A Spatio-Temporal Approach to Individual Mobility Modeling in On-Device Cognitive Computing Platforms.

The increased availability of GPS-enabled devices makes possible to collect location data for mining purposes and to develop mobility-based services (MBS). For most of the MBSs, determining interesting locations and frequent Points of Interest (POIs) is of paramount importance to study the semantic of places visited by an individual and the mobility patterns as a spatio-temporal phenomenon. In this paper, we propose a novel approach that uses mobility-based services for on-device and individual-centered mobility understanding.

A Cognitive-Inspired Event-Based Control for Power-Aware Human Mobility Analysis in IoT Devices.

Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness in mobile devices in IoT. Under this paradigm, we focus on mobility-based services (MBSs), where mobile devices are expected to perform energy-efficient GPS data acquisition while also providing location accuracy.

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications.

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection.