scholarly journals Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1073 ◽  
Author(s):  
Hesham El-Sayed ◽  
Moumena Chaqfeh
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Dissemination ◽  
Smart Cities ◽  
Transportation Systems ◽  
Edge Computing ◽  
Communication Overhead ◽  
Mobile Edge Computing ◽  
Communication Frameworks

Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.

scholarly journals Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaolan Tang ◽  
Zhi Geng ◽  
Wenlong Chen ◽  
Mojtaba Moharrer
Keyword(s):  
Fuzzy Logic ◽  
Network Coding ◽  
Vehicular Networks ◽  
Data Dissemination ◽  
Smart Cities ◽  
High Reliability ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Velocity Change ◽  
Backbone Network

Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.

scholarly journals Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6309
Author(s):  
Mohammad Peyman ◽  
Pedro J. Copado ◽  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Edge Computing ◽  
Analytical Models ◽  
Current Status ◽  
Ride Sharing ◽  
The City

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

scholarly journals Analysis of Mobile Edge Computing for Vehicular Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1303 ◽  
Author(s):  
Zachary Lamb ◽  
Dharma Agrawal
Keyword(s):  
Cellular Networks ◽  
Data Storage ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Transportation Systems ◽  
Edge Computing ◽  
Test Bed ◽  
And Storage ◽  
Cloud Resources

Vehicular ad-hoc Networks (VANETs) are an integral part of intelligent transportation systems (ITS) that facilitate communications between vehicles and the internet. More recently, VANET communications research has strayed from the antiquated DSRC standard and favored more modern cellular technologies, such as fifth generation (5G). The ability of cellular networks to serve highly mobile devices combined with the drastically increased capacity of 5G, would enable VANETs to accommodate large numbers of vehicles and support range of applications. The addition of thousands of new connected devices not only stresses the cellular networks, but also the computational and storage requirements supporting the applications and software of these devices. Autonomous vehicles, with numerous on-board sensors, are expected to generate large amounts of data that must be transmitted and processed. Realistically, on-board computing and storage resources of the vehicle cannot be expected to handle all data that will be generated over the vehicles lifetime. Cloud computing will be an essential technology in VANETs and will support the majority of computation and long-term data storage. However, the networking overhead and latency associated with remote cloud resources could prove detrimental to overall network performance. Edge computing seeks to reduce the overhead by placing computational resources nearer to the end users of the network. The geographical diversity and varied hardware configurations of resource in a edge-enabled network would require careful management to ensure efficient resource utilization. In this paper, we introduce an architecture which evaluates available resources in real-time and makes allocations to the most logical and feasible resource. We evaluate our approach mathematically with the use of a multi-criteria decision analysis algorithm and validate our results with experiments using a test-bed of cloud resources. Results demonstrate that an algorithmic ranking of physical resources matches very closely with experimental results and provides a means of delegating tasks to the best available resource.

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

2020 ◽  
Vol 12 (4) ◽  
pp. 63
Author(s):  
Nishu Gupta ◽  
Ravikanti Manaswini ◽  
Bongaram Saikrishna ◽  
Francisco Silva ◽  
Ariel Teles
Keyword(s):  
Automobile Industry ◽  
Intelligent Transportation Systems ◽  
Data Dissemination ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The Internet ◽  
Vehicular Communication ◽  
Security Attacks ◽  
Architecture Model

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.

Data Dissemination in Vehicular Networks

2012 ◽  
pp. 271-297
Author(s):  
Nitin Maslekar ◽  
Mounir Boussedjra ◽  
Houda Labiod ◽  
Joseph Mouzna
Keyword(s):  
Ad Hoc Networks ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Data Dissemination ◽  
Transportation Systems ◽  
Communications Systems ◽  
Effective Dissemination ◽  
Hoc Networks

Vehicular ad hoc networks (VANETs) represent an important component necessary to develop Intelligent Transportation Systems. Recent advances in communications systems have created significant opportunities for a wide variety of applications and services to be implement in vehicles. Most of these applications require a certain dissemination performance to work satisfactorily. Although a variety of optimizations are possible, the basic idea for any dissemination scheme is to facilitate the acquisition of the knowledge about the surrounding vehicles. However, the dynamic nature of vehicular networks makes it difficult to achieve an effective dissemination among vehicles. This chapter provides an overview on those challenges and presents various approaches to disseminate data in vehicular networks.

scholarly journals DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5240
Author(s):  
Anis Koubaa ◽  
Adel Ammar ◽  
Mahmoud Alahdab ◽  
Anas Kanhouch ◽  
Ahmad Taher Azar
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Edge Computing ◽  
Aerial Images ◽  
Computation Offloading ◽  
The Internet ◽  
Real Time Processing ◽  
Computing Approach

Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

scholarly journals On-demand multi-hop forwarding for sustainable vehicular data transfer network

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110151
Author(s):  
Ayoub el Bendali ◽  
Anis Ur Rahman ◽  
Asad Waqar Malik ◽  
Muazzam Ali Khan ◽  
Sri Devi Ravana
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Transfer ◽  
Smart Cities ◽  
Transportation Systems ◽  
Vital Role ◽  
Vehicle Speed ◽  
Coverage Area ◽  
Smart Hospitals

Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

scholarly journals Evaluating the Impact of Drone Signaling in Crosswalk Scenario

2020 ◽  
Vol 11 (1) ◽  
pp. 157
Author(s):  
Sana Bouassida ◽  
Najett Neji ◽  
Lydie Nouvelière ◽  
Jamel Neji
Keyword(s):  
Intelligent Transportation Systems ◽  
Emergency Services ◽  
Data Dissemination ◽  
Smart Cities ◽  
Low Cost ◽  
Well Being ◽  
Transportation Systems ◽  
Traffic Efficiency ◽  
Braking Distance ◽  
The Impact

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is to develop means and solutions capable of meeting the needs of the population, while preserving resources and the environment. Owing to their little size, their flexibility, and their low cost, Unmanned Aerial Vehicles (UAV) are today used in a huge number of daily life applications. UAV use cases can be classified into three categories: data covering (like surveillance and event covering), data relaying (like delivery and emergency services), and data dissemination (like cartography and precise agriculture). In addition, the interest to Cooperative Intelligent Transportation Systems (C-ITS) has risen in these recent years, especially in the context of smart cities. In such systems, both drivers and traffic managers share the information and cooperate to coordinate their actions to ensure safety, traffic efficiency, and environment preservation. In this work, we aimed at introducing a UAV in a use case that is likely to happen in C-ITS. A conflict is considered involving a car and a pedestrian. A UAV observes from the top of the scene and will play the role of the situation controller, the information collector, and the assignment of the instructions to the car driver in case of a harmful situation to avoid car-pedestrian collision. To this end, we highlight interactions between the UAV and the car vehicle (U2V communication), as well as between the UAV and infrastructure (U2I communication). Hence, the benefit of using UAV is emphasized to reduce accident gravity rate, braking distance, energy consumption, and occasional visibility reduction.

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