3,350 research outputs found

    Optimal Content Downloading in Vehicular Networks

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    We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modelling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying

    Timely Data Delivery in a Realistic Bus Network

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    Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I

    Efficient Data Collection in Multimedia Vehicular Sensing Platforms

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    Vehicles provide an ideal platform for urban sensing applications, as they can be equipped with all kinds of sensing devices that can continuously monitor the environment around the travelling vehicle. In this work we are particularly concerned with the use of vehicles as building blocks of a multimedia mobile sensor system able to capture camera snapshots of the streets to support traffic monitoring and urban surveillance tasks. However, cameras are high data-rate sensors while wireless infrastructures used for vehicular communications may face performance constraints. Thus, data redundancy mitigation is of paramount importance in such systems. To address this issue in this paper we exploit sub-modular optimisation techniques to design efficient and robust data collection schemes for multimedia vehicular sensor networks. We also explore an alternative approach for data collection that operates on longer time scales and relies only on localised decisions rather than centralised computations. We use network simulations with realistic vehicular mobility patterns to verify the performance gains of our proposed schemes compared to a baseline solution that ignores data redundancy. Simulation results show that our data collection techniques can ensure a more accurate coverage of the road network while significantly reducing the amount of transferred data

    Reinforcement Learning based Gateway Selection in VANETs

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    In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways\u27 bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time

    A GRASP-based heuristic for allocating the roadside infrastructure maximizing the number of distinct vehicles experiencing contact opportunities

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    In this work the allocation of Roadside Units (RSUs) in a V2I network is modeled as a Maximum Coverage Problem. The main objective is to maximize the number of distinct vehicles contacting the infrastructure. Two different approaches are presented to solve the problem. The first one is an ILP model that can found optimal solutions or give sharp upper and lower bounds for the problem. The second one is a GRASP-based heuristic that can found close-to-optimal solutions. The GRASP-based heuristic is compared with a previous work achieving better results. Furthermore, a new metric to measure the efficiency of a Deployment strategy is presented

    Persistent Localized Broadcasting in VANETs

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    We present a communication protocol, called LINGER, for persistent dissemination of delay-tolerant information to vehicular users, within a geographical area of interest. The goal of LINGER is to dispatch and confine information in localized areas of a mobile network with minimal protocol overhead and without requiring knowledge of the vehicles' routes or destinations. LINGER does not require roadside infrastructure support: it selects mobile nodes in a distributed, cooperative way and lets them act as "information bearers", providing uninterrupted information availability within a desired region. We analyze the performance of our dissemination mechanism through extensive simulations, in complex vehicular scenarios with realistic node mobility. The results demonstrate that LINGER represents a viable, appealing alternative to infrastructure-based solutions, as it can successfully drive the information toward a region of interest from a far away source and keep it local with negligible overhead. We show the effectiveness of such an approach in the support of localized broadcasting, in terms of both percentage of informed vehicles and information delivery delay, and we compare its performance to that of a dedicated, state-of-the-art protoco

    Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

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    Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era
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