Experimental Demonstration of Integrated WiFi Communication and Occupancy Monitoring

Accurate occupancy counting is essential for effective space management, safety protocols, and resource optimisation in various environments. In recent years, the rapid development of the Internet of Things (IoT) has advanced traditional WiFi sensing into a new domain of research known as Integrated Sensing and Communication (ISAC). Such technology enables simultaneous communication and sensing, allowing for efficient data transmission while gathering environmental information. In this paper, we conduct a novel experimental demonstration and robustness validation of ISAC for occupancy counting. Compared to traditional WiFi sensing technology, we first demonstrated that human presence can be detected by sensing applications using communication packets. Specifically, using our Machine Learning (ML) algorithm, we got a sensing accuracy of more than 96 %. However, we found that this accuracy decreases with increased communication rates and higher occupancy levels, as they introduce noise and interference into the WiFi channel. We also experimentally investigated edge communication, which reveals a tradeoff between occupancy monitoring and communication rate regarding the number of Transmission Control Protocol (TCP) retransmission requests for different distances. We observed that improved accuracy results in an increase in TCP retransmission requests and a reduced communication quantity. Both sensing and communication performance lie in an optimal deployment of the transmit and receive with devices. Further tests at various distances revealed novel insight that sensing performance is better at both short and long distances. Specifically, we present that sensing benefits from diverse reflections, whereas communication relies on a balance best achieved at intermediate distances.