Reverse CDN in Fog Computing: The lifecycle of video data in connected and autonomous vehicles

2017 ◽  
Author(s):  
Hassnaa Moustafa ◽  
Eve M. Schooler ◽  
Jessica McCarthy
Keyword(s):  
Autonomous Vehicles ◽  
Fog Computing ◽  
Video Data

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

scholarly journals Fog-Based Distributed Networked Control for Connected Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhuwei Wang ◽  
Yuehui Guo ◽  
Yu Gao ◽  
Chao Fang ◽  
Meng Li ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fog Computing ◽  
Control Design ◽  
Network Control ◽  
Future Research ◽  
Cruise Control ◽  
Real Time Control ◽  
Time Control

With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.

scholarly journals Challenges of Future VANET and Cloud-Based Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rakesh Shrestha ◽  
Rojeena Bajracharya ◽  
Seung Yeob Nam
Keyword(s):  
Cloud Computing ◽  
Autonomous Vehicles ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fog Computing ◽  
High Mobility ◽  
Global Knowledge ◽  
Smart Vehicles ◽  
Traffic Efficiency ◽  
Hoc Networks

Vehicular ad hoc networks (VANETs) have been studied intensively due to their wide variety of applications and services, such as passenger safety, enhanced traffic efficiency, and infotainment. With the evolution of technology and sudden growth in the number of smart vehicles, traditional VANETs face several technical challenges in deployment and management due to less flexibility, scalability, poor connectivity, and inadequate intelligence. Cloud computing is considered a way to satisfy these requirements in VANETs. However, next-generation VANETs will have special requirements of autonomous vehicles with high mobility, low latency, real-time applications, and connectivity, which may not be resolved by conventional cloud computing. Hence, merging of fog computing with the conventional cloud for VANETs is discussed as a potential solution for several issues in current and future VANETs. In addition, fog computing can be enhanced by integrating Software-Defined Network (SDN), which provides flexibility, programmability, and global knowledge of the network. We present two example scenarios for timely dissemination of safety messages in future VANETs based on fog and a combination of fog and SDN. We also explained the issues that need to be resolved for the deployment of three different cloud-based approaches.

scholarly journals Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 57
Author(s):  
Ryan Feng ◽  
Yu Yao ◽  
Ella Atkins
Keyword(s):  
Data Collection ◽  
Real World ◽  
Autonomous Vehicles ◽  
Ground Truth ◽  
Black Box ◽  
Video Data ◽  
Detection Methods ◽  
Real World Data ◽  
The Real ◽  
High Bandwidth

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The smart black box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with an FIFO strategy. The results show that SBB data compression can increase the anomalous-to-normal memory ratio by ∼25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to an FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.

Multicasting Data Routing for Vehicular Ad hoc Network using Fog Computing

2021 ◽  
pp. 37-48
Author(s):  
Seema Gaba ◽  
◽  
Kavita . ◽  
Sahil Verma ◽  
Monica Sood ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Ad Hoc Network ◽  
Autonomous Vehicles ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Intelligent Transportation System ◽  
Fog Computing ◽  
Research Work ◽  
Vehicular Ad Hoc Network ◽  
Network Simulator

A group of vehicles either mobile or stationery that is interconnected through a wireless network generate a vehicular ad hoc network (VANET). Providing comfort as well as safety to the drivers in vehicular scenarios is the main importance of VANETs. Since there is an increase in the number of autonomous vehicles, these networks are now being considered as an infrastructure for an intelligent transportation system. Fog computing can be provided low latent information sharing and more background knowledge by localizing one of the features. This research work is related to data aggregation in vehicular ad hoc networks. In this research work, the technique of multicasting will be proposed for the data aggregation in VANETs. The Network Simulator 2 is used to perform experiments and few performance measures are used for analysing the outcomes.

scholarly journals A method to keep autonomous vehicles steadily drive based on lane detection

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110029
Author(s):  
Zhenyu Wu ◽  
Kai Qiu ◽  
Tingning Yuan ◽  
Hongmei Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Driving Behavior ◽  
Video Data ◽  
Short Term ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory

Existing studies on autonomous driving methods focus on the fusion of onboard sensor data. However, the driving behavior might be unsteady because of the uncertainties of environments. In this article, an expectation line is proposed to quantify the driving behavior motivated by the driving continuity of human drivers. Furthermore, the smooth driving could be achieved by predicting the future trajectory of the expectation line. First, a convolutional neural network-based method is applied to detect lanes in images sampled from driving video. Second, the expectation line is defined to model driving behavior of an autonomous vehicle. Finally, the long short-term memory-based method is applied to the expectation line so that the future trajectory of the vehicle could be predicted. By incorporating convolutional neural network- and long short-term memory-based methods, the autonomous vehicles could smoothly drive because of the prior information. The proposed method is evaluated using driving video data, and the experimental results demonstrate that the proposed method outperforms methods without trajectory predictions.

scholarly journals A Survey of the Tactile Internet: Design Issues and Challenges, Applications, and Future Directions

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2171
Author(s):  
Vaibhav Fanibhare ◽  
Nurul I. Sarkar ◽  
Adnan Al-Anbuky
Keyword(s):  
Autonomous Vehicles ◽  
Fog Computing ◽  
Modern Society ◽  
The Internet ◽  
Research Issues ◽  
Network Function ◽  
Time Interaction ◽  
Enabling Technologies ◽  
Comprehensive Survey ◽  
Internet Design

The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about a massive change in Healthcare 4.0, Industry 4.0 and autonomous vehicles to resolve complicated issues in modern society. This vision of TI makes a dream into a reality. This article aims to provide a comprehensive survey of TI, focussing on design architecture, key application areas, potential enabling technologies, current issues, and challenges to realise it. To illustrate the novelty of our work, we present a brainstorming mind-map of all the topics discussed in this article. We emphasise the design aspects of the TI and discuss the three main sections of the TI, i.e., master, network, and slave sections, with a focus on the proposed application-centric design architecture. With the help of the proposed illustrative diagrams of use cases, we discuss and tabulate the possible applications of the TI with a 5G framework and its requirements. Then, we extensively address the currently identified issues and challenges with promising potential enablers of the TI. Moreover, a comprehensive review focussing on related articles on enabling technologies is explored, including Fifth Generation (5G), Software-Defined Networking (SDN), Network Function Virtualisation (NFV), Cloud/Edge/Fog Computing, Multiple Access, and Network Coding. Finally, we conclude the survey with several research issues that are open for further investigation. Thus, the survey provides insights into the TI that can help network researchers and engineers to contribute further towards developing the next-generation Internet.

scholarly journals Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1517
Author(s):  
Di Xiao ◽  
Min Li ◽  
Hongying Zheng
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Rapid Development ◽  
Fog Computing ◽  
Low Complexity ◽  
Video Data ◽  
Edge Computing ◽  
Scalar Data ◽  
Key Frames ◽  
Multi Access

Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.

scholarly journals DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Volkov Artem ◽  
Kovalenko Vadim ◽  
Ibrahim A. Elgendy ◽  
Ammar Muthanna ◽  
Andrey Koucheryavy
Keyword(s):  
Communication Networks ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Complex Nature ◽  
Application Development ◽  
High Data ◽  
Search Mechanism ◽  
User Query ◽  
Rational Allocation ◽  
Delivery Services

Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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