Signal Processing for Wireless Sensor Networks Research Papers (original) (raw)

Building management systems (BMS) in smart buildings are supposed to support the optimization of energy and resources consumption, while ensuring basic users' comfort. A common and effective optimizing strategy is to detect, with high accuracy, room occupancies, events, and activities that occur within a building, to accordingly control the energy usage. Several approaches have been implemented to achieve this goal, combining many technologies (e.g., sensor networks, machine learning techniques) as well as new data sources (e.g., sensed data, social networks) allowing to better detect occupant activities. In this context, the purpose of this study is twofold: (i) identify existing solutions related to capturing occupant activities and events to better manage energy usage and provide occupants' comfort, and (ii) pin down the lessons to learn from existing approaches and technologies in order to design better solutions in this regard. We do not pretend to give an exhaustive revision, but throughout this review, we aim at showing that several data can significantly enrich the typology and content of information managed to detect occupant activities and highlight new possibilities in terms of activities diagnosis and analysis to generate more opportunities in optimizing the energy consumption and providing comfort in smart building. K EYWORDS Nowadays, advancements in low-cost sensor technology, wireless networks, electronic devices , as well as new powerful data processing methods have fostered the emergence of intelligent Building Management Systems (BMS) [1]. These latter are describing to-day's buildings as complex systems, embedding several subsystems (heating, ventilation, airconditioning systems, lighting systems, etc.) and actors/occupants with different behaviours and needs, aiming to optimize energy and resources usage, while ensuring basic users comfort [2]. To do so, several data sources need to be analyzed, such as: (i) Data related to buildings (physical features, purpose, etc.); (ii) Data related to building equipment (lighting, temperature, heating, etc.); (iii) Data concerned to activities and occupancy (events, number of people, etc.); and (iv) Data from occupants (interests, preferences, etc.). Data related to buildings and equipment have been exploited in most of the existing BMS solutions. However, they have some common limitations related to do not consider individual needs of occupants (which are heterogeneous) and their various activities which cannot be treated as a whole. A better strategy is to detect the activities occurring within a building in order to fine-tune energy and resources usage. This strategy is proven to be very effective if events, activities, and building occupancies can be detected accurately [3]. In order to achieve this goal, several approaches have been implemented by combining various technologies (e.g., sensor networks, machine learning techniques) as well as by enriching the BMS data with new data sources (e.g., Internet of Things, social networks).