Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing

Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, the authors formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, the authors first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, the authors propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l 2 -norm minimization problem. Last but not the least, the authors implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 3547
  • Serial:
  • Publication flags:

    Open Access (libre)

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Filing Info

  • Accession Number: 01716525
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 26 2019 2:20PM