scholarly journals Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3785
Author(s):  
Ali Tufail ◽  
Abdallah Namoun ◽  
Adnan Ahmed Abi Sen ◽  
Ki-Hyung Kim ◽  
Ahmed Alrehaili ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Traffic Monitoring ◽  
Edge Computing ◽  
Small Scale ◽  
The Internet ◽  
Cloud Infrastructure ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Scale Data

Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.

scholarly journals Mobile Edge Computing for the Internet of Vehicles

2021 ◽  
pp. 47-64
Author(s):  
Yan Zhang
Keyword(s):  
Edge Computing ◽  
The Internet ◽  
Vehicular Communication ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Delay Constraints ◽  
Communication Environment ◽  
Artificial Intelligence Technology ◽  
Physical Information ◽  
Computing Framework

AbstractThe advancement of cyber physical information has led to the pervasive use of smart vehicles while enabling various types of powerful mobile applications, which usually require high-intensity processing under strict delay constraints. Given their limited on-board computing capabilities, smart vehicles can offload these processing tasks to edge servers for execution. However, a highly dynamic topology, a complex vehicular communication environment, and edge node heterogeneity pose significant challenges in vehicular edge computing management. To address these challenges, in this chapter we investigate the characteristics of edge computing from both the application and service perspectives and introduce a hierarchical edge computing framework. Moreover, we leverage artificial intelligence technology to propose efficient task offloading and resource scheduling schemes.

scholarly journals Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

2021 ◽  
Vol 59 (8) ◽  
pp. 52-57
Author(s):  
Jie Xu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
...  
Keyword(s):  
Distributed Learning ◽  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Multi Access

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

Information Retrieval and Access in Cloud

2021 ◽  
pp. 1841-1854
Author(s):  
Punit Gupta ◽  
Ravi Shankar Jha
Keyword(s):  
Cloud Computing ◽  
Information Retrieval ◽  
Resource Sharing ◽  
High Reliability ◽  
High Load ◽  
The Internet ◽  
Cloud Infrastructure ◽  
Distributed Environment ◽  
Computing Services ◽  
Cloud Computing Services

With increase of information sharing over the internet or intranet, we require techniques to increase the availability of shared resource over large number of users trying to access the resources at the same time. Many techniques are being proposed to make access easy and more secure in distributed environment. Information retrieval plays an important to serve the most reliant data in least waiting, this chapter discuses all such techniques for information retrieval and sharing over the cloud infrastructure. Cloud Computing services provide better performance in terms of resource sharing and resource access with high reliability and scalability under high load.

Applications of Cloud-Based Internet of Things

2021 ◽  
pp. 65-84
Author(s):  
Nipun R. Navadia ◽  
Gurleen Kaur ◽  
Harshit Bhardwaj ◽  
Taranjeet Singh ◽  
Aditi Sakalle ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Business Model ◽  
Data Driven ◽  
The Internet ◽  
Cloud Infrastructure ◽  
Business Decisions ◽  
Goods And Services ◽  
Cloud Network ◽  
The Internet Of Things

Cloud storage is a great way for companies to fulfill more of their data-driven needs and excellent technology that allows the company to evolve and grow at a faster pace, accelerating growth and providing a flexible forum for developers to build useful apps for better devices to be developed over the internet. The integration of cloud computing and the internet of things creates a scalable, maintainable, end-to-end internet of things solution on the cloud network. By applying the infrastructure to the real universe, it generates sources of insight. Cloud computing and IoT are separate technology but are closely associated and are termed as ‘cloud-based IoT' as IoT has the ability to create intelligent goods and services, gather data that can affect business decisions and probably change the business model to boost success and expansion, and cloud infrastructure can be at the heart of all IoT has to deliver.

A Blockchain-based Containerized Edge Computing Platform for the Internet of Vehicles

2020 ◽  
pp. 1-1
Author(s):  
Laizhong Cui ◽  
Ziteng Chen ◽  
Shu Yang ◽  
Zhongxing Ming ◽  
Qi Li ◽  
...  
Keyword(s):  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Computing Platform

scholarly journals SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Juan Fang ◽  
Kai Li ◽  
Juntao Hu ◽  
Xiaobin Xu ◽  
Ziyi Teng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Simulated Annealing Algorithm ◽  
Smart Cities ◽  
Structural Characteristics ◽  
Edge Computing ◽  
The Internet ◽  
Sensors And Actuators ◽  
Application Module

The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption.

scholarly journals Dashboard for Assessing Data Centers Green Compliance

2013 ◽  
Vol 6 (1) ◽  
pp. 719-726
Author(s):  
Fatemeh Binesh ◽  
Saravanan Muthaiyah
Keyword(s):  
Cloud Computing ◽  
Waste Management ◽  
Data Center ◽  
Data Centers ◽  
Environmental Hazard ◽  
The Internet ◽  
Compliance Level ◽  
The World ◽  
New Research ◽  
Near Future

Abstract: Nowadays, ICT sector activities and in particular Data Centers are known as an important environmental hazard. With the increasing popularity of the Internet and cloud computing, this threat seems to even get worse in the near future. Despite this increasing importance, there is still little have been done about data centers environmental affects and in particular measuring their green compliance level including all three Rs of waste management (Reuse, Reuse and Recycle). This paper tries to introduce a dashboard for evaluating data centers level of green compliance regardless of their tier. However, the dashboard is proposed based on Malaysias data centers condition, it still can be beneficial to data center managers in other parts of the world and researchers to open up new research possibilities.  

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

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