Energy efficiency of Fog Computing and Networking services in 5G networks

2017 ◽  
Author(s):  
Stojan Kitanov ◽  
Toni Janevski
Keyword(s):  
Energy Efficiency ◽  
Fog Computing ◽  
5G Networks

scholarly journals Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges

2017 ◽  
Vol 24 (3) ◽  
pp. 86-94 ◽  
Author(s):  
K. N. R. Surya Vara Prasad ◽  
Ekram Hossain ◽  
Vijay K. Bhargava
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
5G Networks

scholarly journals Energy Efficiency of Fog Computing Health Monitoring Applications

2018 ◽  
Author(s):  
Ida Syafiza M. Isa ◽  
Mohamed O. I. Musa ◽  
Taisir E. H. El-Gorashi ◽  
Ahmed Q. Lawey ◽  
Jaafar M. H. Elmirghani
Keyword(s):  
Energy Efficiency ◽  
Health Monitoring ◽  
Fog Computing ◽  
Monitoring Applications

Quality Evaluation of Cloud and Fog Computing Services in 5G Networks

2019 ◽  
pp. 1-36 ◽  
Author(s):  
Stojan Kitanov ◽  
Borislav Popovski ◽  
Toni Janevski
Keyword(s):  
Network Performance ◽  
Fog Computing ◽  
High Mobility ◽  
Network Control ◽  
5G Networks ◽  
New Paradigm ◽  
Network Function ◽  
Computing Services ◽  
5G Network ◽  
Intelligent Networking

Because of the increased computing and intelligent networking demands in 5G network, cloud computing alone encounters too many limitations, such as requirements for reduced latency, high mobility, high scalability, and real-time execution. A new paradigm called fog computing has emerged to resolve these issues. Fog computing distributes computing, data processing, and networking services to the edge of the network, closer to end users. Fog applied in 5G significantly improves network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This chapter evaluates the quality of cloud and fog computing services in 5G network, and proposes five algorithms for an optimal selection of 5G RAN according to the service requirements. The results demonstrate that fog computing is a suitable technology solution for 5G networks.

A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks

2020 ◽  
Vol 7 (5) ◽  
pp. 4278-4291 ◽  
Author(s):  
Jianbin Gao ◽  
Kwame Opuni-Boachie Obour Agyekum ◽  
Emmanuel Boateng Sifah ◽  
Kingsley Nketia Acheampong ◽  
Qi Xia ◽  
...  
Keyword(s):  
Fog Computing ◽  
5G Networks ◽  
Internet Of Vehicles

scholarly journals A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-enabled IoT

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6555
Author(s):  
Radwa Ahmed Osman ◽  
Sherine Nagy Saleh ◽  
Yasmine N. M. Saleh
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Optimization Technique ◽  
Data Communication ◽  
Learning Model ◽  
Base Station ◽  
System Throughput ◽  
Interference Avoidance ◽  
5G Networks ◽  
Deep Learning Model

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

scholarly journals Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

2021 ◽  
Vol 5 (2) ◽  
pp. 105
Author(s):  
Wasswa Shafik ◽  
S. Mojtaba Matinkhah ◽  
Mamman Nur Sanda ◽  
Fawad Shokoor
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Response Time ◽  
Smart Grids ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Delay Sensitive ◽  
Service Delay

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

Sign in / Sign up

Export Citation Format

Share Document