Energy Efficient Query Processing Mechanism for IoT-Enabled WSNs

Abstract

Wireless Sensor Networks (WSNs) act as an integral part of any Internet of Things (IoT) based system. In IoT-based applications such as disaster management, industry automation, and healthcare, the end user demands real-time data for decision-making. In these applications, query-driven WSNs play a vital role in real-time decision-making. Existing state-of-the-art query-driven approaches suffer from a huge query processing delay, end-to-end delay, and poor network lifetime. Therefore, this paper presents an energy-efficient query processing mechanism for IoT-enabled WSNs where mobile sinks-based query processing is performed to reduce end-to-end delay and improve overall network performance. The proposed scheme uses a minimal set cover algorithm to identify the optimal number of rendezvous points. Furthermore, it selects the optimal number of mobile sinks using an improved shark smell optimization algorithm. Extensive simulations and mathematical analysis have shown that the proposed scheme outperformed as compared to the existing state-of-the-art algorithms such as LEDC, QDWSN, QWRP, and QDVGDD. The proposed scheme depicts 41.26%, 39.84%, 40.77%, 39.74%, and 40.15% improvement in terms of average energy consumption, query processing delay, end-to-end delay, network lifetime, and data delivery ratio, respectively.