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dc.contributor.authorOshin, Thomas Olutoyin
dc.identifier.citationOshin, T. O. 2014. Energy-Efficient Location Determination for Human Mobility Analysis and Monitoring Using Smart Mobile Devices. Queen Mary University of Londonen_US
dc.description.abstractEmbedded smartphone sensors such as GPS and Wi-Fi have the ability to capture location information. These sensors are increasingly being used for location determination to enable Location based services (LBS) such as maps and navigation. Although these sensors improve the reliability and accuracy when identifying a user’s location, they result in high energy consumption and battery depletion. This work presents a novel accelerometer framework based upon a probabilistic algorithm that neutralizes the effect of different smartphone on-body placements and orientations to allow human movements to be more accurately identified. The key benefits of using the smartphone accelerometer for human mobility analysis, with or without location determination based upon GPS, Wi-Fi or GSM is that it is energy-efficient, provides real-time contextual information and has high availability. The core contributions of this thesis are: 1. Using solely the embedded smartphone accelerometer without need for referencing historical data and accelerometer noise filtering, the framework can in real-time with a time constraint of 2 seconds identify the human mobility state. The method achieves an overall average classification accuracy of 92% when evaluated on a dataset gathered from fifteen individuals that classified twelve different urban human mobility states. Results show that GPS location based sensing architectures that implement the algorithm can achieve energy-savings of up to 58% in typical circumstances. 2. The design, implementation, and evaluation of a method to evaluate the energy-efficiency of hybrid location sensing techniques used by smartphones based upon a user-centred metric, battery depletion time. This metric depends upon three main factors: location sensor usage, human mobility state classification, and location accuracy. Results show that the method can improve the energy-savings of hybrid location sensing techniques by up to 71% in typical circumstances.en_US
dc.description.sponsorshipSUNSET (Sustainable Social Network Services for Transport) research project. EU Grant agreement number FP7-270227 as part of the seventh framework programme theme 3 on information and communication technologies. ASSET (Adaptive Security for Smart Internet of Things in eHealth) research project funded by The Research Council of Norway VERDIKT program. Grant agreement number 213131/O70.
dc.publisherQueen Mary University of London
dc.subjectOpen sourceen_US
dc.subjectLabour and capitalen_US
dc.titleEnergy-Efficient Location Determination for Human Mobility Analysis and Monitoring Using Smart Mobile Devicesen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author

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    Theses Awarded by Queen Mary University of London

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