8,410 research outputs found
Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication
Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities
Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks
Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well
Positioning Accuracy Improvement via Distributed Location Estimate in Cooperative Vehicular Networks
The development of cooperative vehicle safety (CVS) applications, such as
collision warnings, turning assistants, and speed advisories, etc., has
received great attention in the past few years. Accurate vehicular localization
is essential to enable these applications. In this study, motivated by the
proliferation of the Global Positioning System (GPS) devices, and the
increasing sophistication of wireless communication technologies in vehicular
networks, we propose a distributed location estimate algorithm to improve the
positioning accuracy via cooperative inter-vehicle distance measurement. In
particular, we compute the inter-vehicle distance based on raw GPS pseudorange
measurements, instead of depending on traditional radio-based ranging
techniques, which usually either suffer from high hardware cost or have
inadequate positioning accuracy. In addition, we improve the estimation of the
vehicles' locations only based on the inaccurate GPS fixes, without using any
anchors with known exact locations. The algorithm is decentralized, which
enhances its practicability in highly dynamic vehicular networks. We have
developed a simulation model to evaluate the performance of the proposed
algorithm, and the results demonstrate that the algorithm can significantly
improve the positioning accuracy.Comment: To appear in Proc. of the 15th International IEEE Conference on
Intelligent Transportation Systems (IEEE ITSC'12
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