Regions-of-interest discovering and predicting in smartphone environments

Identificador:  imarina:5925870

Handle: https://hdl.handle.net/20.500.11797/imarina5925870

Autors:  Al-Molegi, Abdulrahman; Alsmadi, Izzat; Martinez-Balleste, Antoni

Resum:
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 …