homesensing

Home sensing refers to the collection, analysis, and interpretation of data from sensors located in residential environments to monitor occupancy, activity, environmental conditions, and device status.

Common sensor types include occupancy sensors such as passive infrared (PIR) and ultrasonic sensors, contact sensors on doors and windows, environmental sensors measuring temperature, humidity, air quality, and noise levels, and energy meters that track electricity use. Visual sensors, such as cameras, are used with privacy-preserving approaches.

Data from these sensors are transmitted over home networks using protocols such as Wi-Fi, Zigbee, Z-Wave, Thread, and Matter, with processing performed on devices (edge) or in the cloud. Data fusion and machine learning enable occupancy detection, activity recognition, and anomaly detection.

Applications include energy management (HVAC and lighting optimization), safety and security (detecting intrusions or falls), elder or disability care, appliance monitoring, and general automation.

Challenges include privacy and security, data ownership and consent, interoperability across devices, and the potential for errors (false positives/negatives). Costs, reliability, and user acceptance also influence adoption. Efforts such as open standards and privacy-preserving sensing aim to mitigate these issues.

The concept emerged with home automation and domotics in the late 20th century and has grown with the Internet of Things. Modern homes increasingly rely on sensor networks and AI to deliver adaptive comfort, efficiency, and safety.