How Long Are You Staying? Predicting Residence Time from Human Mobility Traces

Welcome to ACM MobiCom 2013, the 19th Annual International Conference on Mobile Computing and Networking. Over the years, MobiCom has established itself as a premier forum for publishing and presenting cutting-edge research in mobile systems and wireless networks, and this year's final program continues this wonderful tradition. The high quality and success of MobiCom can be credited to two groups. First and foremost is the authors of all of the submitted papers who submitted their very best research ideas and results. The 28 accepted papers represent leading-edge, and sometimes bleeding-edge, advances in a large variety of important mobile computing topics -- from traditional yet still very important topics, such as improving the efficiency of cellular and Wi-Fi networks, to topics focusing on the use of new wireless technologies in new mobile environments, such as enabling gesture recognition, managing indoor white space networks, and deploying performance improving femto cells. However, the hidden strength of MobiCom comes from the hard work of a very dedicated program committee, which consisted of 41 members from academia, government and industry spread across 7 different countries with expertise in many areas relevant to wireless networking and mobile computing. As Program Committee (PC) chairs, it was our task to keep MobiCom fresh and grow it to keep up with all the new challenges of the wireless and mobile community. This year, we introduced several new initiatives to further enhance the reach and quality of the conference. To increase MobiCom's visibility in industry, we included five PC members from product teams of Cisco, Google, Qualcomm and Broadcom. To ensure the strength and breadth, but most importantly the vision, of the PC, one-sixth of the PC were new members who had previously not served on the MobiCom PC. This year's MobiCom also has an invited industry session, in which speakers from Broadcom, Alcatel-Lucent, Google, and Microsoft will present the latest results and research challenges from industry in an effort to bridge the gap between academic research and how it is, or maybe isn't, relevant to industry. This year's call for papers attracted 207 submissions from five continents: Asia, Europe, Africa, North America and South America. We used HotCRP for handling the paper submission and reviewing, which was done in three phases. In the first phase, each paper was reviewed by at least three PC members, and the top 98 papers were selected for the next round. In the second phase, each paper was reviewed by at least two more PC members. In some cases when the paper was at the intersection of new topics, such as RADAR or robotics, additional expert opinions were solicited. The final phase was the PC meeting held on May 30th and 31st in Redmond, WA. A total of 34 members attended the PC meeting in-person, while 5 members attended the meeting on Skype. Over one and a half days, the PC extensively discussed the merits and flaws of the 60 toprated papers and ultimately accepted 28 papers for final publication in the conferences' proceedings. Across the three phases, each PC member reviewed about 25 papers, such that most round two papers had an average of 6 reviews (a high number for any top-tier conference). To ensure fairness and preserve the anonymity of all authors and reviewers, papers authored by PC chairs were mixed with a random selection of other papers and handled out-of-band by Alex Snoeren, who was the PC co-chair for MobiCom 2012

Takeaways in Large-scale Human Mobility Data Mining

International audienceEmploying mobile devices to perform data analytics is a typical fog computing application that utilizes the intelligence at the edge of networks. Such an application relies on the knowledge of the mobility of mobile devices and their users, e.g., to deploy computation tasks efficiently at the edge. This paper surveys the literature on the mobility-related utilization of operator-collected CDR (charging data records) – the most significant proxy of large-scale human mobility studies. We provide an innovative introductory guide to the CDR data preliminary. It reveals original issues regarding CDR-based mobility feature computation and applications at the edge. Our survey plays an important role in utilizing mobile devices in terms of both human mobility investigation and fog computing

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain