High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication

Liu, Jianqi; Wan, Jiafu; Jia, Dongyao; Zeng, Bi; Li, Di; Hsu, Ching‐Hsien; Chen, Haibo

doi:10.1109/mcom.2017.1600371cm

Cited by 126 publications

(100 citation statements)

References 14 publications

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“…Many works have proposed congestion detection systems and real-time vehicle path planning with the support of VANETs. They also use a combination of different technologies such as induction loops, central server, and road sensors to achieve their goals [4,5,6,7,16,17,18]. The main purpose of these systems is redistributing the flow of vehicle traffic that is going to the congested area, using real-time traffic data collected, to the non-congested area.…”

Section: Related Workmentioning

confidence: 99%

“…Both the Traffic Model and the Road Model are maintained at a central server and updated with information on the nearby roads around the RSU. Both works [4,16] require RSUs, but it is not clear how they are distributed in the scenario. In addition to that, a broadcast suppression mechanism was not applied during the information dissemination process.…”

Section: Related Workmentioning

confidence: 99%

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Towards a distributed and infrastructure-less vehicular traffic management system

Akabane

Immich

Bittencourt

et al. 2020

Computer Communications

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In the past few years, several systems have been proposed to deal with issues related to the vehicular traffic management. Usually, their solutions include the integration of computational technologies such as vehicular networks, central servers, and roadside units. Most systems use a hybrid approach, which means they still need a central entity (central server or roadside unit) and Internet connection to find out an en-route event as well as alternative routes for vehicles. It is easy to understand the need for a central entity because selecting the most appropriate vehicle to perform aforementioned procedures is a difficult task. This is especially true in a highly dynamic network. In addition to that, as far as we know, there are very few systems that apply the altruistic approach (not selfish behavior) to routing decisions. Because of that, the issue addressed in this work is how to perform the vehicular traffic management, when an en-route event is detected, in a distributed, scalable, and cost-effective fashion. To deal with these issues, we proposed a distributed vehicle traffic management system, named as dEASY (distributed vEhicle trAffic management SYstem).The dEASY system was designed and implemented on a three-layer architecture, namely environment sensing and vehicle ranking, knowledge generation and distribution, and knowledge consumption. Each layer of the dEASY architecture is responsible for dealing with the main issues that were not addressed in related works or could be improved. The essential task of each the layer is: the first layer deals with the task of selecting the most appropriate vehicle, the second one addresses the knowledge generation and distribution, and in the last layer is applied an altruistic approach to choose an alternative route. Simulation results have shown that, compared with other systems from the literature, our proposed system has lower network overhead due to applied vehicle selection and broadcast suppression mechanisms. In average, dEASY also outperformed all other competitors in what regards to the travel time and time lost metrics. Through the analysis of results, it is possible to conclude that our infrastructure-less system is scalable and cost-effective.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards a distributed and infrastructure-less vehicular traffic management system

Akabane

Immich

Bittencourt

et al. 2020

Computer Communications

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“…32 However, a solution at the end-user side can be proposed to cater identified issues in the use of LTE that may also support the changing network dynamic of emerging 5G infrastructure. Issues such as inter-cell inference are also there.…”

Section: Paving Way Towards 5gmentioning

confidence: 99%

“…The problems identified in our work are the potential issues expected to be addressed through full penetration of 5G technology. 32 However, a solution at the end-user side can be proposed to cater identified issues in the use of LTE that may also support the changing network dynamic of emerging 5G infrastructure.…”

Section: Paving Way Towards 5gmentioning

confidence: 99%

LTE efficiency when used in traffic information systems: A stable interest aware clustering

Ahmad

Noor

Z’aba

2018

Int J Communication

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Long-term evolution (LTE) technology is critical for the envisioned usage scenarios in the Internet of Vehicles. An important usage scenario is traffic information systems (TIS) for vehicular ad hoc networks (VANETs) that depend on LTE for the delivery of non-safety information to vehicles. TISs are centralized data warehouses that collect and process traffic information and subsequently allow vehicles to receive such information before and during a road trip. Considering the extensive deployment of VANETs, the LTE demand for TIS is expected to increase. Therefore, we explore and quantify the inefficiency of LTE when used in TIS. By envisioning basic test case scenarios, we establish the trends of data usage while commuting and providing insights into how LTE usage in TIS may lead to the inefficient use of the available wireless spectrum during road trips. To reduce identified inefficiency in LTE usage and cluster instability due to the high speed of the vehicles, we proposed a novel stable interest-aware clustering (SIAC) mechanism. SIAC consider VANETs mobility constraint, LTE link quality, and exploit interest of the vehicles, in cluster formation phase. Resulting in the reduction in the data demand of the vehicles and frequency of link failures among the vehicles and provides transmission path stability. SIAC performs well as compared with the existing approaches shows more cluster stability and reduces the LTE usage for driving assistance and route planning applications.KEYWORDS data and spectrum usage, internet of vehicles, LTE, stability, traffic information system, vehicular clustering

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“…Wg (1,1) (k) = 0, Wg (1,2) (k) = 1, Wg (1,3) (k) = 0, Wg (1,4) (k) = 0, Wg (1,5) (k) = 0, Wg (1,6) (k) = 1, Wg (1,7) (k) = 1, Wg (1,8) (k) = 0, Wg (1,9) (k) = 0, Wg (1,10) (k) = 0.…”

Section: Simulationmentioning

confidence: 99%

A high‐performance maintenance strategy for stochastic selective maintenance

Zhao

Liu

Zhao

et al. 2018

Concurrency and Computation

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Selective maintenance is often applied in many industrial environments where maintenance is performed between sequence missions. When the mission time is stochastic and there are multiple maintenance workers with different capacities, the system reliability of the next work mission can be maximized by using a stochastic model under the constraint of the limit maintenance time.The optimal maintenance strategy is obtained with an optimization algorithm. A simulation was performed to verify the validity and feasibility of the proposed model. KEYWORDS group maintenance, maintenance time, mission time, stochastic model, stochastic selective maintenance INTRODUCTIONAfter the wisdom of China was presented, cyber-physical systems, 1 cloud computing, wireless networks, 2 big data, Vehicular Network, 3,4 Industrial Intelligence Management, etc, have developed rapidly. In the proper and safe operation of industrial and military equipment, transportation systems maintenance is very important. 5 Maintenance management can establish reasonable maintenance policies for complex systems to meet new design requirements with using reliability and maintenance engineering analyses, such as equipment reliability and maintainability, in order to realize timely and effective maintenance to ensure the performance of the system with the lowest resource consumption. 6In many industrial fields (eg, manufacturing systems, military equipment systems, and power generation systems), maintenance is performed between two continuous missions. Each component or equipment can be repaired in different ways, such as no maintenance, minimal maintenance, preventive maintenance, and replacement. However, all maintenance tasks are not fully performed because of limited resources, such as maintenance time, cost, tools, and maintenance workers, 7 so part of the maintenance tasks are executed to maximize the system reliability of the next work mission. This is called selective maintenance. The workflow of selective maintenance is shown in Figure 1.Unlike the deterministic selective maintenance, a feasible deterministic maintenance set cannot be obtained in the stochastic selective maintenance because of the stochastic maintenance time or work time. For different systems, many different models are defined. Cassady et al 7 defined selective maintenance and a mathematical programming model for a two-state system. Cassady et al 8 developed a model that maximizes the system reliability with maintenance time Constraints for a series-parallel system with two-state components. In the model, the lifespan of a component follows a Weibull distribution, so that this reliability function is influenced by the age of components at the beginning of the mission and the mission time. Zhu et al 9 established a time-constrained Concurrency Computat Pract Exper. 2019;31:e4840. wileyonlinelibrary.com/journal/cpe

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High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication

Cited by 126 publications

References 14 publications

Towards a distributed and infrastructure-less vehicular traffic management system

Towards a distributed and infrastructure-less vehicular traffic management system

LTE efficiency when used in traffic information systems: A stable interest aware clustering

A high‐performance maintenance strategy for stochastic selective maintenance

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