Fuzzy Traffic Control with Vehicle-to-Everything Communication

Salman, Muntaser A.; Özdemir, Suat; Çelebi, Fatih V.

doi:10.3390/s18020368

Cited by 16 publications

(10 citation statements)

References 10 publications

(17 reference statements)

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“…At the second stage, a green-time adjustor was proposed to determine the green time for the selected phase. The work in [35] proposed a fuzzy traffic control approach by utilizing V2X communication. Another example of efficiency-based applications can be found in [36] for cooperative driving which proposed modeling trajectories in advance for vehicles in the vicinity of traffic signals to reduce stopping frequency and travel time and improve the throughput of the road according to traffic conditions and the situation regarding adjacent vehicles.…”

Section: A Its-based Iov Applicationsmentioning

confidence: 99%

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

et al. 2019

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The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario. INDEX TERMS Internet of Vehicles, IoV, layer architecture, smart city, applications, big data.

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Section: A Its-based Iov Applicationsmentioning

confidence: 99%

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

et al. 2019

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“…Besides health-care systems, the IoT can also assist in the integration of communications, control and information processing across various transportation systems, which helps improve the efficiency and convenience of modern day transportation [5]. In particular, the IoT is used for smart road and traffic management as a means of reducing traffic congestions or redirecting traffic in response to an accident or other types of hazard by building sensors into highways and surface streets [6]. Moreover, the IoT has driven the autonomous vehicle revolution.…”

Section: Introductionmentioning

confidence: 99%

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Pham

Nguyen

et al. 2019

Symmetry

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The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

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“…Therefore, the fuzzy model can be a viable alternative [27]. The fuzzy system has the characteristics of simple rules and wide applicability [28], and can analyze the inaccurate information and receive irrigation guidance from complex farming sites [29]. So it is easier for users to accept DSSs based on fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing

Shi

Han

Zhao

et al. 2019

Sensors

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Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.

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Fuzzy Traffic Control with Vehicle-to-Everything Communication

Cited by 16 publications

References 10 publications

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing

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