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

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3547 ◽  
Author(s):  
Kong Ye ◽  
Penglin Dai ◽  
Xiao Wu ◽  
Yan Ding ◽  
Huanlai Xing ◽  
...  
Keyword(s):  
Data Collection ◽  
Vehicular Networks ◽  
Estimation Error ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Traffic Data ◽  
Norm Minimization ◽  
Traffic Data Collection ◽  
Dynamic Mobility ◽  
Traffic Systems

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.

Use of Reduction-Effectiveness Ratios to Evaluate Reduced Traffic Data Collection Plans

2013 ◽  
Vol 2339 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Chieh (Ross) Wang ◽  
Yichang (James) Tsai
Keyword(s):  
Data Collection ◽  
Estimation Error ◽  
Percentage Error ◽  
Traffic Data ◽  
Traffic Data Collection ◽  
The Impact ◽  
Departments Of Transportation ◽  
Reduced Data ◽  
Reduction Effectiveness ◽  
Aadt Estimation

Because of budget shortfalls in recent years, state departments of transportation (DOTs) must adjust their traffic data collection plans by reducing data collection locations or extending data collection cycles; however, few studies have been performed to evaluate the cost-effectiveness of various efforts to reduce data collection. This study developed a quantitative method for evaluating the impact of various reduced plans for traffic data collection on the overall accuracy of the annual average daily traffic (AADT) estimation. The mean absolute percentage error is calculated to compare the accuracy of 10 reduced data collection plans with a base plan. In addition, a reduction-effectiveness ratio (i.e., the percentage of reduced data collection cost to the percentage of increased AADT estimation error) is proposed. Results from this study show that the current practice, which randomly selects data collection sites on the basis of different cycles, performs well in maintaining AADT estimation accuracy but may not be the most cost-effective approach. Results also show that certain types of sites, such as rural sites, lower-AADT sites, and sites with higher AADT variation, tend to produce larger errors if they are not counted. The results imply that the proposed method both provides a quantitative means with which to evaluate plans for reduced data collection and suggests ways to enhance current data collection and traffic estimation practices. The method also enriches the information provided to state departments of transportation for effective and informed decision making with limited resources.

HOTSPOT: A UAV-Assisted Dynamic Mobility-Aware Offloading for Mobile Edge Computing in 3D Space

2021 ◽  
pp. 1-1
Author(s):  
Zhuofan Liao ◽  
Yinbao Ma ◽  
Jiawei Huang ◽  
Jianxin Wang ◽  
Jin Wang
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
3D Space ◽  
Dynamic Mobility

scholarly journals Vision-Based Traffic Data Collection Sensor for Automotive Applications

Sensors ◽  
2010 ◽  
Vol 10 (1) ◽  
pp. 860-875 ◽  
Author(s):  
David F. Llorca ◽  
Sergio Sánchez ◽  
Manuel Ocaña ◽  
Miguel. A. Sotelo
Keyword(s):  
Data Collection ◽  
Traffic Data ◽  
Automotive Applications ◽  
Traffic Data Collection

scholarly journals An Efficient Forwarding Capability Evaluation Method for Opportunistic Offloading in Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Qian Wang ◽  
Zhipeng Gao ◽  
Kun Niu ◽  
Yang Yang ◽  
Xuesong Qiu
Keyword(s):  
Evaluation Method ◽  
Edge Computing ◽  
Second Step ◽  
Delivery Ratio ◽  
Mobile Edge Computing ◽  
Traffic Data ◽  
Data Forwarding ◽  
Data Redundancy ◽  
Computation Process ◽  
Capability Evaluation

Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device’s forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient-cluster-based Capability Evaluation Method (TCEM) to evaluate a device’s data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device’s possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device’s probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device’s forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM-based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.

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