7,033 research outputs found

    A Reliable and Efficient Encounter-Based Routing Framework for Delay/Disruption Tolerant Networks

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    This article addresses Delay/Disruption Tolerant Networking (DTN) routing under a highly dynamic scenario, envisioned for communication in Vehicular Sensor Networks (VSNs) suffering from intermittent connection. Here, we focus on the design of a high level routing framework, rather than the dedicated encounter prediction. Based on an analyzed utility metric to predict nodal encounter, our proposed routing framework considers the following three cases: 1) Messages are efficiently replicated to a better qualified candidate node, based on the analysed utility metric related to destination. 2) Messages are conditionally replicated if the node with a better utility metric has not been met. 3) Messages are probabilistically replicated if the information in relation to destination is unavailable in the worst case. With this framework in mind, we propose two routing schemes covering two major technique branches in literature, namely Encounter-Based Replication Routing (EBRR) and Encounter-Based Spraying Routing (EBSR). Results under the scenario applicable to VSNs show that, in addition to achieving high delivery ratio for reliability, our schemes are more efficient in terms of a lower overhead ratio. Our core investigation indicates that apart from what information to use for encounter prediction, how to deliver messages based on the given utility metric is also important

    A novel queue management policy for delay-tolerant networks

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    Delay-tolerant networks (DTNs) have attracted increasing attention from governments, academia and industries in recent years. They are designed to provide a communication channel that exploits the inherent mobility of trams, buses and cars. However, the resulting highly dynamic network suffers from frequent disconnections, thereby making node-to-node communications extremely challenging. Researchers have thus proposed many routing/forwarding strategies in order to achieve high delivery ratios and/or low latencies and/or low overheads. Their main idea is to have nodes store and carry information bundles until a forwarding opportunity arises. This, however, creates the following problems. Nodes may have short contacts and/or insufficient buffer space. Consequently, nodes need to determine (i) the delivery order of bundles at each forwarding opportunity and (ii) the bundles that should be dropped when their buffer is full. To this end, we propose an efficient scheduling and drop policy for use under quota-based protocols. In particular, we make use of the encounter rate of nodes and context information such as time to live, number of available replicas and maximum number of forwarded bundle replicas to derive a bundle\u27s priority. Simulation results, over a service quality metric comprising of delivery, delay and overhead, show that the proposed policy achieves up to 80 % improvement when nodes have an infinite buffer and up to 35 % when nodes have a finite buffer over six popular queuing policies: Drop Oldest (DO), Last Input First Output (LIFO), First Input First Output (FIFO), Most FOrwarded first (MOFO), LEast PRobable first (LEPR) and drop bundles with the greatest hop-count (HOP-COUNT)

    Offloading Content with Self-organizing Mobile Fogs

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    Mobile users in an urban environment access content on the internet from different locations. It is challenging for the current service providers to cope with the increasing content demand from a large number of collocated mobile users. In-network caching to offload content at nodes closer to users alleviate the issue, though efficient cache management is required to find out who should cache what, when and where in an urban environment, given nodes limited computing, communication and caching resources. To address this, we first define a novel relation between content popularity and availability in the network and investigate a node's eligibility to cache content based on its urban reachability. We then allow nodes to self-organize into mobile fogs to increase the distributed cache and maximize content availability in a cost-effective manner. However, to cater rational nodes, we propose a coalition game for the nodes to offer a maximum "virtual cache" assuming a monetary reward is paid to them by the service/content provider. Nodes are allowed to merge into different spatio-temporal coalitions in order to increase the distributed cache size at the network edge. Results obtained through simulations using realistic urban mobility trace validate the performance of our caching system showing a ratio of 60-85% of cache hits compared to the 30-40% obtained by the existing schemes and 10% in case of no coalition

    Optimal Caching Policy of Stochastic Updating Information in Delay Tolerant Networks

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    To increase the speed of information retrieval, one message may have multiple replicas in Delay Tolerant Networks (DTN). In this paper, we adopt a discrete time model and focus on the caching policy of stochastic updating information. In particular, the source creates new version in every time slot with certain probability. New version is usually more useful than the older one. We use a utility function to denote the availability of different versions. To constrain the number of replicas, we propose a probabilistic management policy and nodes to discard information with certain probability determined by the version of the information. Our objective is to find the best value of the probability to maximize the total utility value. Because new version is created with certain probability, nodes other than the source may not know whether the information stored in them is the latest version. Therefore, they can make decisions only according to the local state and decisions based on the local state can be seen as local-policy. We also explore the global-policy, that is, nodes understand the real state. We prove that the optimal policies in both cases conform to the threshold form. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical model. Surprisingly, numerical results show that local-policy is better than the global-policy in some cases
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