Experience: From Sensors to Service: Advancing Ambient Living with an AIoT Community Testbed

Rapid advancements in IoT and AI over the past decade, coupled with their pervasive integration into daily life, have created substantial opportunities for research and development in Artificial Intelligence of Things (AIoT) toward the comprehensive realization of smart city services. These human-centered applications require suitable testbeds for in-the-wild usability studies and performance validation, ensuring reliable sensory data for training AI models. Yet, most existing testbeds remain either application-focused (e.g., occupancy, energy, mobility) or scenario-bound, which limits their extensibility, heterogeneity, and usability for researchers. This paper introduces SensePod, an AIoT-based smart home testbed established in the IIT Jodhpur campus, designed to promote Sensing as a Service for ambient living. Unlike city-scale or lab-restricted platforms, SensePod transforms a one-bedroom residential space into a modular and reconfigurable substrate where environmental, motion, acoustic, and wearable streams can be orchestrated and reused across studies. We document the requirement analysis, system design, and operational lessons learned in deploying SensePod, and demonstrate two representative use cases: privacy-preserving occupancy estimation and multimodal activity recognition via wearable integration. Our results show that multimodal fusion achieves over 90% accuracy for activity inference, while edge-based deployment enables low-latency, privacy-aware operation. By exposing real-world challenges such as calibration drift, heterogeneous sampling rates, and privacy trade-offs, SensePod provides a researcher-oriented platform that bridges the gap between controlled lab testbeds and the unpredictability of lived environments, paving the way for community-driven innovation in ambient intelligence.