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

Ang, Li-Minn; Seng, Kah Phooi; Ijemaru, Gerald K.; Zungeru, Adamu Murtala

doi:10.1109/access.2018.2887076

Cited by 151 publications

(69 citation statements)

References 101 publications

(113 reference statements)

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“…2020, 10, x FOR PEER REVIEW 4 of 18 solution. As service platforms for smart cities, the deployment of connected cars and edge devices has been studied in many works [33,34]. In this paper, a general framework is proposed based on IoT in a mixed traffic scenario.…”

Section: Architecture Of Mobile Service Computing For Traffic Static mentioning

confidence: 99%

“…Benefiting from the development of embedded devices, the computing capability of the connected traffic infrastructures' controller enhances greatly. Moreover, the cloud [32] and edge computing [33] can be used to serve the traffic states sensing. A framework of smart city with intelligent IoT, cloud, edge, and 5G is constructed to provide more efficiency and rationality [34].…”

Section: Introductionmentioning

confidence: 99%

“…The infrastructure-based mechanisms mentioned in the paper belong to an IoT-based solution. As service platforms for smart cities, the deployment of connected cars and edge devices has been studied in many works [33,34].…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

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Section: Architecture Of Mobile Service Computing For Traffic Static mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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“…By 2020, around 50 billion devices will be connected to the Internet for a better society using different technological systems. The concept of smart objects which provide seamless connectivity along with ensuring safety and a smart environment through increasing interaction and interoperability is called the Internet of Things (IoT) [1]. Vehicle users enjoy a better experience when amalgamating the IoT and Vehicular Ad hoc Network (VANET) architectures, and this emerging field is called the Internet of Vehicles (IoV) [2].…”

Section: Introductionmentioning

confidence: 99%

Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET

Gupta¹,

Manaswini²,

Saikrishna³

et al. 2020

Future Internet

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The amalgamation of Vehicular Ad hoc Network (VANET) with the Internet of Things (IoT) leads to the concept of the Internet of Vehicles (IoV). IoV forms a solid backbone for Intelligent Transportation Systems (ITS), which paves the way for technologies that better explain about traffic efficiency and their management applications. IoV architecture is seen as a big player in different areas such as the automobile industry, research organizations, smart cities and intelligent transportation for various commercial and scientific applications. However, as VANET is vulnerable to various types of security attacks, the IoV structure should ensure security and efficient performance for vehicular communications. To address these issues, in this article, an authentication-based protocol (A-MAC) for smart vehicular communication is proposed along with a novel framework towards an IoV architecture model. The scheme requires hash operations and uses cryptographic concepts to transfer messages between vehicles to maintain the required security. Performance evaluation helps analyzing its strength in withstanding various types of security attacks. Simulation results demonstrate that A-MAC outshines other protocols in terms of communication cost, execution time, storage cost, and overhead.

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“…Smart wearable electronic devices, such as smart bracelets and wristwatches, will certainly create another major change in smart devices as they act as entrance to the Internet of Things for human beings, just as smartphones became the entrance to the Internet . The wide application of smart wearable electronic devices effectively promotes the development of flexible devices, integrated electronic circuit, and other disciplines, improves human self‐monitoring and self‐management capabilities, and brings great convenience to our lives .…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Helical Triboelectric Nanogenerators for Energy Conversion of Motion

et al. 2020

Adv Materials Technologies

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lithium-ion batteries, such as low life, serious pollution, and low safety, have always plagued the development of intelligent wearable devices and intelligent devices. If the energy module of smart wearable devices explodes, the apparent harm is conceivable. [10][11][12] Therefore, the development of a safe and lightweight energy source is urgently necessary. Given the rapid development of the integrated circuits (IC) and low power sensors, the power requirement of smart wearable electronic devices has been reduced from mW to µW. [13][14][15] To solve this problem, it is feasible and effective to collect body movement energy and convert it into electric energy to supply power for smart wearable devices. [16,17] Presently, many kinds of equipment designed to collect body energy have been reported. [18][19][20][21] This study is divided into electromagnetic generator, friction generator, and composite generator. The friction generator provides energy for external electrical appliances under the principle of friction charge effect and electrostatic balance effect, where the relative position change of different electronegative materials will produce the change of electric potential. [22] The electromagnetic generator generates power through the induction of current by the change of magnetic field in closed coil. The composite generator is an effective combination of these two kinds of generator. The friction nanogenerator is obviously more suitable to supply power for intelligent wearable equipment because of its safety and lightweight compared with magnetoelectricity and composite generator. The main direction of this research is to improve the output performance of the proposed nanogenerator. This can be achieved by changing the characteristics of materials through mixing, microprocessing, and other methods. [23][24][25][26] Another method is to improve the collection efficiency of environmental energy by changing the structure of devices.Nature has rich scientific principles such as DNA, which is tightly and steadily aligned with base pairs containing genetic information in the form of helical twists. The combination of the corresponding base pairs can form an effective genetic expression. This allows the arrangement of more genetic information and match more genetic information accurately in a smaller space in the nucleus. It guarantees the richness and security of information. The relative friction layer and electrode Collecting energy from body movements to supply power for equipment is an important method to rapidly develop wearable intelligent equipment. This method places high requirements, such as being lightweight and stable, on a nanogenerator. Herein, a bioinspired helical triboelectric nanogenerator (BH-TENG) that realizes energy acquisition through the intensity and frequency of body movement is designed based on the helical structure of DNA. The helical structure facilitates more contact areas and provides increased stability to complete the power generation process than the simple structure with pl...

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

Cited by 151 publications

References 101 publications

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET

Bioinspired Helical Triboelectric Nanogenerators for Energy Conversion of Motion

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