We collected mobility traces of 84,208 avatars spanning 22 regions over two months in Second Life, a popular networked virtual environment. We analyzed the traces to characterize the dynamics of the avatars mobility and behavior, both temporally and spatially. We discuss the implications of the our findings to the design o f peer-to-peer networked virtual environments, interest management, mobility modeling of avatars, server load balancing and zone partitioning, client-side caching, and prefetching.
Most recently, La and Pietro have independently conducted a similar study on mobility in Second Life @cite_17 . Their study, however, focuses on metrics relevant to mobile communications, such as graph theoretic properties of line-of-sight networks formed by the avatars, travel length and time of avatars, and contact opportunities among avatars. Their goal is to use the mobility traces of avatars to model human mobility for applications related to wireless and delay-tolerant networks. On the other hand, we focus on metrics that are of interest to systems design of NVEs.
@cite_17
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In this work we present a measurement study of user mobility in Second Life. We first discuss different techniques to collect user traces and then focus on results obtained using a crawler that we built. Tempted by the question whether our methodology could provide similar results to those obtained in real-world experiments, we study the statistical distribution of user contacts and show that from a qualitative point of view user mobility in Second Life presents similar traits to those of real humans. We further push our analysis to line of sight networks that emerge from user interaction and show that they are highly clustered. Lastly, we focus on the spatial properties of user movements and observe that users in Second Life revolve around several point of interests traveling in general short distances. Besides our findings, the traces collected in this work can be very useful for trace-driven simulations of communication schemes in delay tolerant networks and their performance evaluation. We examine the fundamental properties that determine the basic performance metrics for opportunistic communications. We first consider the distribution of inter-contact times between mobile devices. Using a diverse set of measured mobility traces, we find as an invariant property that there is a characteristic time, order of half a day, beyond which the distribution decays exponentially. Up to this value, the distribution in many cases follows a power law, as shown in recent work. This powerlaw finding was previously used to support the hypothesis that inter-contact time has a power law tail, and that common mobility models are not adequate. However, we observe that the time scale of interest for opportunistic forwarding may be of the same order as the characteristic time, and thus the exponential tail is important. We further show that already simple models such as random walk and random way point can exhibit the same dichotomy in the distribution of inter-contact time ascin empirical traces. Finally, we perform an extensive analysis of several properties of human mobility patterns across several dimensions, and we present empirical evidence that the return time of a mobile device to its favorite location site may already explain the observed dichotomy. Our findings suggest that existing results on the performance of forwarding schemes basedon power-law tails might be overly pessimistic. We examine the fundamental properties that determine the basic performance metrics for opportunistic communications. We first consider the distribution of intercontact times between mobile devices. Using a diverse set of measured mobility traces, we find as an invariant property that there is a characteristic time, order of half a day, beyond which the distribution decays exponentially. Up to this value, the distribution in many cases follows a power law, as shown in recent work. This power law finding was previously used to support the hypothesis that intercontact time has a power law tail, and that common mobility models are not adequate. However, we observe that the timescale of interest for opportunistic forwarding may be of the same order as the characteristic time, and thus, the exponential tail is important. We further show that already simple models such as random walk and random waypoint can exhibit the same dichotomy in the distribution of intercontact time as in empirical traces. Finally, we perform an extensive analysis of several properties of human mobility patterns across several dimensions, and we present empirical evidence that the return time of a mobile device to its favorite location site may already explain the observed dichotomy. Our findings suggest that existing results on the performance of forwarding schemes based on power law tails might be overly pessimistic. Short range vehicle to vehicle and device to device communications are of growing interest due to their utility for vehicular safety and infotainment applications as well as for improving the capacity of cellular networks. These mobile systems are characterized by ephemeral, stochastic links. We consider a fundamental problem in this domain -- how to maximize the amount of useful content downloaded by a client from a server over an encounter that lasts a random amount of time. We assume that the distribution of link duration is known or estimated based on historical as well as real-time measurements. We present MERLIN (Maximum Expected download over Random LINks), a single-phase file request protocol that is provably optimal. We evaluate MERLIN comprehensively via simulations based on both ideal link duration distributions as well as empirical distributions obtained from real vehicular mobility traces (from Taxis in Shanghai and Buses in Chicago). We also present two Contiki OS-based implementations of MERLIN (with local and remote calculations) evaluated on the Tmote Sky wireless embedded platform. 3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device service discovery and content transfer. Today, numerous models and metrics are available to capture and characterize static properties of online social networks. When it comes to understanding their dynamics and evolution, however, research offers little interms of metrics or models. Current metrics are limited to logical time clocks, and unable to capture interactions with external factors that rely on physical time clocks. In this paper, our goal is to take initial steps towards building a set of metrics for characterizing social network dynamics based on physical time. We focus our attention on two metrics that capture the "eagerness" of users in building social structure. More specifically, we propose metrics of link delay and triadic closure delay, two metrics that capture the time delay between when a link or triadic closure is possible,and when they actually instantiate in the trace. Considered over time or across traces, the value of these metrics can provide insight on the speed at which users act in building and extending their social neighborhoods. We apply these metrics to two real traces of social network dynamics from the Renren and Facebook networks. We show that these metrics are generally consistent across networks, but their differences reveal interesting properties of each system. We argue that they can be attributed to factors such as network maturity, environmental and social contexts, and services offered by network provider, all factors independent of the network topology and captured by our proposed metrics. Finally, we find that triadic closure delays capture the ease of neighbor discovery in social networks, and can be strongly influenced by friend recommendation systems. Portable devices have more data storage and increasing communication capabilities everyday. In addition to classic infrastructure based communication, these devices can exploit human mobility and opportunistic contacts to communicate. We analyze the characteristics of such opportunistic forwarding paths. We establish that opportunistic mobile networks in general are characterized by a small diameter, a destination device is reachable using only a small number of relays under tight delay constraint. This property is first demonstrated analytically on a family of mobile networks which follow a random graph process. We then establish a similar result empirically with four data sets capturing human mobility, using a new methodology to efficiently compute all the paths that impact the diameter of an opportunistic mobile networks. We complete our analysis of network diameter by studying the impact of intensity of contact rate and contact duration. This work is, to our knowledge, the first validation that the so called "small world" phenomenon applies very generally to opportunistic networking between mobile nodes. Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17 . With the emergence of smartphones and location-based services, user mobility prediction has become a critical enabler for a wide range of applications, like location-based advertising, early warning systems, and citywide traffic planning. A number of techniques have been proposed to either conduct spatio-temporal mobility prediction or forecast the next-place. However, both produce diverse prediction performance for different users and display poor performance for some users. This paper focuses on investigating the effect of living habits on the models of spatio-temporal prediction and next-place prediction, and selects one from these two models for an individual to achieve effective mobility prediction at users’ points of interest. Based on the hidden Markov model (HMM), a spatio-temporal predictor and a next-place predictor are proposed. Living habits are analyzed in terms of entropy, upon which users are clustered into distinct groups. With large-scale factual mobile data captured from a big city, we compare the proposed HMM-based predictors with existing state-of-the-art predictors and apply them to different user groups. The results demonstrate the robust performance of the two proposed mobility predictors, which outperform the state of the art for various user groups. There is substantial interest in the effect of human mobility patterns on opportunistic communications. Inspired by recent work revisiting some of the early evidence for a L´evy flight foraging strategy in animals, we analyse datasets on human contact from real world traces. By analysing the distribution of inter-contact times on different time scales and using different graphical forms, we find not only the highly skewed distributions of waiting times highlighted in previous studies but also clear circadian rhythm. The relative visibility of these two components depends strongly on which graphical form is adopted and the range of time scales. We use a simple model to reconstruct the observed behaviour and discuss the implications of this for forwarding efficiency. The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture. In this paper we propose new temporal distance metrics to quantify and compare the speed (delay) of information diffusion processes taking into account the evolution of a network from a local and global view. We show how these metrics are able to capture the temporal characteristics of time-varying graphs, such as delay, duration and time order of contacts (interactions), compared to the metrics used in the past on static graphs. As a proof of concept we apply these techniques to two classes of time-varying networks, namely connectivity of mobile devices and e-mail exchanges. There are many studies that collect and store life log for personal memory. The paper explains how a system can create someone's life log in an inexpensive way to share daily life events with family or friends through socialnetwork or messaging. In the modern world where people are usually busier than ever, family members are geographically distributed due to globalization of companies and humans are inundated with more information than they can process, ambient communications through mobile media or internet based communication can provide rich social connections to friends and family. People can stay connected to their loving ones ubiquitously that they care about by sharing awareness information in a passive way. For users who wish to have a persistent existence in a virtual world - to let their friends know about their current activity or to inform their caretakers - new technology is needed. Research that aims to bridge real life and the virtual worlds (e.g., Second Life, Face book etc.) to simulate virtual living or logging daily events, while challenging and promising, is currently rare. Only very recently the mapping of real-world activities to virtual worlds has been attempted by processing multiple sensors data along with inference logic for realworld activities. Detecting or inferring human activity using such simple sensor data is often inaccurate, insufficient and expensive. Hence, this paper proposes to infer human activity from environmental sound cues and common sense knowledge, which is an inexpensive alternative to other sensors (e.g., accelerometers) based approaches. Because of their ubiquity, we believe that mobile phones or hand-held devices (HHD) are ideal channels to achieve a seamless integration between the physical and virtual worlds. Therefore, the paper presents a prototype to log daily events by a mobile phone based application by inferring activities from environmental sound cues. To the best of our knowledge, this system pioneers the use of environmental sound based activity recognition in mobile computing to reflect one's real-world activity in virtual worlds. Mobile radio communications raise two major problems. First: a very poor radio link quality. Second: the users' mobility, which requires the management of their position, is resource consuming (especially radio bandwidth). This paper focuses on the second issue and proposes an intelligent method for users locating: the alternative strategy (AS). Our proposal is based on the observation that the mobility behavior of a majority of people can be foretold. If taken into consideration by the system, this characteristic can save signaling messages due to mobility management procedures, leading thus to savings in the system resources. Several versions of the AS are described: a basic version for long term events (i.e., received calls and registrations), and versions with increased memory for short and medium term events. The evaluation of the basic versions was performed using analytic and simulation approaches. It shows that storing the mobility related information brings great savings in system resources when the users have medium or high predictable mobility patterns. More generally speaking, this work points out the fact that the future systems will have to integrate users related information in order: firstly: to provide customized services and secondly: to save system resources. On the other hand, current trends in mobile communications show that adaptive and dynamic system capabilities require that more information to be collected and computed. > Mobile devices carried by people form dynamic networks. Understanding the social structures within the human mobility traces captured from the mobile devices help us to design more efficient message dissemination schemes. People who are in multiple communities are good message carriers. Thus, the ability to identify the different communities efficiently from the various communication traces e.g. contact traces from users' mobile devices is important. In this paper, using some human mobility traces from the real world, we first identify nodes that can play key roles using some social network metrics. Then, we investigate the usefulness of utilizing the keyrole nodes information in the design of multicast delivery schemes in human contact-based networks. Our results indicate that using such information can achieve similar delivery performance as the multi-copy epidemic scheme but at a much smaller communication cost. Most highly dynamic infrastructure-less networks have in common that the assumption of connectivity does not necessarily hold at a given instant. Still, communication routes can be available between any pair of nodes over time and space. These networks (variously called delay-tolerant, disruptive-tolerant, challenged) are naturally modeled as time-varying graphs (or evolving graphs), where the existence of an edge is a function of time. In this paper we study deterministic computations under unstructured mobility, that is when the edges of the graph appear infinitely often but without any (known) pattern. In particular, we focus on the problem of broadcasting with termination detection. We explore the problem with respect to three possible metrics: the date of message arrival (foremost), the time spent doing the broadcast (fastest), and the number of hops used by the broadcast (shortest). We prove that the solvability and complexity of this problem vary with the metric considered, as well as with the type of knowledge a priori available to the entities. These results draw a complete computability map for this problem when mobility is unstructured. Mobile ad hoc networks enable communications between clouds of mobile devices without the need for a preexisting infrastructure. One of their most interesting evolutions are opportunistic networks, whose goal is to also enable communication in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronously in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (e.g., smartphones and tablets) carried by human users, which is the typical OppNets scenario. In such a network where the movements of the communicating devices mirror those of their owners, finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this article we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start by reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobility. We discuss the shortcomings of the existing knowledge about human movements and extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that provides the basis for a discussion on open problems and further directions for research on modeling human mobility. One of the most disconcertingly unnatural properties of most virtual environments (VEs) is the ability of the user to pass through objects. I hypothesize that passive haptics, augmenting a high-fidelity visual virtual environment with low-fidelity physical objects, will markedly improve both sense of presence and spatial knowledge training transfer. The low-fidelity physical models can be constructed from cheap, easy-to-assemble materials such as styrofoam, plywood, and particle board. The first study investigated the effects of augmenting a visual-cliff environment with a slight physical ledge on participants' sense of presence. I found when participants experienced passive haptics in the VE, they exhibited significantly more behaviors associated with pit avoidance than when experiencing the non-augmented VE. Changes in heart rate and skin conductivity were significantly higher than when they experienced the VE without passive haptics. The second study investigated passive haptics' effects on performance of a real-world navigation task after training in a virtual environment. Half of the participants trained on maze navigation in a VE augmented with a styrofoam physical model, while half trained in a non-augmented VE but were given visual and audio contact cues. The task was to gain as much information as possible about the layout of the environment. Participants knew before the VE session that their training would be tested by navigating an identical real maze environment while blindfolded. Significant differences in the time to complete the blindfolded navigation task and significant differences in the number of collisions with objects were found between the participants trained in an augmented VE and the participants trained in a non-augmented VE. 11 of 15 participants trained without passive haptics bumped into the next-to-last obstacle encountered in the testing session and turned the wrong direction to navigate around it: only 2 of 15 participants trained with passive haptics made the same navigation error. On the other hand, the assessment of the participants' cognitive maps of the virtual environment did not find significant differences between groups as measured by sketch maps and object dimension estimation. We present a method for using real world mobility traces to identify tractable theoretical models for the study of distributed algorithms in mobile networks. We validate the method by deriving a vehicular ad hoc network model from a large corpus of position data generated by Boston-area taxicabs. Unlike previous work, our model does not assume global connectivity or eventual stability; it instead assumes only that some subset of processes are connected through transient paths (e.g., paths that exist over time). We use this model to study the problem of prioritized gossip, in which processes attempt to disseminate messages of different priority. Specifically, we present CabChat, a distributed prioritized gossip algorithm that leverages an interesting connection to the classic Tower of Hanoi problem to schedule the broadcast of packets of different priorities. Whereas previous studies of gossip leverage strong connectivity or stabilization assumptions to prove the time complexity of global termination, in our model, with its weak assumptions, we instead analyze CabChat with respect to its ability to deliver a high proportion of high priority messages over the transient paths that happen to exist in a given execution. Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts. This work addresses the problem of balancing the trade-off between the energy cost due to communication and the accuracy of the tracking-based trajectories’ detection and representation in Wireless Sensor Networks (WSNs) settings. We consider some of the approaches used by the Moving Objects Databases (MOD) and Computational Geometry (CG) communities, and we demonstrate that with appropriate adaptation, they can yield significant benefits in terms of energy savings and, consequently, lifetime of a given WSN. Towards that, we developed distributed variations of three approaches for spatio-temporal data reduction – two heuristics (Dead-Reckoning and the Douglas-Peuker algorithm), and a variant of a CG-based optimal algorithm for polyline reduction. In addition, we examine different policies for managing the buffer used by the individual tracking nodes for storing the partial trajectory data. Lastly, we investigated the potential benefits of combining the different data-reduction approaches into ”hybrid” ones during tracking of a particular object’s trajectory. Our experiments demonstrate that the proposed methodologies can significantly reduce the network-wide energy expenses due to communication and increase the network lifetime.