Evaluating Next Cell Predictors with Extensive Wi-Fi Mobility Data

Location is an important feature for many applications, and wireless networks can better serve their clients by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with empirical evidence. This paper reports on the results of the first extensive empirical evaluation of location predictors, using a two-year trace of the mobility patterns of over 6,000 users on Dartmouth\u27s campus-wide Wi-Fi wireless network. The surprising results provide critical evidence for anyone designing or using mobility predictors. \par We implemented and compared the prediction accuracy of several location predictors drawn from four major families of domain-independent predictors, namely Markov-based, compression-based, PPM, and SPM predictors. We found that low-order Markov predictors performed as well or better than the more complex and more space-consuming compression-based predictors

Cluster-Aided Mobility Predictions

Predicting the future location of users in wireless net- works has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user. These predictors rely on clustering techniques and extract from the training data similarities among the mobility patterns of the various users to improve the prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility Predictor), a cluster-aided predictor whose design is based on recent non-parametric bayesian statistical tools. CAMP is robust and adaptive in the sense that it exploits similarities in users' mobility only if such similarities are really present in the training data. We analytically prove the consistency of the predictions provided by CAMP, and investigate its performance using two large-scale datasets. CAMP significantly outperforms existing predictors, and in particular those that only exploit individual past trajectories

The heterogeneity of inter-contact time distributions: its importance for routing in delay tolerant networks

Prior work on routing in delay tolerant networks (DTNs) has commonly made the assumption that each pair of nodes shares the same inter-contact time distribution as every other pair. The main argument in this paper is that researchers should also be looking at heterogeneous inter-contact time distributions. We demonstrate the presence of such heterogeneity in the often-used Dartmouth Wi-Fi data set. We also show that DTN routing can benefit from knowing these distributions. We first introduce a new stochastic model focusing on the inter-contact time distributions between all pairs of nodes, which we validate on real connectivity patterns. We then analytically derive the mean delivery time for a bundle of information traversing the network for simple single copy routing schemes. The purpose is to examine the theoretic impact of heterogeneous inter-contact time distributions. Finally, we show that we can exploit this user diversity to improve routing performance.Comment: 6 page

Analysis of location prediction performance of LZ algorithms using GSM Cell-based location data

Proceedings of the 5th International Symposium of Ubiquitous Computing and Ambient Intelligence (UCAMI 2011), December 5-8th, 2011, Riviera Maya, MexicoPredictions about users' next locations allow bringing forward their future context, thus having additional time to react. To make such predictions, algorithms capable of learning mobility patterns and estimating the next location are needed. This work is focused on making the predictions on mobile terminals, thus resource consumption being an important constraint. Among the predictors with low resource consumption, the family of LZ algorithms has been chosen to study their performance, analyzing the results drawn from processing location records of 95 users. The main contribution is to divide the algorithms into two phases, thus being possible to use the best combination to obtain better prediction accuracy or lower resource consumption.Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madri

Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing

We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol\u27s performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other

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

Creating Full Individual-level Location Timelines from Sparse Social Media Data

In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges especially for full timeline inference due to their sparse nature. We propose a stochastic framework, Intermediate Location Computing (ILC) which uses prior knowledge about human mobility patterns to predict every missing location from an individual's social media timeline. We compare ILC with a state-of-the-art RNN baseline as well as methods that are optimized for next-location prediction only. For three major cities, ILC predicts the top 1 location for all missing locations in a timeline, at 1 and 2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all compared methods). Specifically, ILC also outperforms the RNN in settings of low data; both cases of very small number of users (under 50), as well as settings with more users, but with sparser timelines. In general, the RNN model needs a higher number of users to achieve the same performance as ILC. Overall, this work illustrates the tradeoff between prior knowledge of heuristics and more data, for an important societal problem of filling in entire timelines using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table