Edge Computing in IoT: A 6G Perspective

10.36227/techrxiv.17031665 ◽

2021 ◽

Author(s):

Mariam Ishtiaq ◽

Nasir Saeed ◽

Muhammad Asif Khan

Keyword(s):

Driving Forces ◽

Access Network ◽

Edge Computing ◽

Radio Access ◽

Future Networks ◽

Traffic Volumes ◽

Research Problems ◽

Potential Applications ◽

Promising Solution ◽

Multi Access

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, Multi-access Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a huge amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.

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Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Journal of Sensor and Actuator Networks ◽

10.3390/jsan8040051 ◽

2019 ◽

Vol 8 (4) ◽

pp. 51 ◽

Cited By ~ 2

Author(s):

Federico Tonini ◽

Bahare Khorsandi ◽

Elisabetta Amato ◽

Carla Raffaelli

Keyword(s):

Vehicular Networks ◽

Programming Model ◽

High Reliability ◽

Cost Optimization ◽

Access Network ◽

Computational Effort ◽

Edge Computing ◽

Hybrid Strategy ◽

Radio Access ◽

Multi Access

The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽

10.3390/telecom2040026 ◽

2021 ◽

Vol 2 (4) ◽

pp. 446-471

Author(s):

Percy Kapadia ◽

Boon-Chong Seet

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Access Network ◽

Edge Computing ◽

Fifth Generation ◽

Radio Access ◽

Ping Pong ◽

Lstm Network ◽

Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

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Programmability of Connectivity Control

Elektronika ir Elektrotechnika ◽

10.5755/j02.eie.28819 ◽

2021 ◽

Vol 27 (2) ◽

pp. 78-85

Author(s):

Ivaylo I. Atanasov ◽

Evelina N. Pencheva

Keyword(s):

Access Network ◽

Edge Computing ◽

Core Network ◽

Access Technology ◽

Radio Access Network ◽

Radio Access Technology ◽

Fifth Generation ◽

Radio Access ◽

Multi Access ◽

Network Interfaces

Network programmability and edge computing as key features of next generation communications enable innovative services. While the programmability is focused on the core network of the fifth-generation system, the edge computing moves the network intelligence to the radio access network. This paper presents a study on the programmability of connectivity control as a function of radio access network using Multi-access Edge Computing. The capability of using more than one radio access technology simultaneously enhances reliability and increases the throughput, especially in dense networks. Opening the radio access network interfaces for programmability of multi-connectivity enables analytics applications to control the device connections to multiple radio links simultaneously based on information of radio conditions, user location or specific policies. The research novelty is in opening the radio access network interfaces for edge applications to access connectivity control.

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The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

10.21203/rs.3.rs-924666/v1 ◽

2021 ◽

Author(s):

Xiaolong Wang ◽

Jianwu Dang ◽

Shuxu Zhao ◽

Zhanping Zhang ◽

Yangping Wang ◽

...

Keyword(s):

Coalition Formation ◽

Large Scale ◽

Market Equilibrium ◽

Access Network ◽

Equilibrium Price ◽

Edge Computing ◽

Computation Offloading ◽

Data Traffic ◽

Radio Access ◽

Multi Access

Abstract The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale IoT services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. The coalition method has been used in MEC for resource optimization, latency, energy consumption reduction, computation offloading, etc. However, the majority of research does not consider the price of computing resources corresponded to a container. Moreover, each SP does not choose EIP with the highest cost-performance to sign a medium/long-term computing resource purchase or lease contract. In this work, we consider a scenario with a collection of SPs with different budgets and several EIPs distributed in geographical locations. During the first phase, we get the market equilibrium price and select the optimal EIPs to make a deal by solving the Eisenberg-Gale convex program. In the second stage, using a mathematical model, we maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that the effectiveness of our method is significantly better than the existing model.

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