Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Guo, Shuo"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks
    (2007-07-02) Jeong, Jaehoon; Guo, Shuo; He, Tian; DuHung-Chang, David
    In road networks, sensor nodes are deployed sparsely (hundreds of meters apart) to save costs. This makes the existing localization solutions based on the ranging ineffective. To address this issue, this paper introduces an autonomous passive localization scheme, called APL. Our work is inspired by the fact that vehicles move along routes with a known map. Using vehicle-detection timestamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of road map, in order to identify where sensors are located in roadways. We evaluate our design outdoor in local roadways and show that our distance estimate method works well despite of traffic noises. In addition, we show that our localization scheme is effective in a road network with eighteen intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.3[sec] and the vehicle speed deviation of 10[km/h].
  • Thumbnail Image
    Item
    TBD: Trajectory-Based Data Forwarding for Light-Traffic Vehicular Networks
    (2008-11-24) Jeong, Jaehoon; Guo, Shuo; Gu, Yu; He, Tian; Hung-Chang Du, David
    This paper proposes a Trajectory-Based Data Forwarding (TBD) scheme, tailored for the data forwarding in light-traffic vehicular ad-hoc networks. We consider the scenarios in which Internet access points are sparsely deployed to receive the roadside reports of time-critical information such as driving accident or hazard. Since the Internet access points have limited communication coverage, a vehicular ad-hoc network is needed to forward data packets to the access points. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds) in such a network. These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to investigate how to effectively utilize vehicles' trajectory information in a privacy-preserving manner. In our design, the trajectory information is combined with the traffic statistics to improve the performance of data forwarding in road networks. Through theoretical analysis and extensive simulation, it is shown that our design outperforms the existing scheme in terms of both the data delivery delay and packet delivery ratio, specially under light-traffic situations.
  • Thumbnail Image
    Item
    Travel Prediction-based Data Forwarding for Sparse Vehicular Networks
    (2011-07-28) Xu, Fulong; Guo, Shuo; Jeong, Jaehoon; Gu, Yu; Cao, Qing; Liu, Ming; He, Tian
    Vehicular Ad Hoc Networks (VANETs) represent promising technologies of cyber-physical systems for improving driving safety and communication mobility. Due to the highly dynamic driving patterns of vehicles, effective packet forwarding, especially for time sensitive data, has been a challenging research problem. Previous works forward data packets mostly utilizing statistical information about road network traffic, which becomes much less accurate when vehicles travel in sparse network as highly dynamic traffic introduces large variance for these statistics.With the popularity of on-board GPS navigation systems, individual vehicle trajectories become available and can be utilized for removing the uncertainty in road traffic statistics and improve the performance of the data forwarding in VANETs. In this paper, we propose Travel Prediction based Data-forwarding (TPD), in which vehicles share their trajectory information to achieve the low delay and high reliability of data delivery in multi-hop carry-and-forward environments. The driven idea is to construct a vehicle encounter graph based on pair-wise encounter probabilities, derived from shared trajectory information. With the encounter graph available, TPD optimizes delivery delay under a specific delivery ratio threshold, and the data forwarding rule is that a vehicle carrying packets always selects the next packet-carrier that can provide the best forwarding performance within the communication range. Through extensive simulations we demonstrate that TPD significantly outperforms existing schemes of TBD and VADD with more than 5% more packets delivery while reducing more than 40% delivery delay.
  • Thumbnail Image
    Item
    TSF: Trajectory-based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery in Vehicular Networks
    (2010-03-12) Jeong, Jaehoon; Guo, Shuo; Gu, Yu; He, Tian; Hung-Chang Du, David
    We consider the scenarios where Internet access points are sparsely deployed in road networks to provide individual vehicles with customized road condition information for the driving safety, such as holes and bumps along their trajectories. Due to the limited communication coverage, vehicular ad-hoc networks are used to support the multi-hop data forwarding. State-of-the-art schemes have demonstrated their effectiveness in the data forwarding from vehicles to stationary points (e.g., Internet access points). However, they are not designed for the reverse data forwarding from Internet access points to vehicles, a much more challenging problem because of the mobility of the packet destination. This paper proposes a data forwarding scheme called Trajectory-based Statistical Forwarding (TSF), tailored for the infrastructure-to-vehicle data delivery in vehicular networks. TSF forwards packets over multi-hop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packet's delivery delay distribution and the destination vehicle's travel delay distribution. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the destination vehicle's trajectory to compute such an optimal target point. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.