Occupancy monitoring and prediction in ambient intelligent environment

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

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Developing a user perception model for smart living: A partial least squares structural equation modelling approach

Smart living is highly advocated to improve the quality of life by involving original and innovative solutions. This trend has been jointly driven by policymakers and domain specialists such as urban planners, property developers, and computer engineers. However, little attention has been paid to understanding the perception of the actual users, whose opinions should have been considered in the design and development of smart living systems. To address the gap, this study aims to investigate the user perceptions towards smart living by adopting an exploratory sequential quantitative research method. A user perception model is proposed based on a comprehensive literature review. Using smart student residence as an example scenario, 221 valid data was obtained through open-ended questionnaires, which were then analysed using a partial least squares structural equation modelling approach. This approach analysed the complex relationship among the identified latent dimensions in realising smart living based on the users' perceptions. The finding demonstrated four significant dimensions to consider in realising smart living: system-to-user conditions, system-to-system conformity conditions, safety and service-related conditions, and tracking and monitoring-related conditions. The proposed model explained 78.3% of the variance in realising smart living for the users considered in the study's context. The study makes a unique contribution to the knowledge body by proposing a model to understand smart living from users' perspectives. It reflects the increasing clamour to incorporate user perspectives into the design of smart living systems. The developed model could serve as a decision-support tool to fulfil users' expectations of smart living