Al-Molegi, Abdulrahman; Alsmadi, Izzat; Martinez-Balleste, Antoni; (2018). Regions-of-interest discovering and predicting in smartphone environments. Pervasive And Mobile Computing, 47(), 31-53. DOI: 10.1016/j.pmcj.2018.05.001
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Pervasive And Mobile Computing. 47 31-53
Abstract:
Abstract Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location prediction accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to discover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of classical MC. Our …
Abstract Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location prediction accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to discover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of classical MC. Our …
Applied Mathematics,Computer Networks and Communications,Computer Science (Miscellaneous),Computer Science Applications,Computer Science, Information Systems,Hardware and Architecture,Information Systems,Software,Telecommunications Regions-of-interest discovering People movement Markov chain Location-based services Location prediction Telecommunications Software Information systems Hardware and architecture Computer science, information systems Computer science applications Computer science (miscellaneous) Computer networks and communications Ciência da computação Applied mathematics