Real-world Mobility Traces Research Articles

Social contact graph-based group communication schemes for delay tolerant networks

Delay-tolerant networks (DTNs) are sparse mobile ad hoc networks in which there is typically no complete path between the source and destination. Multicast and anycast are important group communication paradigms for numerous DTN applications. For example, multicast is used to disseminate real-time traffic information reporting and software patch to multiple devices, and anycast is used for resource discovery and information exchange in emergency or crisis situation. While multicast and anycast have been studied extensively in the context of the Internet and Mobile Ad-Hoc Networks (MANETs), efficient multicast and anycast in DTNs are significantly different and challenging problems due to frequent partitions and intermittent connectivity among nodes. In this paper, we propose single-copy routing strategies for multicast and anycast based on the multi-hop delivery probabilities. Multicast employs a dynamic tree branching technique that allows routing paths to be efficiently shared among multicast destinations. Anycast selects relay nodes based on social distances to anycast group members. It balances the trade-off between a short path to the closest, single group member and a longer path to the area where many other group members reside. That is, it optimises both the efficiency and robustness of message delivery. Through extensive simulation studies using a real-world mobility trace, we show that our schemes achieve a high delivery ratio, low delay, and low (or comparable) transmission cost compared to other group communication strategies.

You Can Act Locally With Efficiency: Influential User Identification in Mobile Social Networks

As mobile social networks grow rapidly, influential user identification has attracted much more attention. Previous studies either need large message overhead to achieve global maxima in influence computation or focus on relatively stable network topology. To tackle the dynamic topology, we present an influential user identification scheme that fully exploits the active mobile users, in which the stable-state property could be leveraged under information potential construction scheme. We also propose an efficient routing algorithm for reaching the global maxima without depending on specific routing protocols. The proposed scheme is validated with extensive simulations using both synthetic random-walk and real-world mobility traces. The results demonstrate that it achieves considerable performance on influential user identification and route construction with little overhead. Furthermore, we present a case of mobile data offloading, and the results show that our scheme could reduce the efficient data traffic by up to 79.2%, compared with a baseline without data offloading.

Opportunistic mobile social networks: From mobility and Facebook friendships to structural analysis of user social behavior

In the last few years, several real-world mobility traces for opportunistic networks have been collected in order to explore node mobility and evaluate the performance of opportunistic networking protocols. These datasets, often including online social data of the mobile users involved, are increasingly driving the research towards the analysis of user social behavior. Within these challenged infrastructureless networks where connectivity is highly intermittent and contact opportunities are exploited to allow communication, node mobility is basically driven by human sociality. As such, understanding node sociality is of paramount importance, especially for finding suitable relays in message forwarding. This paper presents a detailed analysis of a set of six different mobility traces for opportunistic network environments including nodes’ Facebook friendships. Using a multi-layer social network approach and defining several similarity classes between layers, we analyze egocentric and sociocentric node behaviors on the two-layer social graph constructed on offline mobility and online social data. Results show that online and offline centralities are not significantly correlated on most datasets. Also online and offline community structures are different. On the contrary, most of the offline strong social ties correspond to online social ties and in some cases, online and offline brokerage roles show high similarity.

Robustness Analysis and Enhancement of MANETs Using Human Mobility Traces

Understanding network behavior that undergoes challenges is essential to constructing a resilient and survivable network. Due to the mobility and wireless channel properties, it is more difficult to model and analyze mobile ad hoc networks under various challenges. In this paper, we provide a model to assess the vulnerability of mobile ad hoc networks in face of malicious attacks. We analyze comprehensive graph-theoretical properties and network performance of the dynamic networks under attacks against the critical nodes using real-world mobility traces. Motivated by minimum spanning tree and small-world networks, we propose a network enhancement strategy by adding long-range links. We compare the performance of different enhancement strategies by evaluating a list of robustness measures. Our study provides insights into the design and construction of resilient and survivable mobile ad hoc networks.

Mobility prediction in mobile ad hoc networks using neural learning machines

Recent advances in wireless and mobile computing have paved the way for an unprecedented demand growth for mobile services and applications. These services and applications communicate and exchange information using wireless local area networks (WLANs) and mobile ad hoc networks (MANETs). However, new design challenges emerge due to the error-proneness, self-organization and mobility nature of these networks. This paper proposes a neural learning-based solution to the problems associated with the mobility of MANET nodes where future changes in the network topology are efficiently predicted. Using synthetic and real-world mobility traces, the proposed predictor does not only outperform existing mobility prediction algorithms but achieves accuracy scores higher by an order of magnitude. The attained accuracy enables the proposed mobility predictor to improve the overall quality of service in MANETs.

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks (MSNs) has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users’ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

Tracking Message Spread in Mobile Delay Tolerant Networks

We consider a delay tolerant network under two message forwarding schemes—a non-replicative direct delivery scheme and a replicative epidemic routing scheme. Our objective is to track the degree of spread of a message in the network. Such estimation can be used for on-line control of message dissemination. With a homogeneous mobility model with pairwise i.i.d. exponential inter-meeting times, we rigorously derive the system dynamic and measurement equations for optimal tracking by a Kalman filter. Moreover, we provide a framework for tracking a large class of processes that can be modeled as density-dependent Markov chains . We also apply the same filter with a heterogeneous mobility, where the aggregate inter-meeting times exhibit a power law with exponential tail as in real-world mobility traces, and show that the performance of the filter is comparable to that with homogeneous mobility. Through customized simulations, we demonstrate the trade-offs and provide several insightful observations on how the number of observers impacts the filter performance.

Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments

Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios.

Cellular Traffic Offloading through Opportunistic Communications Based on Human Mobility

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

Identifying Influential Nodes for Efficient Routing in Opportunistic Networks

—Opportunistic networks exploit human mobility and consequent device-to-device contacts to opportunistically create data paths over time. Identifying influential nodes as relay is a crucial problem for efficient routing in opportunistic networks. The degree centrality method is very simple but of little relevance. Although closeness centrality and betweenness centrality can effectively identify influential nodes, they are incapable to be applied in large-scale networks due to the high computational complexity. In this paper, we focus on designing an effective centrality ranking metric with low computational complexity in opportunistic networks. We propose the semi locally evaluated centrality metric to identify influential nodes for message forwarding in opportunistic networks. We also present a simple message forwarding algorithm, and employ real world mobility traces and synthetic mobility traces to evaluate the benefits of the proposed semi locally evaluated centrality metric. Results demonstrate the efficiency of the proposed metric in opportunistic networks.

Your Friends Have More Friends Than You Do: Identifying Influential Mobile Users Through Random-Walk Sampling

In this paper, we investigate the problem of identifying influential users in mobile social networks. Influential users are individuals with high centrality in their social-contact graphs. Traditional approaches find these users through centralized algorithms. However, the computational complexity of these algorithms is known to be very high, making them unsuitable for large-scale networks. We propose a lightweight and distributed protocol, iWander, to identify influential users through fixed-length random-walk sampling. We prove that random-walk sampling with O(logn) steps, where n is the number of nodes in a graph, comes quite close to sampling vertices approximately according to their degrees. To the best of our knowledge, we are the first to design a distributed protocol on mobile devices that leverages random walks for identifying influential users, although this technique has been used in other areas. The most attractive feature of iWander is its extremely low control-message overhead, which lends itself well to mobile applications. We evaluate the performance of iWander for two applications, targeted immunization of infectious diseases and target-set selection for information dissemination. Through extensive simulation studies using a real-world mobility trace, we demonstrate that targeted immunization using iWander achieves a comparable performance with a degree-based immunization policy that vaccinates users with a large number of contacts first, while generating only less than 1% of this policy's control messages. We also show that target-set selection based on iWander outperforms the random and degree-based selections for information dissemination in several scenarios.

Analysing social behaviour and message dissemination in human based delay tolerant network

Recent advances in mobile communication shows proliferation in networks formed by human carried devices known as the pocket switched network (PSN). Human beings are social animals. They tend to form groups and communities, and have repetitive mobility pattern which can be used to disseminate information in PSNs. In this paper, we give a deeper insight to the nature of community formation and how such information can be used to help opportunistic forwarding in mobile opportunistic networks. Using real world mobility traces, we first derive the adjacency list for each node and form the contact graph. Using tools from social network analysis we then determine various node properties like centrality and clustering coefficient and graph properties like average path length and modularity. Based on the derived graph properties, node encounter process and nature of message dissemination in PSNs, we propose two social based routing, known as the contact based routing and community aware two-hop routing. We compare the proposed routing techniques with generic epidemic and prophet routing and Bubble-Rap, a social based routing. Results show that the proposed algorithms is able to achieve better delivery ratio and lower delay than Bubble Rap, while reducing the high overhead ratio of epidemic and prophet routing.

Mobility Models for Delay Tolerant Network: A Survey

Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and application protocols are highly dependent on the underlying mobility of nodes and its characteristics. Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate DTN protocols across many scenarios. In DTN mobility modelling literature, a number of mobility models have been developed based on synthetic theory and real world mobility traces. Furthermore, many researchers have developed specific application oriented mobility models. All these models do not provide accurate evaluation in the all scenarios. Therefore, model selection is an important issue in DTN protocol simulation. In this study, we have summarized various widely used mobility models and made a comparison of their performances. Finally, we have concluded with future research directions in mobility modelling for DTN simulation.

Investigating the Privacy versus Forwarding Accuracy Tradeoff in OpportunisticInterest-Casting

Many mobile social networking applications are based on a “friend proximity detection” step, according to which two mobile users try to jointly estimate whether they have friends in common, or share similar interests, etc. Performing “friend proximity detection” in a privacy-preserving way is fundamental to achieve widespread acceptance of mobile social networking applications. However, the need of privacy preservation is often at odds with application-level performance of the mobile social networking application, since only obfuscated information about the other user's profile is available for optimizing performance. In this paper, we study for the first time the fundamental tradeoff between privacy preservation and application-level performance in mobile social networks. More specifically, we consider a mobile social networking application for opportunistic networks called interest-casting. In the interest-casting model, a user wants to deliver a piece of information to other users sharing similar interests (“friends”), possibly through multi-hop forwarding. In this paper, we propose a privacy-preserving friend proximity detection scheme based on a protocol for solving the Yao's “Millionaire's Problem”, and we introduce three interest-casting protocols achieving different tradeoffs between privacy and accuracy of the information forwarding process. The privacy versus accuracy tradeoff is analyzed both theoretically, and through simulations based on a real-world mobility trace. The results of our study demonstrate for the first time that privacy preservation is at odds with forwarding accuracy, and that the best tradeoff between these two conflicting goals should be identified based on the application-level requirements.

Preventing spam in opportunistic networks

In case of a network outage or strict censorship, opportunistic networking is an appealing solution to uphold communications. News such as tweets or videos can be disseminated widely in an epidemic and delay-tolerant fashion between mobile phones. Yet, the cooperative nature of such networks can be abused to disseminate unsolicited content at no cost. Aside from harassing other users with spam, this behavior consumes scarce resources such as battery power.Opportunistic networks’ challenging features, such as its highly dynamic node contacts, render traditional decentralized trust and reputation frameworks insufficient. They mainly fail at the ‘Cold Start Problem’ when mobile users find themselves in a new surrounding without established trust or reputation available, a frequent phenomena in opportunistic networks.To overcome these challenges we propose Trust-Based Spreading (TBS) – a scheme where trusted nodes collaborate and filter spam by opportunistically exchanging assessments to promote or block the spreading of content. TBS copes with the ‘Cold Start Problem’ by allowing the trust structure to be initialized randomly and being extremely resilient to false positives in the feedback process. We evaluate TBS by replaying a variety of real-world mobility traces and show that TBS disseminates legitimate content almost as effectively as classical epidemic spreading, while significantly limiting the reach of spam.

Grammatical Inference for Modeling Mobility Patterns in Networks

Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context-free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context-free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: 1) recognizing mobility patterns of monitored agents in different event data sets collected by sensor networks, and 2) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real-world mobility traces.

Reprint of “Prioritized gossip in vehicular networks”

We propose using real world mobility traces to identify tractable theoretical models for the study of distributed algorithms in mobile networks. Specifically, we derive a vehicular ad hoc network model from a large corpus of position data generated by San Francisco-area taxicabs. Unlike previous work, our model does not assume global connectivity or eventual stability. Instead, we assume only that some subset of processes might be 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. 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.

Prioritized gossip in vehicular networks

We propose using real world mobility traces to identify tractable theoretical models for the study of distributed algorithms in mobile networks. Specifically, we derive a vehicular ad hoc network model from a large corpus of position data generated by San Francisco-area taxicabs. Unlike previous work, our model does not assume global connectivity or eventual stability. Instead, we assume only that some subset of processes might be 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. 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.

Mobile Data Offloading through Opportunistic Communications and Social Participation

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.

Enhanced buffer management policy that utilises message properties for delay-tolerant networks

A delay-tolerant network is a network designed so that temporary or intermittent communication problems and limitations have the least possible adverse impact. Two major issues should be considered to achieve data delivery in such challenging networking environments: a routing strategy for the network and a buffer management policy for each node in the network. The routing strategy determines which messages should be forwarded when nodes meet and the buffer management policy determines which message is purged when the buffer overflows in a node. This study proposes an enhanced buffer management policy that utilises message properties. For maximisation of the message deliveries and minimisation of the average delay, two utility functions are proposed on the basis of message properties, particularly the number of replicas, the age and the remaining time-to-live. The experimental results on two types of well-known real-world mobility trace data and synthetic data show that the proposed buffer management policy yields better results over the history-based drop and traditional policies, such as the shortest lifetime first, the most forwarded first in terms of the number of message deliveries and average delay.