Several social-aware forwarding strategies have been recently introduced in opportunistic networks, and proved effective in considerably in- creasing routing performance through extensive simulation studies based on real-world data. However, this performance improvement comes at the expense of storing a considerable amount of state information (e.g, history of past encounters) at the nodes. Hence, whether the benefits on routing performance comes directly from the social-aware forwarding mechanism, or indirectly by the fact state information is exploited is not clear. Thus, the question of whether social-aware forwarding by itself is effective in improving opportunistic network routing performance remained unaddressed so far. In this paper, we give a first, positive answer to the above question, by investigating the expected message delivery time as the size of the net- work grows larger.
Performance analysis of opportunistic networks has been subject of intensive research in recent years. In particular, the analysis of routing performance -- expressed in terms of the expected message delivery time, as done in this paper -- has been considered in @cite_9 @cite_17 @cite_20 @cite_7 @cite_5 @cite_10 . More recently, also the distribution of the message delivery time has been studied @cite_11 . These studies assume a mobility model equivalent to one of the two-mobility models considered in this paper, namely the social-oblivious mobility model. Furthermore, they all consider social-oblivious routing protocols such as epidemic @cite_16 , two-hops @cite_3 , and BinarySW routing @cite_5 .
@cite_11 @cite_7 @cite_9 @cite_3 @cite_5 @cite_16 @cite_10 @cite_20 @cite_17
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In this paper, we present a framework for analyzing routing performance in delay tolerant networks (DTNs). Differently from previous work, our framework is aimed at characterizing the exact distribution of relevant performance metrics, which is a substantial improvement over existing studies characterizing either the expected value of the metric, or an asymptotic approximation of the actual distribution. In particular, the considered performance metrics are packet delivery delay, and communication cost, expressed as number of copies of a packet circulating in the network at the time of delivery. Our proposed framework is based on a characterization of the routing process as a stochastic coloring process and can be applied to model performance of most stateless delay tolerant routing protocols, such as epidemic, two-hops, and spray and wait. After introducing the framework, we present examples of its application to derive the packet delivery delay and communication cost distribution of two such protocols, namely epidemic and two-hops routing. Characterizing packet delivery delay and communication cost distribution is important to investigate fundamental properties of delay tolerant networks. As an example, we show how packet delivery delay distribution can be used to estimate how epidemic routing performance changes in presence of different degrees of node cooperation within the network. More specifically, we consider fully cooperative, noncooperative, and probabilistic cooperative scenarios, and derive nearly exact expressions of the packet delivery rate (PDR) under these scenarios based on our proposed framework. The comparison of the obtained packet delivery rate estimation in the various cooperation scenarios suggests that even a modest level of node cooperation (probabilistic cooperation with a low probability of cooperation) is sufficient to achieve 2-fold performance improvement with respect to the most pessimistic scenario in which all potential forwarders drop packets. Considered is a mobile ad hoc network consisting of three types of nodes (source, destination and relay nodes) and using the two-hop relay routing protocol. Packets at relay nodes are assumed to have a limited lifetime in the network. All nodes are moving inside a bounded region according to some random mobility model. Both closed-form expressions, and asymptotic results when the number of nodes is large, are provided for the packet delivery delay and the energy needed to transmit a packet from the source to its destination. We also introduce and evaluate a variant of the two-hop relay protocol that limits the number of generated copies in the network. Our model is validated through simulations for two mobility models (random waypoint and random direction mobility models), numerical results for the two-hop relay protocols are reported, and the performance of the two-hop routing and of the epidemic routing protocols are compared. The capacity of ad hoc wireless networks is constrained by the mutual interference of concurrent transmissions between nodes. We study a model of an ad hoc network where n nodes communicate in random source-destination pairs. These nodes are assumed to be mobile. We examine the per-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery. Under this assumption, the per-user throughput can increase dramatically when nodes are mobile rather than fixed. This improvement can be achieved by exploiting a form of multiuser diversity via packet relaying. Intermittently connected mobile networks are wireless networks where most of the time there does not exist a complete path from the source to the destination. There are many real networks that follow this model, for example, wildlife tracking sensor networks, military networks, vehicular ad hoc networks, etc. In this context, conventional routing schemes fail, because they try to establish complete end-to-end paths, before any data is sent. To deal with such networks researchers have suggested to use flooding-based routing schemes. While flooding-based schemes have a high probability of delivery, they waste a lot of energy and suffer from severe contention which can significantly degrade their performance. Furthermore, proposed efforts to reduce the overhead of flooding-based schemes have often been plagued by large delays. With this in mind, we introduce a new family routing schemes that "spray" a few message copies into the network, and then route each copy independently towards the destination. We show that, if carefully designed, spray routing not only performs significantly fewer transmissions per message, but also has lower average delivery delays than existing schemes; furthermore, it is highly scalable and retains good performance under a large range of scenarios. Finally, we use our theoretical framework proposed in our 2004 paper to analyze the performance of spray routing. We also use this theory to show how to choose the number of copies to be sprayed and how to optimally distribute these copies to relays. Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100 of messages with reasonable aggregate resource consumption in a number of interesting scenarios. In this paper, we develop a rigorous, unified framework based on ordinary differential equations (ODEs) to study epidemic routing and its variations. These ODEs can be derived as limits of Markovian models under a natural scaling as the number of nodes increases. While an analytical study of Markovian models is quite complex and numerical solution impractical for large networks, the corresponding ODE models yield closed-form expressions for several performance metrics of interest, and a numerical solution complexity that does not increase with the number of nodes. Using this ODE approach, we investigate how resources such as buffer space and the number of copies made for a packet can be traded for faster delivery, illustrating the differences among various forwarding and recovery schemes considered. We perform model validations through simulation studies. Finally we consider the effect of buffer management by complementing the forwarding models with Markovian and fluid buffer models. We study data transfer opportunities between wireless devices carried by humans. We observe that the distribution of the intercontact time (the time gap separating two contacts between the same pair of devices) may be well approximated by a power law over the range [10 minutes; 1 day]. This observation is confirmed using eight distinct experimental data sets. It is at odds with the exponential decay implied by the most commonly used mobility models. In this paper, we study how this newly uncovered characteristic of human mobility impacts one class of forwarding algorithms previously proposed. We use a simplified model based on the renewal theory to study how the parameters of the distribution impact the performance in terms of the delivery delay of these algorithms. We make recommendations for the design of well-founded opportunistic forwarding algorithms in the context of human-carried devices