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

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
College of Computer Science, Chongqing University, Chongqing 400040, China
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3547;
Received: 30 June 2019 / Revised: 9 August 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
(This article belongs to the Special Issue Sensors, Robots, Internet of Things, and Smart Factories)
PDF [6375 KB, uploaded 15 August 2019]


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, we 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, we 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, we 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, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm. View Full-Text
Keywords: traffic sensing; bandwidth-aware; vehicular networks; mobile edge computing; traffic state estimation traffic sensing; bandwidth-aware; vehicular networks; mobile edge computing; traffic state estimation

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Ye, K.; Dai, P.; Wu, X.; Ding, Y.; Xing, H.; Yu, Z. Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing. Sensors 2019, 19, 3547.

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