Abstract
The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed.
Highlights
Rapid advances in mobile communications technology have made information and services ubiquitously accessible
Signals of opportunity are defined in this paper as signals that are not originally intended for positioning and navigation purposes, and they include radio frequency (RF) signals, e.g., cellular networks, digital television (DTV), frequency modulation broadcasting (FM), wireless local area networks (WLAN) and Bluetooth [24,36,39], as well as naturally occurring signals such as Earth’s magnetic field, ambient light, and polarized sun light [6,7]
Because the HIPE is designed as a universal engine with different smartphone and location-based services (LBSs) platforms, which probably have different types of sensors available to measure motion dynamics information (MDI), it is important for the HIPE to have adequate flexibility to yield enough accuracy using different represent the results of the baseline method maximum likelihood estimation (MLE)
Summary
Rapid advances in mobile communications technology have made information and services ubiquitously accessible. The geo-context is defined as an integral synthesis of the geographical location, human motion states and mobility context. A geo-context computing solution consists of a positioning engine, a human motion state recognition engine, and a computing component of inferring the mobility context knowledge. This paper presents certain experimental results that can be achieved by using hidden Markov model (HMM) methods for simultaneously estimating the pedestrian location and motion state. This paper is organized as follows: It first reviews existing methods of smartphone mobility sensing, including location estimation and motion state recognition. It presents the proposed geo-context computing solution, including the applied method and experimental results of office daily mobility. The paper concludes with a summary and a proposal of further work
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