Corrigendum to “Adaptive scheduling for real-time and temporal information services in vehicular networks” [Transport. Res. Part C 71 (2016) 313–332]

2017 ◽  
Vol 75 ◽  
pp. 254
Author(s):  
Penglin Dai ◽  
Kai Liu ◽  
Liang Feng ◽  
Qingfeng Zhuge ◽  
Victor C.S. Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicular Networks ◽  
Information Services ◽  
Temporal Information ◽  
Adaptive Scheduling ◽  
Part C ◽  
C 71

Adaptive scheduling for real-time and temporal information services in vehicular networks

2016 ◽  
Vol 71 ◽  
pp. 313-332 ◽  
Author(s):  
Penglin Dai ◽  
Kai Liu ◽  
Liang Feng ◽  
Qingfeng Zhuge ◽  
Victor C.S. Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicular Networks ◽  
Information Services ◽  
Temporal Information ◽  
Adaptive Scheduling

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

A real-time multi-vehicle tracking framework in intelligent vehicular networks

2021 ◽  
Vol 18 (6) ◽  
pp. 89-99
Author(s):  
Huiyuan Fu ◽  
Jun Guan ◽  
Feng Jing ◽  
Chuanming Wang ◽  
Huadong Ma
Keyword(s):  
Real Time ◽  
Vehicular Networks ◽  
Vehicle Tracking
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