Occupancy Monitoring using Environmental & Context Sensors and a Hierarchical Analysis Framework

Saving energy in residential and commercial buildings is of great interest due to diminishing resources. Heating ventilation and air conditioning systems, and electric lighting are responsible for a sig-nificant share of energy usage, which makes it desirable to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast to current, of-ten invasive or unreliable methods we present an approach for accu-rate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analy-sis method. We integrate potentially uncertain contextual informa-tion to produce occupancy estimates at different levels of granular-ity and provide confidence measures for effective building manage-ment. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitor-ing in both low- and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and iden-tify effective room misuse and thus a potential for energy saving