Abstract
Abstract. The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.
Highlights
In the recent past, the appearance of smart cities and internet of things (IoT) systems along with new technologies, and new tools, has led to an impressive growth of amount data and information produced
We aim to focus on the main concepts related to the mobility, data required for mobility prediction and on related works on mobility prediction
We focus on the data required for mobility prediction
Summary
The appearance of smart cities and internet of things (IoT) systems along with new technologies (e.g., mobile networks-MN, sensor networks), and new tools (e.g., smartphone), has led to an impressive growth of amount data and information produced. The first level concerns the data acquisition by a system or by an application from a mobile device, such in the case of the Mobile Crowd Sensing and Computing (MCSC) paradigm or even with the use of a recommendation application on a smartphone In this case, the data are stored in the mobile device or in an external storage place related to the application. The second level is related to a collection of data from a storage device used by a system, such as MCSs, smart cards management systems, etc In this case, the data, in particular mobility data, are stored in specific equipment (e.g., HLR1, VLR2, sensor nodes, etc.) and are collected directly from this equipment.
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