Software-Defined and Fog-Computing-Based Next Generation Vehicular Networks

2018 ◽  
Vol 56 (9) ◽  
pp. 34-41 ◽  
Author(s):  
Yaomin Zhang ◽  
Haijun Zhang ◽  
Keping Long ◽  
Qiang Zheng ◽  
Xiaoming Xie
Keyword(s):  
Vehicular Networks ◽  
Fog Computing ◽  
Next Generation

scholarly journals Millimeter Wave Spatial Channel Characterization for Vehicular Communications

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 64 ◽  
Author(s):  
Fidel Rodríguez-Corbo ◽  
Leyre Azpilicueta ◽  
Mikel Celaya-Echarri ◽  
Peio López-Iturri ◽  
Imanol Picallo ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Autonomous Vehicles ◽  
Communication Systems ◽  
Vehicular Networks ◽  
Small Scale ◽  
Vehicular Communications ◽  
Next Generation ◽  
Vehicular Communication ◽  
High Definition ◽  
High Data

With the growing demand of vehicle-mounted sensors over the last years, the amount of critical data communications has increased significantly. Developing applications such as autonomous vehicles, drones or real-time high-definition entertainment requires high data-rates in the order of multiple Gbps. In the next generation of vehicle-to-everything (V2X) networks, a wider bandwidth will be needed, as well as more precise localization capabilities and lower transmission latencies than current vehicular communication systems due to safety application requirements; 5G millimeter wave (mmWave) technology is envisioned to be the key factor in the development of this next generation of vehicular communications. However, the implementation of mmWave links arises with difficulties due to blocking effects between mmWave transceivers, as well as different channel impairments for these high frequency bands. In this work, the mmWave channel propagation characterization for V2X communications has been performed by means of a deterministic in-house 3D ray launching simulation technique. A complex heterogeneous urban scenario has been modeled to analyze the different propagation phenomena of multiple mmWave V2X links. Results for large and small-scale propagation effects are obtained for line-of-sight (LOS) and non-LOS (NLOS) trajectories, enabling inter-data vehicular comparison. These analyzed results and the proposed methodology can aid in an adequate design and implementation of next generation vehicular networks.

scholarly journals IoT-Fog A Communication Framework using Blockchain in the Internet of Things

2020 ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Fog Computing ◽  
Emerging Technology ◽  
Smart Devices ◽  
The Internet ◽  
Next Generation ◽  
Communication Framework ◽  
Generation Computing ◽  
The Internet Of Things

<p>The fog computing is the emerging technology to compute, store, control and connecting smart devices with each other using cloud computing. The Internet of Things (IoT) is an architecture of uniquely identified interrelated physical things, these physical things are able to communicate with each other and can transmit and receive information. <a>This research presents a framework of the combination of the Internet of Things (IoT) and Fog computing. The blockchain is also the emerging technology that provides a hyper, distributed, public, authentic ledger to record the transactions. Blockchains technology is a secured technology that can be a boon for the next generation computing. The combination of fog, blockchains, and IoT creates a new opportunity in this area. In this research, the author presents a middleware framework based on the blockchain, fog, and IoT. The framework is implemented and tested. The results are found positive. </a></p>

Time‐Critical Fog Computing for Vehicular Networks

Fog Computing ◽  
2020 ◽  
pp. 431-458 ◽  
Author(s):  
Ahmed Chebaane ◽  
Abdelmajid Khelil ◽  
Neeraj Suri
Keyword(s):  
Vehicular Networks ◽  
Fog Computing ◽  
Time Critical

scholarly journals Efficient Task and Data Scheduling Policy for Vehicular Fog Computing Based on Link Weight

2019 ◽  
Vol 8 (4) ◽  
pp. 10693-10697
Keyword(s):  
Data Dissemination ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Vehicular Network ◽  
Next Generation ◽  
New Paradigm ◽  
Multiple Parameters ◽  
Link Weight ◽  
Time Period ◽  
Generation Computing

Vehicular network has several applications in the smart city and IoT. Recently with the advancement in the computing technology such as fog computing and its application in the vehicular network and its services, a new paradigm known as vehicular fog computing has evolved as a hot topic of investigation in the research community because of the next generation computing and communication requirements. Vehicular fog computing can be used to solve the issues of next generation computing and communication scenario. There are several issues in vehicular fog computing. Efficient task computing and data dissemination is an important issue. Several approaches are proposed by different authors to solve the issues, but none of them has addressed the service completion and failure rate which is very important in the vehicular scenario as the vehicles move very fast and its contact time with the RSU controller is limited. The task has to be completed by the vehicular server within that time period, otherwise computation will fail. Once the computation and communication fails, the RSU controller will reinitiate to form the vehicular fog resulting high overhead. In this paper we address this issue and proposed an efficient scheduling algorithm based on multiple parameters namely queue length, response time and link weight. We simulated the algorithm using java and compared with the existing algorithm showing better performance.

scholarly journals Trust management in social Internet of vehicles: Factors, challenges, blockchain, and fog solutions

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771982582 ◽  
Author(s):  
Razi Iqbal ◽  
Talal Ashraf Butt ◽  
Muhammad Afzaal ◽  
Khaled Salah
Keyword(s):  
Internet Of Things ◽  
Trust Management ◽  
Vehicular Networks ◽  
High Efficiency ◽  
Fog Computing ◽  
Trust Model ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Social Internet Of Things ◽  
Trust Models

The Internet of things is the next stage in the evolution of the Internet that is being materialized with the integration of billions of smart objects. The state-of-the-art communication technologies have enabled the previously isolated devices to become an active part of the Internet. This constant connectivity opens new avenues for novel applications such as the realization of social Internet of things and its subdomain the social Internet of vehicles. Socializing requires sharing of information that entails trust, especially in an open and broad social environment. This article highlights the key factors involved in conceptualizing an efficient trust model for social Internet of vehicles. Furthermore, it focuses on the unique challenges involved in designing the trust models for social Internet of vehicles. Several trust models exist in literature; however, most of the existing trust models are specific to their domains, for example, Internet of things, social Internet of things, or general vehicular networks. This article presents a brief review of the trust models that have the potential to be implemented in Social Internet of vehicles. Finally, the authors present an overview of how trending concepts and emerging technologies like blockchain and fog computing can assist in developing a trust-based social Internet of vehicles model for high-efficiency, decentralized architecture and dynamic nature of vehicular networks.

Expounding the Edge/Fog Computing Infrastructures for Data Science

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Pethuru Raj ◽  
Pushpa J.
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Analytical Methods ◽  
Data Analytics ◽  
Data Science ◽  
Storage Systems ◽  
Fog Computing ◽  
Next Generation ◽  
Distributed Source ◽  
Real Time Applications

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.

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