Data traffic management in a hybrid cloud composed of Openstack and Azure

2019 ◽  
Author(s):  
Santiago J. Arevalo-Cordero ◽  
Pablo L. Gallegos-Segovia ◽  
Paul E. Vintimilla-Tapia ◽  
Jack F. Bravo-Torres ◽  
E. Julieta Cedillo-Elias ◽  
...  
Keyword(s):  
Traffic Management ◽  
Hybrid Cloud ◽  
Data Traffic

5G Mobile Communication, Challenges: A Survey

2020 ◽  
Vol 2 (1) ◽  
pp. 35-42
Author(s):  
Hemalatha R ◽  
Rhesa M.J. ◽  
Revathi S
Keyword(s):  
Mobile Communication ◽  
Traffic Management ◽  
Radiation Effects ◽  
Critical Infrastructure ◽  
Security Threats ◽  
Technological Advancement ◽  
Data Traffic ◽  
Communication Challenges ◽  
5G Mobile Communication ◽  
Living Organisms

The hest for technological advancement in mobile communication is due to augmentation of wireless user. The deployment of 5G mobile communication is less than 4G mobile communication due to challenges in security like cyberwarfare, espionage, critical infrastructure threats. Nevertheless, critic of neurological discomforts, tissue damage in living organisms occur in the existence of EMF radiation. Also, physical scarcity for spectral efficiency arises due to ubiquitous data traffic. Inspite of these disputes data rate, low latency, device to device communication is also a challenge. In this paper we provide a survey on radiation effects, security threats, traffic management.

The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

2021 ◽  
pp. 1-16
Author(s):  
Sasikala Chinthakunta ◽  
Shoba Bindu Chigarapalle ◽  
Sudheer Kumar E.
Keyword(s):  
Data Storage ◽  
Smart Grids ◽  
Traffic Management ◽  
Fog Computing ◽  
Poor Quality ◽  
Data Traffic ◽  
Cloud Data ◽  
Security Issues ◽  
Huge Data ◽  
Iot Devices

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

A Communication Model Based on Fractal Geometry for Internet of Things

2021 ◽  
pp. 192-212
Author(s):  
Sergio Ariel Salinas
Keyword(s):  
Internet Of Things ◽  
Human Activities ◽  
Fractal Geometry ◽  
Physical System ◽  
Traffic Management ◽  
Communication Model ◽  
Resource Usage ◽  
Data Traffic ◽  
Strongly Coupled ◽  
Decentralized Model

Internet of things (IoT) is a paradigm that involves an increasing number of human activities. IoT fuses heterogeneous electronic devices and processes into cyber physical system (CPS) to improve conventional processes efficiency in terms of performance and resource usage. The Institute of Electrical and Electronics Engineering (IEEE) has polled more than 150 IoT scenarios based on CPSs to be developed in the next years. A CPS is strongly coupled to a communication system for interchanging data among devices that orchestrate actions in certain environments such as a factory. In general, a decentralized communication model is more resilient and efficient in data traffic management than centralized model. A problem with decentralized models is how to keep track of nodes location without a centralized service that keeps updated data of nodes. This work describes a decentralized model where any node is able to be located based on its identification in the system.

Efficient Solutions for Enhancing Data Traffic Management in 3GPP Networks

2015 ◽  
Vol 9 (2) ◽  
pp. 519-528 ◽  
Author(s):  
Tarik Taleb ◽  
Yassine Hadjadj-Aoul ◽  
Konstantinos Samdanis
Keyword(s):  
Traffic Management ◽  
Efficient Solutions ◽  
Data Traffic

Value-based utility implementation in software-defined testbed for sensor data traffic management

2019 ◽  
Vol 101 ◽  
pp. 737-746
Author(s):  
Melchizedek I. Alipio ◽  
Alex Gyran A. Co ◽  
Melinda Faye C. Hilario ◽  
Christian Miguel C. Pama
Keyword(s):  
Traffic Management ◽  
Sensor Data ◽  
Data Traffic

scholarly journals An Interest-Based Approach for Reducing Network Contentions in Vehicular Transportation Systems

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2325 ◽  
Author(s):  
Allan M. de Souza ◽  
Guilherme Maia ◽  
Torsten Braun ◽  
Leandro A. Villas
Keyword(s):  
Traffic Management ◽  
State Of The Art ◽  
Transportation Systems ◽  
Traffic Information ◽  
Vehicular Traffic ◽  
Data Traffic ◽  
Traffic Demand ◽  
Traffic Efficiency ◽  
High Data ◽  
Content Sharing

Traffic management systems (TMS) are the key for dealing with mobility issues. Moreover, 5G and vehicular networking are expected to play an important role in supporting TMSs for providing a smarter, safer and faster transportation. In this way, several infrastructure-based TMSs have been proposed to improve vehicular traffic mobility. However, in massively connected and multi-service smart city scenarios, infrastructure-based systems can experience low delivery ratios and high latency due to packet congestion in backhaul links on ultra-dense cells with high data traffic demand. In this sense, we propose I am not interested in it (IAN3I), an interest-based approach for reducing network contention and even avoid infrastructure dependence in TMS. IAN3I enables a fully-distributed traffic management and an opportunistic content sharing approach in which vehicles are responsible for storing and delivering traffic information only to vehicles interested in it. Simulation results under a realistic scenario have shown that, when compared to state-of-the-art approaches, IAN3I decreases the number of transmitted messages, packet collisions and latency in up to 95 % , 98 % and 55 % respectively while dealing with traffic efficiency properly, not affecting traffic management performance at all.

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