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.