Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Sensing Human Activity for Smart Cities’ Mobility Management

Knowledge about human mobility patterns is the key element towards efficient mobility management. Traditionally, these data are collected by paper/phone household surveys or travel diaries and serve as input for transportation planning models. In this chapter, we report on current state-of-the-art techniques for sensing human activity and report on their applicability for smart city mobility management purposes. We particularly focus on the use of location-enabled devices and their potential towards replacing traditional data collection approaches. Furthermore, to illustrate applicability of smartphones as ubiquitous sensing devices we report on the use of Routecoach application that was used for mobility data collection in the city of Leuven, Belgium. We provide insights into lessons learned, ways in which collected data were used by different stakeholders, and identify existing gaps and future research needs in this field

Efficient Event Detection in Public Transport Tracking

Abstract— Personal mobile devices are widespread and carried by their users most of the time over the day. Thanks to the integrated sensors they can report about visited places, movement types and speed of the users. However, efficient stopping event detection on public transport vehicles is still a challenge. These events, associated with the coordinates of real stations, can be useful to update public transit timetables according to real-time traffic. In field tests we evaluated the most commonly available suitable sensors’ precision and efficiency and developed our Stopping Event Detection Algorithm (SEDA), which utilizes only the accelerometer to find potential stopping times and the Wi-Fi sensor to validate or discard them by a novel localization method. Wi-Fi is used only 6.66% of the time of actual traveling on public vehicles. Our algorithm is shown to recognize properly 82.9-89.47% of public traffic stations while consuming daily only 13% the capacity of an average smartphone's battery