Long-term automated monitoring of residential or small in- dustrial properties is an important task within the broader scope of human activity recognition. We present a device- free wifi-based localization system for smart indoor spaces, developed in a collaboration between McGill University and Aerˆıal Technologies. The system relies on existing wifi net- work signals and semi-supervised learning, in order to au- tomatically detect entrance into a residential unit, and track the location of a moving subject within the sensing area. The implemented real-time monitoring platform works by detect- ing changes in the characteristics of the wifi signals collected via existing off-the-shelf wifi-enabled devices in the environ- ment. This platform has been deployed in several apartments in the Montreal area, and the results obtained show the poten- tial of this technology to turn any regular home with an ex- isting wifi network into a smart home equipped with intruder alarm and room-level location detector. The machine learn- ing component has been devised so as to minimize the need for user annotation and overcome temporal instabilities in the input signals. We use a semi-supervised learning framework which works in two phases. First, we build a base learner for mapping wifi signals to different physical locations in the en- vironment from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncer- tainty level rises significantly, without the need for further supervision. This paper describes the technical and practical issues arising in the design and implementation of such a sys- tem for real residential units, and illustrates its performance during on-going deployment.