MoSen: Sensor Network Optimization in Multiple-Occupancy Smart Homes
Yuting Zhan, Hamed Haddadi · 2021
Smart home solutions increasingly rely on a variety of sensors for behavioral analytics and activity recognition models to provide context-aware applications and personalized care. Optimizing the sensor network is one of the most important approaches to ensure classification accuracy and the system's efficiency. However, the trade-off between the cost and performance is often a challenge in real deployments, particularly for multiple-occupancy smart homes. To aid in accelerating the adoption of practical sensor-based activity recognition technology, we design MoSen, a framework to simulate the interaction dynamics between sensor-based environments and multiple residents. We explore and quantify the trade-offs between the cost of sensor deployments and the expected labeling accuracy in different scenarios with 2-5 residents. By evaluating the factors that affect the performance of any target sensor network, MoSen is able to provide the sensor selection strategy and design metrics for the sensor layout in real deployments. Using our selection strategy in a 5-person scenario as the case study, we demonstrate that MoSen can significantly improve overall system performance without increasing the deployment costs.