TY - UNPB AB - Smart home solutions increasingly rely on a variety of sensors for behavioralanalytics and activity recognition to provide context-aware applications andpersonalized care. Optimizing the sensor network is one of the most importantapproaches to ensure classification accuracy and the system's efficiency.However, the trade-off between the cost and performance is often a challenge inreal deployments, particularly for multiple-occupancy smart homes or carehomes. In this paper, using real indoor activity and mobility traces, floor plans,and synthetic multi-occupancy behavior models, we evaluate severalmulti-occupancy household scenarios with 2-5 residents. We explore and quantifythe trade-offs between the cost of sensor deployments and expected labelingaccuracy in different scenarios. Our evaluation across different scenarios showthat the performance of the desired context-aware task is affected by differentlocalization resolutions, the number of residents, the number of sensors, andvarying sensor deployments. To aid in accelerating the adoption of practicalsensor-based activity recognition technology, we design MoSen, a framework tosimulate the interaction dynamics between sensor-based environments andmultiple residents. By evaluating the factors that affect the performance ofthe desired sensor network, we provide a sensor selection strategy and designmetrics for sensor layout in real environments. Using our selection strategy ina 5-person scenario case study, we demonstrate that MoSen can significantlyimprove overall system performance without increasing the deployment costs. AU - Zhan,Y AU - Haddadi,H PB - arXiv PY - 2021/// TI - MoSen: activity modelling in multiple-occupancy smart homes UR - http://arxiv.org/abs/2101.00235v1 UR - http://hdl.handle.net/10044/1/85774 ER -