Next Article in Journal
putEMG—A Surface Electromyography Hand Gesture Recognition Dataset
Next Article in Special Issue
Use of Thermistor Temperature Sensors for Cyber-Physical System Security
Previous Article in Journal
Sensorless Control of the Permanent Magnet Synchronous Motor
Previous Article in Special Issue
Deep CNN for Indoor Localization in IoT-Sensor Systems
Open AccessArticle

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

1
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2
College of Computer Science, Chongqing University, Chongqing 400040, China
3
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3547; https://doi.org/10.3390/s19163547
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop