Caching in opportunistic networks with churn

SummaryOpportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.

Opportunistic networks are usually formed spontaneously by mobile devices equipped with short range wireless communication interfaces. The idea is that an end-to-end connection may never be present. Designing and implementing a routing protocol to support both service discovery and delivery in such kinds of networks is a challenging problem on account of frequent disconnections and topology changes. In this network one of the most important issues relies on the selection of the best intermediate node to forward the messages towards the destination. This paper presents a trust framework for opportunistic network where the nodes in the network follow the trace based mobility model. The selection of next hop to forward the data packets is based on the trust value as well as the direction of movement of node towards the destination. The trust value is obtained from the trust framework of the data forwarding node. The direction of destination is obtained from the movement trace file that is maintained by the nodes in the network. In this proposed framework, the message is encrypted to secure both the data and path information. The effectiveness of this proposed framework is shown using simulation.

Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.

Realistic mobility models are crucial for the simulation of Delay Tolerant and Opportunistic Networks. The long standing benchmark of reproducing realistic pairwise statistics (e.g., contact and inter-contact time distributions) is today mastered by state-of-the-art models. However, mobility models should also reflect the macroscopic community structure of who meets whom. While some existing models reproduce realistic community structure - reflecting groups of nodes who work or live together - they fail in correctly capturing what happens between such communities: they are often connected by few bridging links between nodes who socialize outside of the context and location of their home communities. In a first step, we analyze the bridging behavior in mobility traces and show how it differs to that of mobility models. By analyzing the context and location of contacts, we then show that it is the social nature of bridges which makes them differ from intra-community links. Based on these insights, we propose a Hypergraph to model time-synchronized meetings of nodes from different communities as a social overlay. Applying this as an extension to two existing mobility models we show that it reproduces correct bridging behavior while keeping other features of the original models intact.

There are many cities where urbanization occurs at a faster rate than that of communication infrastructure deployment. Mobile users with sophisticated devices are often dissatisfied with this lag in infrastructure deployment; their Internet connection is via opportunistic open Access Points for short durations, or via weak, unreliable, and costly 3G connections. With increased demands on network infrastructure, we believe that opportunistic networking, where user mobility is exploited to increase capacity and augment Internet reachability, can play an active role as a complimentary technology to improve user experience, particularly with delay insensitive data. Opportunistic forwarding solutions were mainly designed using a set of assumptions that have grown in complexity, rendering them unusable outside their intended environment. Figure 1 categorizes sample state-of-the-art opportunistic forwarding solutions based on their assumption complexity. Most of these solutions however are not designed for large scale urban environments. In this work, we believe to be the first to exploit the space syntax paradigm to better guide forwarding decisions in large scale urban environments. Space syntax, initially proposed in the field of architecture to model natural mobility patterns by analyzing spacial configurations, offers a set of measurable metrics that quantify the effect of road maps and architectural configurations on natural movement. By interacting with the pre-built static environment, space syntax predicts natural movement patterns in a given area. Our goal is to leverage space syntax concepts to create efficient opportunistic forwarding distributed solutions for large scale urban environments. We address two communication themes: (1) Mobile-to-Infrastructure: We propose a set of space syntax based algorithms that adapt to a spectrum of simplistic assumptions in urban environments. As depicted in Figure 1, our goal is to gain performance improvement across the spectrum, within each assumption category, when compared to other state-of-the-art solutions. We adopt a data driven approach to evaluate the space syntax based forwarding algorithms we propose, within each of three assumption categories, based on large scale mobility traces capturing vehicle mobility. Overall, our results show that our space syntax based algorithms perform more efficiently within each assumption category. (2) Infrastructure-to-Mobile: We propose a new algorithm, Select&Spray, which leverages space syntax, and enables data transfers to mobile destinations reached directly through the infrastructure or opportunistically via other nodes. This architecture consists of: (i) a select engine that identifies a subset of directly connected nodes with a higher probability to forward messages to destinations, and (ii) a spray engine residing on mobile devices that guide the opportunistic dissemination of messages towards destination devices. We evaluate our algorithm using several mobility traces. Our results show that Select&Spray is more efficient in guiding messages towards their destinations. It helps extend the reach in data dissemination to more than 20% of the interested destinations within very short delays, and successfully reaches almost 90% of the destinations in less than 5 minutes.

Intermittently connected opportunistic networks experience frequent disconnections and shorter contact durations. Therefore routing of messages towards their destinations needs to be handled from various points of view. Predictability and connectedness are two information which can be determined by participating mobile nodes of an opportunistic content distribution network using their past contacts. Epidemic or probabilistic routing protocols do not fully utilize these information to route messages towards their destinations. In this paper we describe the routing algorithm, implementation details, experiment design and the performance validation of a new, adaptive routing protocol which utilizes the predictability and connectedness information to route messages efficiently. Simulation based comparative studies show that the proposed routing protocol outperforms existing Epidemic and probabilistic routing protocols in delivering messages.

An increasing number of mobile users (MUs) share common interests in general information, such as traffic information, weather forecast, and domestic/international news. However, the centralized infrastructure-based system has not been designed for efficiently disseminating the information of common interest (IoCI) to numerous requesters. Thanks to the rapid development of mobile devices equipped with large storage and multiple communication modes, opportunistic communication between a pair of MUs can be readily realized. With the aid of opportunistic networks formed by MUs, we can improve the connectivity of cellular networks in the rural areas, we can offload the tele-traffic from the overloaded cellular networks, and we can efficiently disseminate the IoCI in the densely populated areas. We commence with a detailed survey on the cross-disciplinary research area of social network analysis aided telecommunication networking. We continue by focusing our attention on the family of integrated cellular and large-scale opportunistic networks, whose performance is dominated by the inter-contact duration as well as the contact duration between any pairs of MUs. A continuous-time-pure-birth Markov chain is invoked for analyzing the relevant performance. We demonstrate that the delivery ratio of the IoCI before it expires becomes higher than 90% with the aid of opportunistic networks consisting of 20 MUs. Moreover, our experiments based on the InfoCom 2006 mobility trace show that the opportunistic networks are capable of offloading 58% of the tele-traffic from the cellular networks. Thereafter, we propose a hybrid information dissemination scheme for the integrated cellular and small-scale opportunistic networks, which is comprised of two main stages: 1) the base station-aided single-hop multicast (BSSHM) stage and 2) the cooperative multicast aided spontaneous dissemination stage. In contrast to their large-scale counterparts, in small-scale opportunistic networks, the information dissemination is mainly affected by the mobility of the MUs, by the wireless channel attenuation and by the resource scheduling, where a discrete-time-pure-birth Markov chain is utilized for characterizing the relevant performance. We demonstrate that our hybrid information dissemination scheme outperforms the traditional BSSHM in terms of various delay metrics, despite consuming less energy.

Opportunistic networks are formed by small devices that communicate over a wireless link with each other. Opportunistic network possess various heterogeneous resources. Resource management and scheduling of these heterogeneous resources are major problems in the Opportunistic network due to highly dynamic behavior of the devices in the opportunistic network. Especially challenging is the multisuser aspect in the opportunistic network environment, since with a high and changeable number of users the workload is difficult to predict, and so making a proper scheduling of tasks becomes a serious problem. The overheads imposed by the waiting queues may become a major part of the total execution time. So to efficiently manage the resources in the opportunistic network, a Grid environment is considered to address the problem of fair sharing. An auction model which is an economic based technique has also been considered in the Grid environment. In the proposed work, a second price auction mechanism is considered to employ a pricing mechanism that is incentive compatible. Incentive compatible pricing mechanisms ensure that the users' self-interests are maximized when they honestly reveal their preferences to the allocation mechanism. These mechanisms can provide powerful guarantees about optimal user behavior and can deliver highly efficient allocations. So in the proposed work, a second price auction mechanism is considered which is very efficient and also it elicits honest information from the participants thereby successfully managing the resource allocation in the Grid environment. A Gossip based mechanism is also considered in the proposed work in order to reduce the overheads caused by the waiting queues.

Existing routing schemes in opportunistic networks can be classified two categories: multi-copy based schemes and single-copy based schemes. Multi-copy based schemes can increase the robustness of networks and reduce average delay. However, they consume a large amount of network resources and induce large overhead. Single-copy based schemes can save network resources, but it is difficult to guarantee the transmission delay and packet delivery ratio for these schemes. Hence, how to design an efficient routing scheme which can make a tradeoff between multi-copy schemes and single-copy schemes in opportunistic networks with limited resources is a challenging issue. Inspired by this issue, in this paper, we propose a novel routing scheme based on game theory in opportunistic networks under limited resources, named GTR. In GTR, we map the message transmission to a multi-player bargaining model. By constructing proper utility function, GTR can save the network resources, and achieve better performance in dense opportunistic networks. Our extensive simulation results also demonstrate this point, and GTR performs better compared to some classical routing schemes in terms of packet delivery ratio, average delay, overhead ratio and average hops.

A geo-temporal mobility prediction method for cooperative time-validity-constrained content delivery in opportunistic networks

A pioneering body of work in the area of mobile opportunistic networks has shown that characterizing aggregated inter-contact time between mobile nodes is crucial. The most common approaches to study aggregated inter-contact time (ICT) in literatures are focused on a certain type of mobile wireless networks. Up to now, it still lacks a general approach to model the aggregated ICT effectively in different type of networks. This paper proposes a new approach to model aggregated ICT based on extreme value theory (EVT), which uses the generalized Pareto distribution as the unique asymptotic model of the tail distribution. Mean excess function is proposed to choose the threshold value and also other parameter estimation methods to the statistical model are discussed and applied to real mobility traces collected from different opportunistic networks, such as social pocket switched networks and vehicular ad hoc network. A decision rule based on confidence level of parameter is proposed to determine the statistical characteristics of aggregated ICT. By performing extensive experiments using EVT-based model, the aggregated ICT distributions for different type of networks are obtained. To show how well the EVT-based model actually fits the data in the tails, the complementary cumulative distribution function in a logarithmic scale is used to validate the model. Also according to the Kolmogorov---Smirnov test, it illustrates that EVT-based distributions with different parameter values fit the tails well at the desired significance level 0.05 for the taxi mobility in VANET and the human node in the other type of opportunistic networks. Finally, some prospective applications based on the statistical model are discussed in designing new network protocols.

Understanding mobility characteristics is important for the design and analysis of routing schemes for mobile ad hoc networks (MANETs). This is especially true for mobile opportunistic networks where node mobility is utilized to achieve message delivery. In this paper, we study the properties of common mobility models. Specifically, we study inter-contact times of mobile nodes in random waypoint and random direction mobility models under opportunistic network settings. We also introduce a modified random waypoint model with hot-spots to study its mobility properties. Through extensive simulation study, we provide simulation results for mobility properties of random waypoint and random direction models. Further, our modified random waypoint with hot-spots model is also found to show an approximate power-law and exponential inter-contact time dichotomy found in real-world mobility traces as described in recent literature.

In this paper, we present a forwarding algorithm for mobile social opportunistic networks which is both centrality-based and energy-aware. In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Centrality-based forwarding schemes use the structurally important nodes (i.e. nodes having many contacts with neighbors) as message relays and have been shown to be good candidates for social forwarding in opportunistic network environments. However, the most central nodes which are structurally more important than other network nodes, have the disadvantage to be used very frequently as message relays, thus consuming their energy very quickly. Here, we exploit the advantage of using a centrality-based scheme which is distributed and needs only the history of contacts as social information. In addition, we modulate the computed node centrality value using the node energy level in order to avoid transmissions in nodes more central but with a limited energy budget. Simulating three real-world mobility traces, we show that our scheme is able to achieve better delivery ratio and overhead ratio while sensibly reducing the energy consumption of nodes.

Opportunistic networks consist of mobile devices, carried by people in their everyday lives. They organize autonomously to exchange data with direct neighbors without the use of any infrastructural services. Since the devices are carried by humans, one of the main challenges to consider in opportunistic networks is the human mobility behavior. However, little work exists on how the social behavior of people drives their mobility behavior and how this context information can be systematically leveraged for opportunistic networking applications. This paper tackles this problem by providing both experimental and theoretical analysis of human mobility context information. We present a novel real world experiment with sensor nodes carried by people to demonstrate and study the effect of context on people mobility. Furthermore, we define a novel metric of social distance to put this new evidence on solid mathematical foundation. Thus, our work puts a basis to systematically leveraging context information for opportunistic networking applications and services. Additionally, our experimental data traces enable testing and evaluation of such novel services in a real world scenario.

This paper presents a publish/subscribe-based multicast distribution infrastructure for DTN-based opportunistic networking environments. The distribution approach is designed to combine an effective distribution of content to interested nodes in the presence of resource constraints, mobility and unstable connectivity. By considering local resource constraints such as limited storage space and limited available bandwidth at opportunistic contacts as well as knowledge about interest for content in the network environment, nodes make local decisions about resource utilization and DTN bundle prioritization. Without further coordination, this approach uses the overall available network resources more effectively compared to other approaches such as epidemic forwarding. We have implemented this approach and have performed a series of measurements in mobile opportunistic networking scenarios under different configurations.