BLOR: An efficient bandwidth and latency sensitive overlay routing approach for flash data dissemination

Ferries play an important role in data dissemination in delay tolerant networks (DTN). The DTN routing hierarchical topology (DRHT) includes three fundamental concepts: ferries messages, ferries routes and clusters. We use ferries in DRHT to improve network performance. In the DRHT, the intra-cluster routing is managed by the cluster head, while the inter-cluster routing is managed by the ferries messages. In this work, we analyse the behavior of ferries’ problem of data dissemination in the DRHT. More specifically, in order to analyse the inter-cluster communication in the DRHT, we formulate game stochastic which models the behavior of the ferries.

We consider the problem of data dissemination in vehicular networks. Our main goal is to compare the application-level performance of fully distributed and centralized data dissemination approaches in the context of traffic advisory systems. Vehicular networks are emerging as a new distributed system environment with myriad promising applications. Wirelessly-connected, GPS-equipped vehicles can be used, for instance, as probes for traffic advisory or pavement condition information services with significant improvements in cost, coverage and accuracy. There is an ongoing discussion on the pros and cons of alternative approaches to data distribution for these applications. Proposed centralized, or infrastructure-based, models rely on road-side equipment to upload information to a central location for later use. Distributed approaches take advantage of the direct exchanges between participating vehicles to achieve higher scalability at the potential cost of data consistency. While distributed solutions can significantly reduce infrastructures' deployment and maintenance costs, it is unclear what the impact of "imprecise" information is to an application or what level of adoption is needed for this model to be effective. This paper investigates the inherent trade-offs in the adoption of distributed or centralized approaches to a traffic advisory service, a commonly proposed application. We based our analysis on a measurements study of signal propagation in urban settings and an extensive simulation-based experimentation in the Chicago road network.

It is known that Internet routing policies for both intra- and inter-domain routing can naturally give rise to triangle inequality violations (TIVs) with respect to quality of service (QoS) network metrics such as latencies between nodes. This motivates the exploitation of such TIVs phenomenon in network metrics to design TlV-aware routing overlay architecture which is capable of choosing quality overlay routing paths to improve end-to-end QoS without changing the underlying network architecture. Our idea is to find quality overlay routes between node pairs based on TIV optimization in terms of the latency and packet loss ratio, and that can offer near optimal routing quality in cost-effective and scalable manner. Our intuition to do this is to choose these overlay routes from a small set of transit nodes. We propose to assign nodes with transit selection frequency scores that are computed based on previous node usage for transit, and consolidate a small set of highly ranked transit nodes. For every node pair, we choose the best transit node in this small set for overlay routing, based on TIV optimization in latency and packet loss ratio. We analyze the quality of our TlV-aware routing overlay algorithm analytically and using real Internet measurements on latency and packet loss ratio. Our results show good quality performance in improving end-to-end QoS routing.

Among the challenges posed by the problem of data dissemination in wireless sensor networks one that has recently received considerable attention concerns the minimization of the node energy consumption for increasing the overall network lifetime. The sensor devices are battery powered thus energy is the most precious resource of a wireless sensor network since periodically replacing the battery of the nodes in large scale deployments is infeasible. Disseminate the collected data peer to peer to a static control point consumes significant amounts of energy especially for the sensor nodes around to the static sink. In this paper, we present new sink mobility-based data dissemination protocol to minimize energy consumption. We define new sink mobility scheme in which sensor sink periodically moves towards any destination based on the data dissemination frequency calculated during the last period. The simulation result shows that the proposal protocol permits to reduce the energy consumption and prolong the network life.Keywordswireless sensor networksdata disseminationenergy efficiencymobile sink

Code verification/validation with respect to experimental data banks

Multi-protocol label switching (MPLS) technology is useful for IP Virtual Private Networks (IP-VPNs), guaranteeing bandwidth in IP (Internet Procotol) networks, and carrying out traffic engineering with explicit routing. The advantage of MPLS is its high capability to achieve of reliable networks when used with Fast Rerouting. However, Fast Rerouting requires a lot of network resources. This is because, for the rapid recovery of end-to-end communications after dtectoin of failures, secondary LSPs must already have been reserved as detours in case there are failures on primary node-of-node links. The sharing of bandwith among secondary LSPs is thus significant as a way of reducing the usage of network resources when Fast Rerouting is applied.In this paper, we propose a new routing algorithm in which bandwidth is shared among the secondary LSPs for multiple primary LSPs. This algorithm produces efficient network-level LSP designs. Three approaches to the dynamical changing of Open Shortest Path First (OSPF) link-cost metrics are applied in the algorithm. Each approach improves efficiency in the sharing of LSPs. The approaches are (1) the broader distribution of primary LSPs to reduce the need for detours in cases of single failures, (2) the concentration of secondary LSPs on links to increase the possibilities for bandwidth sharing, and (3) the distribution of secondary LSPs that cater to a certain failure, thus increasing the numbers of detouring LSPs which are independent of each other on the respective links. The scheme provides a slight improvement over the results of the conventional Dijkstra-algorithm calculation which is used in conventional OSPF. The proposed algorithms are applied with various network models that have been proposed in IETF Internet drafts, e.g., Haskin, to egress, and Next-Next-Hop; the results are evaluated on the basis of parameters that indicate the consumption of network resources, including the numbers of labels per node, usage of the network's bandwidth capacity, and round-trip times for the secondary LSPs. Application of algorithm (1) led to a 10% lower total LSP cost of the network than the result for the conventional form of bandwidth sharing, i.e., for sharing among the secondary LSPs of a single primary LSP.

Edge-cloud collaborative inference is a widely adopted approach in edge intelligence, especially for latency-sensitive tasks such as drone inspection, augmented reality, and disaster relief. To adapt to varying network bandwidth and limited computational capacity, model splitting methods divide Deep Neural Networks (DNNs) at specific split points into edge and cloud segments. However, current methods suffer from high switch latency and memory overhead, and are incompatible with Feature Pyramid Network (FPN)-based multi-branch models. To address the above issues, we propose a dynamic model splitting framework (DMSF) for edge-cloud collaborative inference. We design switchable compress–recover modules after each layer, enabling a single model to support all split points and significantly reduce memory overhead. The optimal split point is selected based on bandwidth and edge computational capacity, and the corresponding compress–recover path is activated for fast switching. For FPN-based multi-branch models, we remove the multi-scale branches from the backbone to the FPN and present hierarchical compensation modules in DMSF, making the model more suitable for splitting while maintaining perception accuracy. Experimental results show that DMSF reduces memory overhead by 75%, achieves millisecond-level switching, and outperforms the existing method with 32.0% lower latency and 12.2% higher perception accuracy.

The technique of distributed storage coding has been widely used in wireless sensor networks for increasing the robustness of data storage and efficiency of data retrieval. Existing works mainly focus on scenarios in which each storage node stores a linear combination of a subset of K data packets generated by different source nodes. By solving the linear equations, a collector can recover all the K data packets with high probability. This paper explores the problem of flexible and efficient data dissemination and retrieval in wireless sensor networks. Our scheme exploits the broadcast nature of wireless transmission and improves the traditional random walk algorithm for efficient data dissemination. Furthermore, through the use of Fountain codes in data encoding, it enables a mobile collector to recover up-to-date data generated by any subset of source nodes. Specifically, by querying any t(1 + ε) storage nodes at the t-th time slot, the target data can be retrieved without having to decode all the source packets. Simulation results validate our analysis and show that the proposed schemes are flexible and efficient.

In this paper, we examine the problem of large-volume data dissemination via overlay networks. A natural way to maximize the throughput of an overlay multicast session is to split the traffic and feed them into multiple trees. While in single-tree solutions, bandwidth of leaf nodes may remain largely under-utilized, multi-tree solutions increase the chances for a node to contribute its bandwidth by being a relaying node in at least one of the trees. We study the following problems: (1) What is the maximum capacity multi-tree solutions can exploit from overlay networks? (2) When multiple sessions compete within the same network, what is the relationship of two contradictory goals: achieving fairness and maximizing overall throughput? (3) What is the impact of IP routing in achieving at constraining the optimal performance of overlay multicast.We extend the multicommodity flow model to the case of overlay data dissemination, where each commodity is associated with an overlay session, rather than the traditional source-destination pair. We first prove that the problem is solvable in polynomial time, then propose an e-approximation algorithm, assuming that each commodity can be split in arbitrary ways. The solution to this problem establishes the theoretical upper bound of overall throughput that any multi-tree solution could reach. We then study the same problem with the restriction that each commodity can only be split and fed into a limited number of trees. A randomized rounding algorithm and an online tree-construction algorithm are presented. All these algorithms are evaluated by extensive simulations.

Fog and Mist computing exploits computational resources of IoT devices located at the edge of the network. This new infrastructure brings benefits related to latency and Internet bandwidth utilization when compared with Cloud computing. However, this scenario is quite challenging mainly due to the considerable heterogeneity of the devices and to the dynamicity of the network topology what adds new issues related to security, privacy, data availability, and service availability, among others. Despite the theme of data dissemination and data storage had been relatively explored in fields of Sensor Wireless Network (WSN) and Vehicular Ad Hoc Networks (VANETs), we found few studies in the Fog computing research area. Then, this work focuses on the problem of data availability and dissemination in this environment by proposing a distributed algorithm using ideas from evolutionary computation and epidemic models. Results obtained through simulation suggest that the algorithm presents proper results in maintaining data availability in the Fog and Mist Computing environments, even under severe conditions.

A comparison of epidemic algorithms in wireless sensor networks

In this work we consider the important problem of energy balanced data dissemination in wireless sensor networks (WSN), where data are generated by the sensors and must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering the data to a neighboring sensor. W e decompose the transmission range of sensors into concentric circular bands rings based on a minimum transmission distance between any pair of sensors. We also propose a data dissemination protocol that exploits the above-mentioned decomposition to meet the specific requirements of a sensing application in terms of trade-off between energy and source-to-sink delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and a sink, as proxy forwarders in data dissemination from sources to a sink, helps simultaneously balance energy consumption and delay. Furthermore, this protocol is based on stochastic estimation methods and is adaptive to environmental changes

In this paper we study the problem of enabling uninterrupted delivery of messages between endpoints, subject to spatially correlated failures in addition to independent failures. We developed a failure model-independent algorithm for computing routing paths based on failure correlations using both a-priory failure statistics together with available real-time monitoring information. The algorithm provides the most cost-efficient message routes that are potentially comprised of multiple simultaneous paths. We also designed and implemented an Internet-based overlay routing service that allows applications to construct and maintain highly resilient end-to-end paths. We have deployed our system over a set of geographically distributed Planetlab nodes. Our experimental results illustrate the feasibility and performance of our approach.

In vehicular ad-hoc networks, high mobility tends to break established paths and cause bandwidth waste because of path reconstruction. To improve the situation, this study presents a new position-based protocol. The new protocol uses control packets to collect approaching-the-junction information and, based on the information, lets moving vehicles adapt their routing paths dynamically when arriving at a junction. Compared with existing protocols which pre-store massive routing paths in a vehicle, the protocol can accurately relay packets to the destination by significantly reduced control packets. Transmission efficiency is therefore enhanced. Performance evaluation exhibits that the proposed protocol is suitable for practice in highly topology-changing vehicle-to-vehicle environments because it can accurately select targets for packet relay at less routing and storage cost.

In real networks, traffic flows are different in amount as well as their priorities. However, the latter priority has rarely been examined in routing strategy studies. In this paper, a novel routing algorithm, which is based on the efficient path routing strategy (EP), is proposed to overcome network congestion problem caused by large amount of traffic flows with different priorities. In this scheme, traffic flows with different priorities are transmitted through different routing paths, which are based on EP with different parameters. Simulation results show that the traffic capacity for flows with different priorities can be enhanced by 12% with this method, compared with EP. In addition, the new method contributes to more balanced network traffic load distribution and reduces average transmission jump and delay of packets.