Application Scheduling With Multiplexed Sensing of Monitoring Points in Multi-Purpose IoT Wireless Sensor Networks

Wireless sensor networks (WSNs) play a crucial role in Internet-of-Things (IoT) systems serving a variety of applications. They gather data from specific sensor nodes and transmit it to remote units for processing. When multiple applications share a WSN infrastructure, efficient scheduling becomes vital. In our research, we address the problem of application scheduling in WSNs. Specifically, we focus on scenarios where applications request data from monitoring points within the coverage area of a WSN. We propose a shared-data approach that reduces the network's sensing and communication load by allowing multiple applications to use the same sensing data. To tackle the scheduling challenge, we introduce a genetic algorithm named GABAS and three greedy algorithms: LMPF, LMSF, and LTSF. These algorithms determine the order in which applications are admitted to the WSN infrastructure, considering various criteria. To assess the performance of our algorithms, we conducted extensive simulation experiments and compared them with standard scheduling methods. We also evaluated the performance of GABAS as compared to another genetic scheduling algorithm that has recently appeared in the literature. The overall experimental results show that the methods we propose outperform the compared approaches across various metrics, namely makespan, turnaround time, waiting time, and successful execution rate. In particular, our genetic algorithm proves to be highly effective in scheduling applications and optimizing the mentioned metrics.