Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

/pdf/friendship-and-mobility-user-movement-in-location-based-2u0rrxuc9l.pdf

Friendship and mobility: user movement in location-based social networks

Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start off by infiltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale infiltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion. We operated such an SbN on Facebook---a 750 million user OSN---for about 8 weeks. We collected data related to users' behavior in response to a large-scale infiltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be infiltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful infiltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in practice, OSN security defenses, such as the Facebook Immune System, are not effective enough in detecting or stopping a large-scale infiltration as it occurs.

/pdf/the-socialbot-network-when-bots-socialize-for-fame-and-money-4tmcjtcf4v.pdf

The socialbot network: when bots socialize for fame and money

System(s) and method(s) for compensation of propagation delay offsets of wireless signals. Compensation is accomplished through determination of an effective wireless signal propagation delay that accounts for signal path delay and propagation delay over the air. Such determination is based at least in part on statistical analysis of accurate location estimates of reference positions throughout a coverage sector or cell, and location estimates of the reference positions generated through time-of-flight (TOF) measurements of wireless signals. Determination of propagation or signal path delay offset also is attained iteratively based at least in part on reference location estimates and TOF location estimates. High-accuracy location estimates such as those obtained through global navigation satellite systems are employed as reference location estimates. Position of probes or wireless beacons, deployed throughout a sector or cell, also are employed as reference locations. Compensation of propagation delay offset improves accuracy of conventional TOF location estimates and radio network performance.

Compensation of propagation delays of wireless signals

Online Social Networks (OSNs) are increasingly becoming one of the key media of communication over the Internet. The potential of these services as the basis to gather statistics and exploit information about user behavior is appealing and, as a consequence, the number of applications developed for these purposes has been soaring. At the same time, users are now willing to share information about their location, allowing for the study of the role of geographic distance in social ties.

In this paper we present a graph analysis based approach to study social networks with geographic information and new metrics to characterize how geographic distance affects social structure. We apply our analysis to four large-scale OSN datasets: our results show that there is a vast portion of users with short-distance links and that clusters of friends are often geographically close. In addition, we demonstrate that different social networking services exhibit different geo-social properties: OSNs based mainly on location-advertising largely foster local ties and clusters, while services used mainly for news and content sharing present more connections and clusters on longer distances. The results of this work can be exploited to improve many classes of systems and a potential vast number of applications, as we illustrate by means of some practical examples.

/pdf/distance-matters-geo-social-metrics-for-online-social-2a7iyky9jt.pdf

Distance matters: geo-social metrics for online social networks

With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks.In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods.

/pdf/containment-of-misinformation-spread-in-online-social-11to66e2oc.pdf

Containment of misinformation spread in online social networks

Location-based Social Networks (LSNs) allow users to see where their friends are, to search location-tagged contentwithin their social graph, and to meet others nearby. The recent availability of open mobile platforms, such as Apple iPhones and Google Android phones, makes LSNs much more accessible to mobile users.To study how users share their location in real world, wecollected traces from a commercial LSN service operated by astartup company. In this paper, we present results of data analysis over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations. To the best of our knowledge, this study is the first large-scale quantitative analysis of a real-world commercial LSN service.

Analysis of a Location-Based Social Network

Online social networks (OSNs) have become important media for information sharing among Internet users. In particular, several OSNs provide mechanisms to facilitate sharing of the users' location, which is gaining increased popularity due to the growth of GPS-equipped smartphones. These location-based OSNs (LSNs) bridge users' physical and social worlds, allowing users to know where their friends are and enabling location-based information access and user interactions. In this article we first introduce several LSNs and compare their location-sharing related features. A user's location, however, is sensitive and personal information that may raise significant privacy concerns. To understand real-world users' location-sharing behavior, we collected 21 months of data traces from a commercial LSN and analyzed its users' location-sharing updates. We found that the characteristics of the users' privacy protection behavior is correlated with their age, gender, mobility, and geographic region. In addition, friends tend to have similar privacy protection patterns. To the best of our knowledge, this article is the first large-scale empirical study of a modern LSN.

Sharing location in online social networks

Online social networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these online social networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in online social networks. Our study is based on a dataset collected from a real-world location-based online social network, which includes not only the social graph formed by its users but also the users' activity events. We analyze the social structure and user activity patterns of this network, and confirm that it is a typical online social network, suggesting that conclusions drawn from this specific network can be translated to other online social networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, social structure, and activity patterns on malware propagation in online social networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in online social networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of online social network malware and also shed light on how to defend against them effectively.

Malware propagation in online social networks: nature, dynamics, and defense implications

Camera-based surveillance system is an important tool for assistive environment to monitor those who may have physical or cognitive impairment. It is, however, expensive to deploy a wired surveillance system and difficult to continuously monitor a moving subject in a large facility where many cameras are deployed. In this paper, we first evaluate the performance of streaming camera images over wireless networks in both residential and office environments and present the quantitative results to show the feasibility of using wireless backbones for camera surveillance systems. We then propose sensor-integrated camera surveillance (SICS) to address the continuous monitoring problem. SICS uses wearable wireless sensors to locate moving subjects and automatically selects the camera covering the subject, allowing human operators to focus on only one screen to monitor an individual. SICS uses a self-organizing wireless mesh network to allow flexible deployment at reduced cost. An on-board image-processing algorithm is used to reduce the bandwidth consumption. Through empirical evaluation, we found that the automatic camera hand-off enabled by SICS was effective for continuous camera monitoring and a sophisticated wireless network management system is required to deploy the SICS in practice.

Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments

Transporting camera surveillance data using wireless networks has benefits of reduced cost and increased flexibility. In this paper, we report a measurement study of wireless camera networks using IEEE 802.11 standards, which are pervasively adopted and supported by a large number of vendors. We studied two testbeds deployed in real-world environments, one in a residential house and the other in an office building. We configured these testbeds with both one-hop star and multi-hop mesh topologies and we also compared the application-layer performance over these camera networks using different physical-layer protocols and MAC-layer configurations. We found that the 10-node single-hop camera network in the residential house provided reasonable application quality, if hidden-terminal protection mechanism is used. On the other hand, the 15-node multi-hop camera network in the office building suffered throughput unfairness across cameras, leading to unacceptable application performance for the cameras that are more than one hops away from the server. One possible solution is to use multiple radios on individual cameras, as our experiments showed great performance improvement. We observed that the suitable choices of several network parameters are often dependent on the radio environment, and network-layer throughput is not sufficient to predict the performance of camera applications. These results suggest cross-layer joint network optimizations must be tailored to meet the specific requirements of the applications using distributed cameras.

Nan Li

Papers

Analysis of a Location-Based Social Network

Sharing location in online social networks

Malware propagation in online social networks: nature, dynamics, and defense implications

Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments

Measurement study on wireless camera networks