A novel framework for optimizing the edge network node for wearable devices

Third party session control can be useful in many Internet of Things (IoT) use cases including monitoring and assistance of elderly and mission critical communications. Enhanced session control with media playing, collecting and recording interaction adds value and enables more information to be provided and gathered. The Multi-access Edge Computing (MEC), which brings the cloud computational and storage power at the network edge, can address many IoT use cases. The paper studies the capabilities of exposing media control to MEC applications. MEC Application Programming Interfaces that enable third party application to play media message, to collect and record media interactions within ongoing session, are presented. Media control data model is proposed. As a proof of the concept, state models supported by the network and MEC application are presented, formally described and verified.

Chapter 28 - Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

Various real-time, latency-sensitive, and high-speed mobile applications are evolving as 5G applications. These applications are realized using Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technologies in the 5G system. MEC platform, MEC service, and MEC application are the main components of an MEC Framework. NFV Orchestrator, Virtualized Infrastructure Manager (VIM), and virtualization technologies such as Virtual Machines (VM) and Containers are the main pillars of the NFV technology. In this paper, we study the impact of the virtualization technologies in the deployment of the MEC framework and its components while. We also study the impact of virtualization technologies on NFV and MEC KPIs such as onboarding time, instantiation time, MEC service and application response times. The experiments and its analysis show that containers perform better than VM to instantiate/terminate MEC components in the NFV framework. The observed MEC service KPIs show that the edge application’s performance will be improved to meet the QoE of the applications irrespective of the virtualization technology used. These results can be used as a reference while deploying the MEC components based on their granular functionalities.

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

Today’s advancements in IoT devices and edge computing platforms have given rise to new scenarios enabling context-aware applications in extremely interconnected environments. To promote the standardization of these platforms the European Telecommunications Standards Institute (ETSI) proposed the Multi-access Edge Computing (MEC) standard, enabling the execution of cloud-like services at the network edge. In this work, we propose the design of a novel MEC-compliant architecture that leverages underutilized far-edge resources to enlarge MEC edge node computational capacity and enhance service availability in highly mobile networks. Our approach allows far-edge devices to participate in a negotiation process embodying a rewarding system while addressing resource volatility as these devices join and leave the edge node resource infrastructure. Furthermore, we developed an original simulation framework to replicate the proposed architecture by using vehicle resources as far-edge devices. Its primary purpose is to demonstrate the viability and flexibility of our proposal, as well as to investigate novel application scenarios using real-world datasets. Our preliminary results show the feasibility and effectiveness of our proposal when using vehicular-based virtual resources in realistically simulated 5G networks.

Fifth generation (5G) mobile system is designed to support mission critical communications which requires significantly reduced delays and high reliability. Multi-access Edge Computing (MEC) technology, as a key component of 5G, may satisfy the strong requirements for network resilience and latency. The most benefit from the advantages of MEC may be achieved in case of MEC deployment as close as to the network edge. In this paper, we consider two MEC deployment scenarios for mission critical communications, and estimate the latency introduced by the control plane in provisioning of real time radio network information.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.

Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.

5G ecosystems will benefit significantly from Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV). While NFV allows for dynamical deployment of virtualized network functions, MEC allows applications to be deployed close to mobile users, thus reducing latency. When used together, NFV and MEC bring flexibility and enhanced performance to meet the user’s demand. However, to increase service availability and maximize the convenience of MEC users, an MEC framework has to interface with multiple NFV orchestrators to utilize running network functions in an efficient manner. We introduce in this demonstration APMEC, a framework addressing the above two challenges. Via a combination of an interactive GUI, KPI views and a live-demo setup, we will showcase the advanced features of the framework.

Data reliability and confidence in the data are very important issues, especially when the system integrates fraud or false information. The misusing of data collected may create serious problems. With the fast development of computing techniques, much data are collected from various terminals and industrial devices. Edge computing operates by driving data, software and computer resources from the centralized network to its extremes, allowing pieces of knowledge to lie on distributed cloud networks. Its target customers continue to use commercial Internet application software for every internet customer. Edge computing is used to provide delay-free customer experience assistance for features of the Internet of Things (IoT) services on the edge of the user network. The document identifies an IoT computing platform collaborating with the edge competitive data management latency (CDML) tool. This approach separately categorizes edge layer requests and response data over time using demand-density driven optimization. A difference-based optimization optimizes the frame limits for simultaneous request processing and exact allocation of data. The architectural efficiency of edge computing can be assessed by comparing latency, bandwidth usage, and overhead. Furthermore, estimating the availability, credibility and confidentiality of security solutions within each party would take into consideration security concerns in edge computing and propose a safety assessment process for IoT networks with edge computing. This procedure is finally validated using appropriate tests, and the resulting findings are examined to demonstrate the method’s accuracy. Experimental data are used to validate methods to request maintenance and processing, response time, resource utilization and contract period. In comparison to current approaches, the results of the proposed CDML are measured with a percentage of 97.90%. The proposed system enhances the request and response comparison ratio 97.5%, analyzing request performance ratio 98.1%, response with time analysis ratio of 98.3%, data allocation approach analysis ratio 97.7%.

Multi-Access Edge Computing (MEC) promises to provide sufficient computing capacity close to users and realize smart management at the edge of mobile network. To achieve the aforementioned objectives, it is indispensable to implement real-time monitoring of the whole MEC network. However, the geo-distributed deployment of MEC infrastructure dramatically increases the communication latency of gathering the state information (e.g. current status and resource utilization) from servers at the edge. This paper addresses latency-optimal placement and autoscaling of monitoring functions in MEC. First, we formally formulate latency-optimal placement of monitoring functions as an integer linear programming problem and proposed a genetic algorithm-based meta-heuristic to obtain the optimal solution with fast convergence. Moreover, to serve the time-varying demand on resource capacity from diversified mobile services, an online VNF scaling scheme is designed for realizing on-demand resource allocation. The effectiveness of our heuristic algorithm is verified through both numerical simulation and experiments in real cloud environment. Experimental results demonstrate performance superiority of the proposed approach over the state-of-art researches, in terms of algorithm CPU time, total network latency and long-term scaling cost.

Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust