SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things-enabled wireless sensor networks

Energy-efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of sensor nodes (SNs). Using a mobile sink (MS) for data collection can lower the energy consumption of SNs to avoid relaying in WSNs. However, a single MS is not a feasible solution for large-scale WSNs, so it was necessary to use multiple MSs to collect data. A synchronous MS scheduling strategy for data collection (SMS2DC) is proposed in this paper, which uses two types of MS, a local MS to collect data from SN and a global MS to collect data from local MS. In this process, we begin by partitioning the network based on chemical reaction optimization. For each partition, a MS is assigned and scheduled using a path construction strategy according to a geometric path construction approach. In addition, a global MS is scheduled based on a local MS trajectory by identifying the most appropriate collision point to collect data. As a result, the algorithm increases data collection accuracy while minimizing network data loss. The asymptotic time complexity of the proposed SMS2DC algorithm needed On2$$ O\left({n}<^>2\right) $$. The comparison results show the superiority of the proposed SMS2DC strategy under multiple scenarios under various deployment conditions. The proposed synchronous MS scheduling strategy for data collection (SMS2DC) uses chemical reaction optimization and computational geometry approaches for local MS to collect data from SN and a global MS to collect data from local MS. image