Predicting encounter and colocation events

Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people\u2019s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user\u2019s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard na\uefve Bayesian and some of the state of the art predictors

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

A Study on Modelling Spatial-Temporal Human Mobility Patterns for Improving Personalized Weather Warning

Understanding human mobility patterns is important for severe weather warning since these patterns can help identify where people are in time and in space when flash floods, tornados, high winds and hurricanes are occurring or are predicted to occur. A GIS (Geographic Information Science) data model was proposed to describe the spatial-temporal human activity. Based on this model, a metric was designed to represent the spatial-temporal activity intensity of human mobility, and an index was generated to quantitatively describe the change in human activities. By analyzing high-resolution human mobility data, the paper verified that human daily mobility patterns could be clearly described with the proposed methods. This research was part of a National Science Foundation grant on next generation severe weather warning systems. Data was collected from a specialized mobile app for severe weather warning, called CASA Alerts, which is being used to analyze different aspects of human behavior in response to severe weather warnings. The data set for this research uses GPS location data from more than 300 APP users during a 14 month period (location was reported at 2 minutes interval, or at based on a 100m change in location). A targeted weather warning strategy was proposed as a result of this research, and future research questions were discussed

Secure forecasting of user activities for distributed urban applications

Modelling human mobility is an interesting yet challenging research topic. Such mobility models can give valuable insight into user behavior. Such models can be used to forecast movement of people. Even though an interesting problem, it was not studied as widely due to lack of available mobility data. But modern communication and digital infrastructure has solved this problem. Thus, as a result, over the past decade and a half, this topic has attracted a lot of attraction. The modelling and forecasting of human mobility has widespread applications from transportation to advertisement. Such models can be used to in a collaborative manner to segment people or used in isolation to bring better services to an individual. Previous researches have presented different approaches for modelling human mobility. These range from neural networks to Markov chains. Some researchers have focused on location data while others have worked with accelerometer data. There are also recommendations to add more information to the data to understand the motive of mobility. This thesis approaches the problem of forecasting human mobility in the form of activities. GPS data is analyzed to mine information and find patterns. The forecasting is done in a twostep process. The first step is to analyze the data to identify and label activities, that are done on a routine basis. This is achieved by using an Adaptive Neuro-Fuzzy Inference System. This additional information helps understand the motive of moving from one place to another. In the second and final step the Markov Chain model is built for the movement among visited locations. The forecasting is done with respect to current time and location, keeping in view the motive of movement. The proposed system is implemented in JAVA and deployed as a combination of RESTful web services. Finally, accuracy tests are made on different datasets which show promising results

Secure forecasting of user activities for distributed urban applications

Modelling human mobility is an interesting yet challenging research topic. Such mobility models can give valuable insight into user behavior. Such models can be used to forecast movement of people. Even though an interesting problem, it was not studied as widely due to lack of available mobility data. But modern communication and digital infrastructure has solved this problem. Thus, as a result, over the past decade and a half, this topic has attracted a lot of attraction. The modelling and forecasting of human mobility has widespread applications from transportation to advertisement. Such models can be used to in a collaborative manner to segment people or used in isolation to bring better services to an individual. Previous researches have presented different approaches for modelling human mobility. These range from neural networks to Markov chains. Some researchers have focused on location data while others have worked with accelerometer data. There are also recommendations to add more information to the data to understand the motive of mobility. This thesis approaches the problem of forecasting human mobility in the form of activities. GPS data is analyzed to mine information and find patterns. The forecasting is done in a twostep process. The first step is to analyze the data to identify and label activities, that are done on a routine basis. This is achieved by using an Adaptive Neuro-Fuzzy Inference System. This additional information helps understand the motive of moving from one place to another. In the second and final step the Markov Chain model is built for the movement among visited locations. The forecasting is done with respect to current time and location, keeping in view the motive of movement. The proposed system is implemented in JAVA and deployed as a combination of RESTful web services. Finally, accuracy tests are made on different datasets which show promising results

Where to go from here? Mobility prediction from instantaneous information

We present the work that allowed us to win the Next-Place Prediction task of the Nokia Mobile Data Challenge. Using data collected from the smartphones of 80 users, we explore the characteristics of their mobility traces. We then develop three families of predictors, including tailored models and generic algorithms, to predict, based on instantaneous information only, the next place a user will visit. These predictors are enhanced with aging techniques that allow them to adapt quickly to the users' changes of habit. Finally, we devise various strategies to blend predictors together and take advantage of their diversity, leading to relative improvements of up to 4%. (C) 2013 Elsevier B.V. All rights reserved

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems : Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU-China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.publishedVersionPeer reviewe