Inter-contact Time Distribution Research Articles

MSN‐CDF: New community detection framework to improve routing in mobile social networks

SummaryIn mobile social networks (MSN), portable devices of mobile users communicate with each other. The contact and intercontact times are the two most important parameters that specify the behavior of MSN. Many social features such as similarity, centrality, closeness, and distance are derived from contact and intercontact time patterns. Those parameters and features can be used in the community detection process in community‐based routing as a vector weight of network graph edges. But there is no solution for proper exploitation of them as a multicriteria optimization of community detection algorithms so that the communities become more adaptive with the MSN quiddity and the computational complexity of the algorithms to be stay constant. In this paper, we propose the MSN‐adaptive community detection framework, MSN‐CDF, which meets these demands. We improve the quality function of community detection methods, for example, the Intensity in clique percolation methods and the Modularity in modularity‐based methods, through our new model‐based joint optimization of their quality functions. To this end, to express the MSN contact and social features, we develop a criterion called MCSF. It holds the contact time and intercontact time distributions parameters and implies their social features for MSN purposes. This criterion has as little traffic as possible and enjoys from the probabilistic joint distribution model. The simulations show that MSN‐CDF reduces the network overhead significantly compared with the DiBUBB, Epidemic, and Dynamic Routing. Moreover, it outperforms DiBUBB and Dynamic Routing in terms of delivery rate and delivery latency.

Fragmented data routing based on exponentially distributed contacts and inter-contact times in DTNs

Existing works in data forwarding in delay tolerant networks often simplify the data transmission during a contact between two network nodes. One common assumption is that once transmitted, messages will always successfully arrive at the encounter node in its entirety regardless of the contact duration. The other common assumption is that messages are unfragmented, and are successfully transmitted only if the contact duration is long enough. If the contact duration is too short, the transmission will not complete, and the message needs to be re-transmitted in its entirety in the next contact. While the first assumption is unrealistic, the second assumption makes the routing strategy inefficient for large data items in network environments where most contact durations are short. In this work, we eliminate the assumption of unfragmented data, and propose a single-copy fragmented data routing strategy (FDR) that performs well with short contact durations. FDR considers the exponential distribution of inter-contact time, contact duration time, and fragmented data sizes when making forwarding decisions. We show that under short contact durations and large message sizes, our scheme can achieve up to 41% higher delivery rate and 38% lower delay compared to other routing strategies.

An Application-Oriented Buffer Management Strategy in Opportunistic Networks

In Opportunistic networks (ONs), buffer management is critical to improve the message exchanging efficiency due to the limited storage space and transmission bandwidth at the wireless edge. Current solutions make message scheduling and drop policy based on assumptions that messages can always been forwarded in a single contact, and all node pairs have the same contact rates. However, such ideal assumptions are invalid for realistic mobility traces of hand-held. Recent studies show that the single contact duration is limited and the mobility of nodes is heterogeneous in reality. In this paper, a buffer management strategy based on contact duration and heterogeneous mobility is proposed to improve the efficiency of buffer policy in the practical applications. We mainly focus on the minimization of the total expected delivery delay for all messages in ONs with resource constraints. Using the global network information including existing copies of message in the network, the distribution of pair-wise inter-contact time and contact duration between nodes, we develop a function to compute per-message utility which reflects the contribution of single message to the total expected delivery delay. Messages are scheduled or dropped according to their utilities. Simulation results show that our proposed strategy not only achieves lower delivery delay than mainstream strategies, but also keeps a high delivery ratio and a low network overhead.

Time-constrained anycast routing under short contact duration in 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. Anycast is an important group communication paradigm for numerous DTN applications such as resource discovery and information exchange in emergency or crisis situations. Unlike unicast and multicast, which have been studied extensively in DTNs, few prior works have addressed the DTN anycast routing problem. Furthermore, they often ignore the time constraint and assume long contact durations in formulating the relay selection strategy. In this paper, we study a single-copy time-constrained anycast (TCA) routing under short contact duration. We address two key issues: (1) to which next hop relay node should messages be forwarded and (2) in which order should messages be forwarded. To reduce the transmission cost, we select relay nodes from both current and past contacts based on the one-hop and two-hop delivery probabilities, respectively. We derive the delivery probability from the distribution of inter-contact time and contact duration time. We address the case of exponential and Pareto distribution, which are the most popular assumptions in literature. For the message scheduling, messages with the highest delivery probability are prioritized to be transmitted first. Extensive simulation results based on Cabspotting and MIT Reality traces show that our scheme can achieve up to 29% higher delivery rate, 24% lower delay, and 36% lower transmission cost compared to other anycast routing strategies.

What You Lose When You Snooze

In opportunistic networks, putting devices in energy-saving mode is crucial to preserve their battery, and hence to increase the lifetime of the network and foster user participation. A popular strategy for energy saving is duty cycling. However, when in energy-saving mode, users cannot communicate with each other. The side effects of duty cycling are twofold. On the one hand, duty cycling may reduce the number of usable contacts for delivering messages, increasing intercontact times, and delays. On the other hand, duty cycling may break long contacts into smaller contacts, thus also reducing the capacity of the opportunistic network. Despite the potential serious effects, the role played by duty cycling in opportunistic networks has been often neglected in the literature. In order to fill this gap, in this article, we propose a general model for deriving the pairwise contact and intercontact times measured when a duty cycling policy is superimposed on the original encounter process determined only by node mobility. The model we propose is general, i.e., not bound to a specific distribution of contact and intercontact times, and very accurate, as we show exploiting two traces of real human mobility for validation. Using this model, we derive several interesting results about the properties of measured contact and intercontact times with duty cycling: their distribution, how their coefficient of variation changes depending on the duty cycle value, and how the duty cycling affects the capacity and delay of an opportunistic network. The applicability of these results is broad, ranging from performance models for opportunistic networks that factor in the duty cycling effect, to the optimisation of the duty cycle to meet a certain target performance.

VSTP: vessel spatio-temporal contact pattern detection based on MapReduce

Due to lack of the coverage of 3G/4G network, satellite communication which costs excessively is the main approach used in ocean to provide network service. Ocean mobile delay tolerant network (OMDTN) can provide low-cost data transmission service in the network by utilizing the contact chances of moving vessels. Spatio-temporal contact pattern is one of the key metrics to improve the efficiency of the routing algorithm in OMDTN. Some researches have been carried out on human handheld device and vehicular ad hoc networks (VANETs). However, the vessel’s trajectory data is distributed and stored disorderly, which makes traditional contact pattern detection algorithm cannot be directly applied. In this paper, we design a parallel algorithm named VSTP based on MapReduce to detect spatio-temporal contact pattern from trajectories of over 2000 vessels. Studying the vessels’ trajectories and the contact records, we observe that the vessels’ contact pattern including inter-contact time distribution and contact times distribution is in sharp contrast to the study on human handheld device and VANETs. Our results can provide the guidelines for the design of data routing protocols on OMDTN and give a new solution to overcome the difficulty of ocean network coverage.

Link Modeling and Delay Analysis in Networks with Disruptive Links

Delay- and Disruption-Tolerant Networks (DTNs) refer to a range of networks with link intermittency that is mainly driven by mobility, predictable or unpredictable network environmental conditions. Examples of DTNs include interplanetary networks, battlefield networks, smart highways, remote sensing, and animal-movement outposts. There exist a number of mobility models describing the operation of various DTNs. One common characteristic that all mobility models share is the distribution of contact time and inter-contact time between nodes. Predicting an end-to-end delay in networks with disruptive links is more complicated than predicting the delay in connected networks. Disruptive patterns and underlying routing algorithms play a major role in an end-to-end delay modeling. In this article, we introduce a new model that can be used to estimate the end-to-end delay in networks with intermittent links. The model incorporates the two non-deterministic delay distributions, namely link intermittency and tandem queuing delay distributions. The model is based on an open queuing system with exponentially distributed link intermittency. The model gives a close approximation of the average end-to-end delay and the delay variance in closed forms. Simulation results on various networks and under different traffic conditions confirm the accuracy of the model within the conventional bounds of statistical significance.

Imperfect spreading on temporal networks

We study spreading on networks where the contact dynamics between the nodes is governed by a random process and where the inter-contact time distribution may differ from the exponential. We consider a process of imperfect spreading, where transmission is successful with a determined probability at each contact. We first derive an expression for the inter-success time distribution, determining the speed of the propagation, and then focus on a problem related to epidemic spreading, by estimating the epidemic threshold in a system where nodes remain infectious during a finite, random period of time. Finally, we discuss the implications of our work to design an efficient strategy to enhance spreading on temporal networks.

Analyzing and modeling mobility for infrastructure-less communication

Unlike infrastructure-based counterparts, services and applications of future infrastructure-less mobile networks highly depend on the mobility patterns of wireless device carriers. Analyzing and modeling their mobility is essential in precise performance evaluation of these opportunistic networks, where inter-contacts are taken advantage of as opportunities for message forwarding. Recently, the fact that human mobility is not entirely random has been proved. To this end, some sophisticated mobility models have emerged to try to capture observed aggregate mobility characteristics. However, characteristics in aggregate level are different from those in individual node level. We provided general analysis and found that distributions of rates and durations played important roles in determining the nature of the aggregate distribution. Aggregate distribution can be regarded as being formed by distributions of different nodes or distributions of different rates. In addition, an individual node usually experiences active or inactive periods alternatively. Using only cumulative distribution characteristics cannot appropriately describe the nodal mobility. So as to precisely analyze the nature of the device carriers׳ mobility in both levels and capture transitions between active and inactive periods, we proposed a flexible framework, where the sequences of inter-contact times were modeled as Semi-Markov Modulated Process. This framework provides flexibility to configure each node׳s inter-contact time distribution independently, thus capturing the heterogenous behavior at will. At the same time, it could obtain required aggregate distribution and variable activities.

The Characterizes of Communication Contacts Between Vehicles and Intersections for Software-Defined Vehicular Networks

In software-defined vehicular networks, a centralized controller in the network through the road-side unit will greatly promote the efficiency of the network efficiency by temporarily storing the packets, improving the delivery ratio and providing highly reliable transmission. The inter-contact time between moving vehicles and intersections, and the duration of each contact are two key metrics in the designing and evaluating of this kind of software-defined vehicular networks, because they decide the frequency of contacts between vehicles and the intersections and how much data can be sent during the contact. In this paper, we analyze traces of thousands of operational taxies in Shanghai. By studying the inter-contact time and the duration of each contact between taxi-intersection pairs, we find that the distribution of inter-contact time follows a power-law up to a small value and decays exponentially afterwards. We find that a power-law with exponential decay model can approximate the distribution of inter-contact time better than exponential model, power law model and piecewise exponential decay model. The duration of contacts, on the other hand, mainly exhibits an exponential decay. Our findings make way for the future study of and the deployment of software-defined vehicular networks.

Flooding Time in Opportunistic Networks under Power Law and Exponential Intercontact Times

Performance bounds for opportunistic networks have been derived in a number of recent papers for several key quantities, such as the expected delivery time of a unicast message, or the flooding time (a measure of how fast information spreads). However, to the best of our knowledge, none of the existing results is derived under a mobility model which is able to reproduce the power law+exponential tail dichotomy of the pairwise node inter-contact time distribution which has been observed in traces of several real opportunistic networks. The contributions of this paper are two-fold: first, we present a simple pairwise contact model -- called the Home-MEG model -- for opportunistic networks based on the observation made in previous work that pairs of nodes in the network tend to meet in very few, selected locations (home locations); this contact model is shown to be able to faithfully reproduce the power law+exponential tail dichotomy of inter-contact time. Second, we use the Home-MEG model to analyze flooding time in opportunistic networks, presenting asymptotic bounds on flooding time that assume different initial conditions for the existence of opportunistic links. Finally, our bounds provide some analytical evidences that the speed of information spreading in opportunistic networks can be much faster than that predicted by simple geometric mobility models.

Impact of human behavior on social opportunistic forwarding

The current Internet design is not capable of supporting communications in environments characterized by very long delays and frequent network partitions. To allow devices to communicate in such environments, delay-tolerant networking solutions have been proposed by exploiting opportunistic message forwarding, with limited expectations of end-to-end connectivity and node resources. Such solutions envision non-traditional communication scenarios, such as disaster areas and development regions. Several forwarding algorithms have been investigated, aiming to offer the best trade-off between cost (number of message replicas) and rate of successful delivered message. Among such proposals, there has been an effort to employ social similarity, inferred from users’ mobility patterns, to improve opportunistic forwarding solutions. However, such proposals present two major drawbacks: first, they focus on distribution of intercontact times over the complete network structure, ignoring the impact that human behavior has on the dynamics of the network; and second, most of the proposed solutions look at challenging networking environments where networks have low density, ignoring the potential use of delay-tolerant networking to support low cost communications in networks with higher density, such as urban scenarios. This paper presents a study of the impact that human behavior has on opportunistic forwarding. Our goal is twofold: (i) to show that performance in low and high density networks can be improved by taking the dynamics of the network into account; and (ii) to show that delay-tolerant networking can be used to reduce communication costs in networks with high density by considering the users’ behavior.

Analysis of Individual Pair and Aggregate Intercontact Times in Heterogeneous Opportunistic Networks

Foundational work in the area of opportunistic networks has shown that the distribution of intercontact times between pairs of nodes has a key impact on the network properties, for example, in terms of convergence of forwarding protocols. Specifically, forwarding protocols may yield infinite expected delay if the intercontact time distributions present a particularly heavy tail. While these results hold for the distributions of intercontact times between individual pairs, most of the literature uses the aggregate distribution, i.e., the distribution obtained by considering the samples from all pairs together, to characterize the properties of opportunistic networks. In this paper, we provide an analytical framework that can be used to check when this approach is correct and when it is not, and we apply it to a number of relevant cases. We show that the aggregate distribution can be way different from the distributions of individual pair intercontact times. Therefore, using the former to characterize properties that depend on the latter is not correct in general, although this is correct in some cases. We substantiate this finding by analyzing the most representative distributions characterizing real opportunistic networks that can be obtained from reference traces. We review key cases for opportunistic networking, where the aggregate intercontact time distribution presents a heavy tail with or without exponential cutoff. We show that, when individual pairs follow Pareto distributions, the aggregate distribution consistently presents a heavy tail. However, heavy tail aggregate distributions can also emerge in networks where individual pair intercontact times are not heavy tailed, for example, exponential or Pareto with exponential cutoff distributions. We show that an exponential cutoff in the aggregate appears when the average intercontact times of individual pairs are finite. Finally, we discuss how to use our analytical model to know whether collecting aggregate information about intercontact times is sufficient or not, to decideâin practiceâwhich type of routing protocols to use.

Distribution of inter-contact time: An analysis-based on social relationships

Communication in delay tolerant networks (DTNs) relies on message transport by mobile nodes, and a correct understanding of the node mobility characteristics is therefore crucial to the design of an efficient DTN routing protocol. However, previous work has mainly focused on uncovering all behaviors of node movement, which is not conducive to accurately detecting the specific movement characteristics of a different node. In this paper, we seek to address this problem based on a consideration of social relationships. We first consider social ties from both static and dynamic perspectives. For a static perspective, in addition to certain accidental events, social relations are considered for a long time granularity and tend to be stable over time. For a dynamic perspective, social relations are analyzed in a relatively short time granularity and are likely to change over time. Based on these perspectives, we adopted different efficient approaches to dividing node pairs into two classes, i.e., familiar and unfamiliar pairs. A threshold approach is used for static social ties whereas a density-based aggregation method is used for dynamic social relationships. Extensive experimental results show that both familiar and unfamiliar node pairs have the same inter-contact time distribution, which closely follows a power-law decay up to a certain point, beyond which it begins to exponentially decay. The results also demonstrate that the inter-contact time distribution of familiar pairs decays faster than that of unfamiliar pairs, whether from a static or dynamic perspective. In addition, we also analyze the reason for the difference between the inter-contact time distributions of both unfamiliar and familiar pairs.

Detecting Hot Road Mobility of Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) can provide value-added services to both drivers and passengers with on-board vehicular communication systems. Node mobility and volatile wireless connection in VANETs affect inter-contact time (TI) between mobile nodes, which greatly degrades the performance of vehicular applications. Nevertheless, the node spatial distribution in VANETs is another important factor especially in real applications. It positively affects the inter-contact time of vehicular nodes. By leveraging it, we can significantly improve the performance of data transmissions and inter-vehicle communication. To this end, we investigate the data collected from around 4,000 taxisin Shanghai and propose in this paper an efficient hot road mobility model. We find that most taxis distribute on some hot roads, which makes the node spatial distribution follow the power law. Based on this observation, we propose the concepts of indirect contact and heterogeneous inter-contact time (TH) to reveal how hot roads can change the distribution of inter-contact time. We find that the tail distribution of TH also appears the power law, and both node spatial distribution and TH distribution decay at least as the power law. We further propose a model for detecting vehicle mobility in hot roads, which can generates synthetic traces that captures both spatial and temporal features of nodes in VANETs.

SPoT: Representing the social, spatial, and temporal dimensions of human mobility with a unifying framework

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they arose from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a flexible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility.Our framework, named SPoT, is able to incorporate the three dimensions–spatial, social, and temporal–of human mobility. The way SPoT does this is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases, and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the flexibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a thorough mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge.

Evaluating performance of two-hop routing in DTN based on Home-MEG model

Performance analysis in delay tolerant networks (DTN) is often based on unrealistic mobility models. Recently, the Home-MEG model is proposed. This model is able to accurately reproduce the power law and exponential decay distribution of inter-contact times between mobile nodes which has been observed in many real motion traces. This letter proposes a theoretical framework to evaluate the performance of two-hop routing based on Home-MEG model for the first time. Simulation results show the accuracy of our theoretical model. Using the model, we explore the impact of some parameters (e.g. the number of relay nodes) on the routing performance.

Human-Mobility-Based Sensor Context-Aware Routing Protocol for Delay-Tolerant Data Gathering in Multi-Sink Cell-Phone-Based Sensor Networks

Ubiquitous use of cell phones encourages development of novel applications with sensors embedded in cell phones. The collection of information generated by these devices is a challenging task considering volatile topologies and energy-based scarce resources. Further, the data delivery to the sink is delay tolerant. Mobility of cell phones is opportunistically exploited for forwarding sensor generated data towards the sink. Human mobility model shows truncated power law distribution of flight length, pause time, and intercontact time. The power law behavior of inter-contact time often discourages routing of data using naive forwarding schemes. This work exploits the flight length and the pause time distributions of human mobility to design a better and efficient routing strategy. We propose a Human-Mobility-based Sensor Context-Aware Routing protocol (HMSCAR), which exploits human mobility patterns to smartly forward data towards the sink basically comprised of wi-fi hot spots or cellular base stations. The simulation results show that HMSCAR significantly outperforms the SCAR, SFR, and GRAD-MOB on the aspects of delivery ratio and time delay. A multi-sink scenario and single-copy replication scheme is assumed.

Empirical Data Analysis in Urban VANET Using Exponential Decay of Inter Contact Time

VANET is a technology that uses moving vehicles as nodes in a network to create a mobile network .It is used to improve safety, security and efficiency of transportation systems. Inter-contact Time between moving vehicle is a key metrics in VANET , and for forwarding data and end to end delay. Inter-contact Time denotes the time elapsed between two successive contacts of the same two vehicles. In order to trace data on the frequencies and duration of transfers between vehicles three models were upgraded. No method or mobility models are proposed to decrease the end to end delay while transferring process, due to interference and signal loss of wireless links. The proposed system uses a Time Variant Random Way Point Mobility Model which minimizes the inter-contact time distribution, data transfer rate and reduces the delay. It improves the texture of Common traffic influxes and Common traffic converges.

A token based distributed algorithm for supporting mutual exclusion in opportunistic networks

Opportunistic networks are essentially distributed networks with transient connectivity among nodes. Nodes in opportunistic networks are resource constrained, mobile and opportunistically come in contact with each other. In such a distributed network, nodes may require exclusive access to a shared object or resource. Ensuring freedom from starvation is a challenging problem in opportunistic networks due to limited pairwise connectivity and node failures. In this paper, we review mutual exclusion algorithms proposed for generic mobile ad hoc networks (MANETs) and discuss their applicability to opportunistic networks. Further, we propose a novel token based algorithm11The research work presented in this paper was carried out with support from the US National Foundation GrantsECCS-0824120 and CSR 0834493. and prove its correctness. Simulation results show that our algorithm is communication efficient as compared to other algorithms proposed for generic mobile ad hoc networks. We also propose a timeout based fault detection algorithm that exploits the intercontact time distributions.