Optimizing Sensor Network Coverage and Regional Connectivity in Industrial IoT Systems

Internet of things (IoT) technologies have been widely used in industrial systems to control the manufacturing environment and monitor production lines. An industrial IoT system can perform data collection and processing and provide services to production decisions. However, the challenge remains for the IoT system to ensure the quality and quantity of data collected from sensor networks. To address the issue, an independent regional connectivity model is presented in the context of sensor networks to guarantee global connectivity with satisfied quality of data service. We also investigate the optimization of sensing coverage and regional connectivity in an industrial IoT system in both deterministic and random deployment. First, a novel optimal network that achieves full sensing coverage and guarantees regional connectivity is presented for deterministic deployment. The optimal pattern is derived, and the advantage of the proposed model is analyzed. Second, based on the assumption that the given sensors are deployed as a Poisson point process, theoretical analysis is presented to determine the minimum number of sensors used for random deployment to achieve certain coverage and connectivity degrees. Numerical results show that our proposed models are efficient for the application of sensor networks in industrial IoT systems.