Smart home solutions increasingly rely on a variety of sensors for behavioral
analytics and activity recognition 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 or care
homes.
In this paper, using real indoor activity and mobility traces, floor plans,
and synthetic multi-occupancy behavior models, we evaluate several
multi-occupancy household scenarios with 2-5 residents. We explore and quantify
the trade-offs between the cost of sensor deployments and expected labeling
accuracy in different scenarios. Our evaluation across different scenarios show
that the performance of the desired context-aware task is affected by different
localization resolutions, the number of residents, the number of sensors, and
varying sensor deployments. 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. By evaluating the factors that affect the performance of
the desired sensor network, we provide a sensor selection strategy and design
metrics for sensor layout in real environments. Using our selection strategy in
a 5-person scenario case study, we demonstrate that MoSen can significantly
improve overall system performance without increasing the deployment costs.