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Title: Occupancy monitoring and prediction in ambient intelligent environment
Author: Akhlaghinia, M. J.
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2010
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Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent. The occupancy data is then collected from different areas in the monitored environment by installing a wireless sensory agent in each area. In a multiple-occupant scenario, monitored occupants are tagged to support sensory signals in distinguishing them from nonmonitored occupants or visitors. Upon enabling the wireless sensory agents to measure the radio signal strength of received data from tags associated with occupants, wireless localising sensory agents are formed and used for occupancy data collection in the multiple-occupant scenario. After the data collection, suitable occupancy time-series are generated from the collected raw data by applying analysis and suitable occupancy signal representation methods, which make it possible to apply time-series predictors for the prediction of reshaped occupancy signal. In addition, an occupancy signal generator is proposed and implemented to generate sufficient occupancy signal data for choosing the best amongst the prediction techniques. After converting the occupancy of different areas in an environment to an occupancy timeseries, the occupancy prediction problem is solved by time-series analysis and prediction techniques for the single-occupant scenario. The proposed technique has made it possible to predict the occupancy signal for 530 seconds in a real environment and up to 900 seconds for a virtual environment. The occupancy signal generator created based on the proposed statistical model is proved to be able to generate different occupancy signals for different occupant profiles incorporating different environmental layouts. This can give a good understanding of the occupancy pattern in indoor spaces and the effect of the uncertainty factors in the occupancy time-series. In the multiple-occupant scenario, the tagging technology integrated with data acquisition system has made it possible to distinguish monitored occupants and separate their occupancy signals. Separated signals can then be treated as individual time-series for prediction. All the proposed techniques and models are tested and validated by real occupancy data collected from different environments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available