Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.787469
Title: A cognitive IoE (Internet of Everything) approach to ambient-intelligent smart space
Author: Singh Jamnal, Gopal
ISNI:       0000 0004 7972 5820
Awarding Body: Edinburgh Napier University
Current Institution: Edinburgh Napier University
Date of Award: 2019
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Abstract:
At present, the United Nations figures claim that the current world population would rise from 7.6 billion to 8.5 billion in 2030 and 9.7 billion in 2050. Therefore by 2050, 65 percent of the world's population would be living in urban mega-cities and each megacity would be accommodating around 10 million inhabitants. Such massive urbanization of growing population would be known as 21st century's 'Urban Age'. On the other side, by 2020 the growing population of elderly people above 65 years old would be increasing by 25 percent in EU countries and by 30 percent in other developing nations including Asia and North America. As a result, the growth of massive population and elderly inhabitants in urban cities would require an assisted living environment for independent and comfortable living experiences. As can be expected, a persuasive demand of assisted living environment would be vital to the humankind. The goal of an assisted living environment is to support the aging population and inhabitants to live independently in their own home and communities with the support of trained services and personal digital assistants. Therefore, the continuous growing demand of assisted living environment targets to improve the inhabitants comfort level and efficiency to do their ADL (Activity Daily Living) routine tasks by enabling the cooperation among various IoT smart objects and sensors which will understand the environmental surroundings and the inhabitant's contextual needs in a proactive manner. In this work, a Cognitive IoE (Internet of Everything) framework with ambient intelligence capability is proposed to observe the inhabitant activities with heterogeneous IoT network objects and sensors in a time series manner to perceive the inhabitant intentions and situations in the environment. The predictive regression model forecasts the inhabitant's activity patterns with regressive machine learning algorithms. The interconnected network objects (sensors and actuators) behave as agents to learn, think and adapt to contextual situations in the dynamic environment with no or minimum human intervention. Therefore, the first research challenge is to recognize the inhabitant's intentional-situation in the environment, and it is achieved by the Ambient Cognition Model(ACM). The ACM not only performs IoT data-fusion but also applies a statistical model for threshold and weight scheme to extract contextual information in a more systematic manner. The second research challenge of automating the predictive regression model to forecast the time series activity patterns of inhabitants is addressed within the Ambient-Expert Model(AEM). The hidden activity state patterns are identified, trained and tested with the supervised machine learning method of Hidden Markov Model, Recurrent-Neural Network, and Naive Bayes classifier. In addition, a recursive training mechanism of DATAWELL is integrated with the architecture to train(re-train) the model over new datasets and perform predictive analysis in a proactive manner. Furthermore, the unified framework CAiSH (Cognitive Ambient Intelligent Smart Home), built upon the integration of ACMand AEM architectures to a provide an intelligent IoT framework for the ambient intelligence smart home environment. The trained model uses maximum likelihood posterior probabilities to forecast the inhabitant's intentional activity states. The CAiSH works as a proactive digital assistant to the inhabitant provide a development platform for autonomous and enhanced assisted living services in the cognitive IoE environment. The research has been carried out on time-series data sets, deploying IoT lab to generate and collect time series data for the training and testing purpose and providing hands-on research experience on IoT prototype deployment. Overall, 5499 datasets of 30 SA (Spot-Activities) and 9 IA (Intention- Activities) data sets have been engaged for the training and evaluation. The result outputs are evaluated with MAE (Mean-Square Error), MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) metrics for the prediction accuracy measures.
Supervisor: Liu, Xiaodong ; Esteves, Augusto Sponsor: Edinburgh Napier University
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.787469  DOI: Not available
Keywords: Time Series Forecasting ; Data Science for IoT systems ; Cognitive Ambient Intelligent Smart Home ; Activity Pattern Recognition ; Cognitive IoTs ; Ambient Cognition Model ; Real-time IoT system ; Ambient Expert Model ; 006 Special Computer Methods ; QA75 Electronic computers. Computer science
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