On achieving optimized capacity utilization in application overlay networks with multiple competing sessions

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

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.

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

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 address the problem of optimal routing in overlay networks. An overlay network is constructed by adding new overlay nodes on top of a legacy network. The overlay nodes are capable of implementing any dynamic routing policy, however, the legacy underlay has a fixed, single path routing scheme and uses a simple work-conserving forwarding policy. Moreover, the underlay routes are pre-determined and unknown to the overlay network. The overlay network can increase the achievable throughput of the underlay by using multiple routes, which consist of direct routes and indirect routes through other overlay nodes. We develop a throughput optimal dynamic routing algorithm for such overlay networks called the Optimal Overlay Routing Policy (OORP). OORP is derived using the classical dual subgradient descent method, and it can be implemented in a distributed manner. We show that the underlay queue-lengths can be used as a substitute for the dual variables. We also propose various schemes to gather the information about the underlay that is required by OORP and compare their performance via 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.

Code verification/validation with respect to experimental data banks

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

This paper investigates the issues of QoS multicast routing in wireless ad hoc networks. Due to limited bandwidth of wireless nodes, a QoS multicast call will be blocked if there does not exist a single multicast tree that has the requested bandwidth, even though there is enough bandwidth in the system to support the call. We propose a new QoS multicast routing scheme that uses multiple trees. The aggregate bandwidth of multiple trees can meet the bandwidth requirement and the delay from the source to the farthest destination node shall not exceed a pre-specified bound. Two strategies for constructing multiple trees are studied. The simulation results show that the new scheme significantly improves the request success rate and makes a better use of network resources.KeywordsDestination NodeNetwork LoadSuccess RatioBandwidth RequirementMulticast TreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

A comparison of epidemic algorithms in wireless sensor networks

SummaryThe problem of flash data dissemination refers to transmitting time‐critical data to a large group of distributed receivers in a timely manner, which widely exists in many mission‐critical applications and Web services. However, existing approaches for flash data dissemination fail to ensure the timely and efficient transmission, because of the unpredictability of the dissemination process. Overlay routing has been widely used as an efficient routing primitive for providing better end‐to‐end routing quality by detouring inefficient routing paths in the real networks. To improve the predictability of the flash data dissemination process, we propose a bandwidth and latency sensitive overlay routing approach named BLOR, by optimizing the overlay routing and avoiding inefficient paths in flash data dissemination. BLOR tries to select optimal routing paths in terms of network latency, bandwidth capacity, and available bandwidth in nature, which has never been studied before. Additionally, a location‐aware unstructured overlay topology construction algorithm, an unbiased top‐kdominance model, and an efficient semi‐distributed information management strategy are proposed to assist the routing optimization of BLOR. Extensive experiments have been conducted to verify the effectiveness and efficiency of the proposals with real‐world data sets. Copyright © 2014 John Wiley & Sons, Ltd.

In a content distribution network based on overlay networks, the peer's upload bandwidth is always constrained. To optimize the system performance, one problem is how to assign the upload capacity to the right neighbors for transferring data blocks. In a system using network coding technology, a strategy which is to select the right neighbors based upon the degrees of them is proposed. The selection probability of each neighbor is determined by its degree. After. this strategy is applied, the utilization of network is improved, and the distribution progress is shortened. From the simulation, we found that the performance of content distribution is obviously better than the baseline system on three metrics, including maximum finish time, average finish time and standard deviation.

SURF: A distributed channel selection strategy for data dissemination in multi-hop cognitive radio networks

Scoped broadcast disseminates a message to all the nodes within a designated physical/logical region in an overlay network. It can be a basic building block for applications such as information search, data broadcasting, and overlay structure diagnostic. In this paper, we study scoped broadcast in peer-to-peer (P2P) overlay networks based on distributed hash tables (DHT). Since P2P networks have very dynamic behavior due to peer joining and departure, it is interesting to know how many peers can be reached with one scoped broadcast. This depends mainly on the cost we would like to pay for maintaining the geometric structure of the DHT-based overlay. The maintenance cost is affected primarily by the failure detection and failure recovery mechanisms. We evaluated the effects of maintenance overhead on scoped broadcast via extensive simulations. The evaluation shows that it is important to exploit fresh nodes as the neighbors of a node. In addition, cooperative failure discovery and recovery can efficiently and effectively disseminate scoped broadcast messages.