendezvous points on the network by solving a minimal set covering problem, followed by Aquila Optimizer (AO), which optimizes the number of mobile sinks. In addition, an optimized movement path for mobile sinks is determined, minimizing delays in data collection and query processing. DSQPS demonstrates superior performance over state-of-the-art approaches based on rigorous testing and mathematical analysis. Results indicate that DSQPS outperforms comparative methods regarding query processing delay, average energy consumption, network lifespan, and throughput, up to 38%, 30%, 150, and 60%, respectively.