A complex network analysis of human mobility

Abstract-Opportunistic networks use human mobility and consequent wireless contacts between mobile devices, to disseminate data in a peer-to-peer manner. To grasp the potential and limitations of such networks, as well as to design appropriate algorithms and protocols, it is key to understand the statistics of contacts. To date, contact analysis has mainly focused on statistics such as inter-contact and contact distributions. While these pair-wise properties are important, we argue that structural properties of contacts need more thorough analysis. For example, communities of tightly connected nodes, have a great impact on the performance of opportunistic networks and the design of algorithms and protocols. In this paper, we propose a methodology to represent a mobility scenario (i.e., measured contacts) as a weighted contact graph, where tie strength represents how long and often a pair of nodes is in contact. This allows us to analyze the structure of a scenario using tools from complex network analysis and graph theory (e.g., community detection, connectivity metrics). We consider four mobility scenarios of different origins and sizes. Across all scenarios, we find that mobility shows typical smallworld characteristics (short path lengths, and high clustering coefficient). Using state-of-the-art community detection, we also find that mobility is strongly modular. However, communities are not homogenous entities. Instead, the distribution of weights and degrees within a community is similar to the global distribution of weights, implying a rather intricate intra-community structure. To the best of our knowledge, this is the most comprehensive study of structural characteristics of wireless contacts, in terms of the number of nodes in our datasets, and the variety of metrics we consider. Finally, we discuss the primary importance of our findings for mobility modeling and especially for the design of opportunistic network solutions

A complex network analysis of human mobility

Abstract-Opportunistic networks use human mobility and consequent wireless contacts between mobile devices, to disseminate data in a peer-to-peer manner. To grasp the potential and limitations of such networks, as well as to design appropriate algorithms and protocols, it is key to understand the statistics of contacts. To date, contact analysis has mainly focused on statistics such as inter-contact and contact distributions. While these pair-wise properties are important, we argue that structural properties of contacts need more thorough analysis. For example, communities of tightly connected nodes, have a great impact on the performance of opportunistic networks and the design of algorithms and protocols. In this paper, we propose a methodology to represent a mobility scenario (i.e., measured contacts) as a weighted contact graph, where tie strength represents how long and often a pair of nodes is in contact. This allows us to analyze the structure of a scenario using tools from complex network analysis and graph theory (e.g., community detection, connectivity metrics). We consider four mobility scenarios of different origins and sizes. Across all scenarios, we find that mobility shows typical smallworld characteristics (short path lengths, and high clustering coefficient). Using state-of-the-art community detection, we also find that mobility is strongly modular. However, communities are not homogenous entities. Instead, the distribution of weights and degrees within a community is similar to the global distribution of weights, implying a rather intricate intra-community structure. To the best of our knowledge, this is the most comprehensive study of structural characteristics of wireless contacts, in terms of the number of nodes in our datasets, and the variety of metrics we consider. Finally, we discuss the primary importance of our findings for mobility modeling and especially for the design of opportunistic network solutions

Collecting big datasets of human activity one checkin at a time

A variety of cutting edge applications for mobile phones exploit the availability of phone sensors to accurately infer the user activity and location to offer more effective services. To validate and evaluate these new applications, appropriate and extensive datasets are needed: in particular, large sets of traces of sensor data (accelerometer, GPS, micro- phone, etc.), labelled with corresponding user activities. So far, such traces have only been collected in short-lived, small-scale setups. The primary reason for this is the difficulty in establishing accurate ground truth information outside the laboratory. Here, we present our vision of a system for large-scale sensor data capturing, leveraging all sensors of todays smart phones, with the aim of generating a large dataset that is augmented with appropriate ground-truth information. The primary challenges that we address consider the energy cost on the mobile device and the incentives for users to keep running the system on their device for longer. We argue for leveraging the concept of the checkin - as successfully introduced in online social networks (e.g. Foursquare) - for collecting activity and context related datasets. With a checkin, a user deliberately provides a small piece of data about their behaviour while enabling the system to adjust sensing and data collection around important activities. In this work we present up2, a mobile app letting users check in to their current activity (e.g., "waiting for the bus", "riding a bicycle", "having dinner"). After a checkin, we use the phone's sensors (GPS, accelerometer, microphone, etc.) to gather data about the user's activity and surrounding. This makes up2 a valuable tool for research in sensor based activity detection

From contacts to graphs: Pitfalls in using complex network analysis for dtn routing

Abstract—Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict which nodes are likely to deliver a content or bring it closer to the destination. One promising way of predicting future contact opportunities is to aggregate contacts seen in the past to a social graph and use metrics from complex network analysis (e.g., centrality and similarity) to assess the utility of a node to carry a piece of content. This aggregation presents an inherent tradeoff between the amount of time-related information lost during this mapping and the predictive capability of complex network analysis in this context. In this paper, we use two recent DTN routing algorithms that rely on such complex network analysis, to show that contact aggregation significantly affects the performance of these protocols. We then propose simple contact mapping algorithms that demonstrate improved performance up to a factor of 4 in delivery ratio, and robustness to various connectivity scenarios for both protocols. I