SLES: Scheduling-Based Low Energy Synchronization for Industrial Internet of Things

The growth in the Industrial Internet of Things (IIoT) drives the development of large-scale wireless sensor networks (WSN), such as smart cities. Time synchronization is essential and primary for enabling collaborative operations among sensor nodes. Generally, all sensor nodes in the network synchronize through exchanging messages, increasing energy consumption. This study introduces scheduling-based low-energy synchronization (SLES) for IIoT. SLES aims to reduce the number of transmitted messages, which in turn reduces the overall energy consumption. In addition, SLES establishes a schedule of distinct synchronization messaging times for each reference node to alleviate the collision problem among the sensor nodes. SLES is constructed and evaluated using real WSNs with different configurations. The experimental results showed that SLES achieves significantly less message traffic than the previous algorithms: FADS – fast scheduling and accurate drift compensation for time synchronization, EERS – energy-efficient reference node selection, and R-sync – robust time synchronization. In this study, we used a simulation to evaluate the performance of SLES in large-scale networks. For the 4-way grid network, the experiential results show that SLES reduced the number of transmitted messages by approximately 22% compared to EERS and FADS. Moreover, SLES outperformed the R-sync algorithm by a factor of 2.5.