View of A Compendium On Edge Computing Where Ai Can Meet

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A Compendium On Edge Computing Where Ai Can Meet

1 _{Research Scholar, Department of Computer Science and Engineering,}

GITAM (Deemed to be University), Visakhapatnam, AP, India.

2 _{Assistant Professor, Department of Computer Science and Engineering,}

GITAM (Deemed to be University), Visakhapatnam, AP, India.

Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online:28April 2021

Abstract

Edge computing, which seems to be the latest computer framework in which information is stored on board, is the gateway to the proliferation of the internet of things and the potential to capture, interpret, and deliver huge information in the cloud. Edge Computing has gained interest in the last 2 years and has strategic implications as being among the best competitive advances in technology. It has less network performance, lower network costs, and better confidentiality security than cloud services. Edge computing contrasts cloud computing by providing computing capabilities that allow incredibly huge amounts of information from IoT devices to be processed and smart decisions need to be made. In general, automated computation is quickly implemented in artificial intelligence technology. The very first focuses on providing more efficient and popular AI technologies to optimize techniques for main problems in Edge Computing since the second discusses how its entire process of AI creation methods, i.e., model creation and expertise, is to become a part of the framework. In this paper, we suggest concepts, histories, and advantages of edge computing, demonstrate what happens and structures hierarchical structures, identify examples of its applications in various fields, and subsequent mainly improvements, and discuss the challenges of its use in 3 reflecting areas of technology.

Keywords: Edge computing, Cloud computing, Artificial Intelligence, Internet of Things. 1. INTRODUCTION

Edge computing is using for several principles, including fog calculation, web, and mobile edge calculation. Edge computing is a technique for analysing and processing data instantaneously also at the edge of the data-gathering device. The data collection and calculation [5] is not a geographically distant database server. It is a system that locates computers or analyses and processes the data inside a central computer. Data are stored mechanically at close ranges in real-time to allow amplification of the flow of data. This decreases the data delay drastically and helps consumers to have faster support [1].In the below -mentioned figure, the Edge Processing architecture offers funding for future information storage and statistical methods from a cloud computing. The following is a description of the architecture elements.

PROXIES ACQUISITION

Sensors and devices interact with various communications techniques and devices. The proxy are a set of operating systems, device commands and keys for networking applications such as 3G, Wi-Fi, BLE etc. This stuff relating that a number of IoT devices would connect to the cloud methodology of Edge Computing.

Principles of Access Management.

Virtual Internet Of Things For Local Data Processing

Data validation Metadata annotation Security Actuation Collection proxies

Edge

c

omput

ing

Cloud

computing

Sensors

Actuations

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Figure 1. Architecture edge computing DATA VALIDATION

This is the very first phase in the IoT information collection where certain variables are checked for the sensor data. This is because devices are vulnerable to disruption in the perceived data, used in smart cities, farming, etc. [10]. This is essential. Consequently, it is maintained by verifying and measuring detector results. It is discharged before the data is found to be corrupted. The Edge verification saves space and reduces the demand on a centralized cloud-based infrastructure.

METADATA ANNOTATION

When required information has been authenticated, it is improved by extensive metadata generation information. For instance, the unit, period frame, placement and the sensor's id Number are relevant alongside climate models from an agriculture field. This makes it easy for modern IoT devices to processing information. SECURITY

IoT communities frequently implement a set of restricted devices that are not capable of running complicated data privacy decryption algorithms [12]. In such instances, the Edge Computing framework is set up to encode every AES-256 payload before moving it to a cloud computing system.

VIRTUAL IOT DEVICE

In the state of the art [12] it has thoroughly explored the idea of interactive sensors, their utility for data analysis and taxonomy. We also applied the virtualization principle to actuators and implemented the "virtual IoT device" generic concept (VID). The system definition contains a list of capacitances for activities, resources, and operations to enable data analysis and interaction to sensors. It is defined as: I the virtualized instances from one or more sensors and devices, (ii) the instance hosted on a cloud or edge computing network. We use VID for IoT data care in this paper and help validation rules and annotation of metadata.

ACTUATION

The Edge Computing architecture has a unique benefit because it offers a fast response to the situation via tactility. If such requirements have been met, regional data collection in a VID is complete and the actuation can be activated. For starters, the turn on agriculture irrigation pump. This can be performed locally to boost the organizational dimensions of real-time [8].

2. REVIEW OF LITERATURE

This chapter summarizes the core principles and key distinctions in cloud computing with Edge. Cloud computing is a technological model, which provides users with on-demand applications across a bunch of computers like processing, programming, etc. [5]. Like technology as a service, application as a platform and enterprise software as a cloud computing services. Both these systems offer digital technology on request, such as data processing. The cloud computing also emphasizes on dynamically leveraging the pooled infrastructure between multiple users [1] as well as delivering the above-mentioned services. For eg, the Western user is assigned a cloud storage service depending on his standard time. The same ability is allocated to the Asian customer by cloud computing depending on his/her time zone.

Edge computing data is completely separated from a cloud server to an edge devices for device data, software and services [14]. By supplying consumers with applications nearer to them, cable providers and web developers may use Edge computing systems. The capabilities of edge computing include large speed, ultra-low-latency, and real-time network connectivity, usable for different applications [6]. By opening up to new software and facilities, the service provider will make the radio access network accessible to Edge users. Edge computing offers a range of innovative industry and customer offerings. Edge computing use cases include translation, enhanced fact, and video data analytics caching [9]. As a result, these emerging Edge computing standards and Edge Network implementations becoming crucial for manufacturers, third parties and operators to generate new sales sources.

In [1], Khan et al. presented survey on edge computing. They addressed various frameworks of edge computing, such as cloudlets, handheld edge computing and fog computing. Key edge computing properties are shown. In addition, basic and open science problems are raised for the advancement of cutting edge computing. The role of edge computing in IoT was also addressed in another analysis [7]. In addition, there are various smart implementations, core needs and few open-ended study problems. A critical evaluation of the role of edge computing in IoT networks was performed. The edge measurement was graded in the width, close end and the

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front end according to its architecture. Moreover, the benefits of the IoT architecture focused on edge computing were addressed and research directions problems were identified.

In[4] Pravin Kshirsagar et al, has defined various neural networks to accomplish their aims, such as the activation functions of one layer, layered perceptions, Rbf, Ffnn, neural probabilism networks (Pnn), Grnn, etc. This standard neural network has been considered useful for a limited quantity of data and structures that need modest runtime [15]. But the volume of data to be processed and the system design implementation period are decreased with digital technological advancements. This prompted many researchers to amend the traditional neural networks, which can accommodate a lot of data and have less time to converge [9]. Neural networks may describe or generate the effects of complicated and unreliable data.

In [16], Taleb et al. described the advancement of border-computing, mobile border computing applications, primary mobile border computing officers, the benchmark architecture, difficulties in implementation, and open research questions. Abbas et al. addressed architecture, new smart apps, security / safety as well as open-ended mobile-end analysis issues. The author conducted a safety and secrecy survey of mobile computing. There is also a comprehensive description of methodological paradigms, problems and security threats [10].

The Cloud services accessible on the internet depends on the multi-hop interval here between service provider and the Base station. The substantially broad delay between some of the portable device and the cloud server allowing cloud computing to be extremely latentious relative to the low latency in Edge computing [14]. Cloud computing still has a high degree, whereas Edge has very low levels of jitter. In comparison to the cloud, position and service versatility are situated at Edge computing [12]. Comparison to Cloud computing, Edge computing uses a distributed architecture for the deployment of the database.

In cloud computing, the possibility of information in the routes is greater than in edge computing due to the extended journey of the server [3]. Specific Users are the primary consumers for cloud services while Edge is the higher skill user. The scope of Edge computing is small, unlike with the larger level of cloud computing. Finally, Edge has minimal features that help the device less flexible than Cloud. As per Edge computing, there are various, dispersed heterogenic systems that interact with the network and carry out computational tasks like storing and processing, [11] as an automated computing platform. Similar activities will also help the renting of facilities in which a customer leases a computer and earns rewards.

3. CLASSIFICATION OF EDGE COMPUTING FOG COMPUTING

The core factor of design is the Fog server, which can only be mounted on TV sets, devices or handheld devices. The internet data service is required the Fog server. Every Fog server serves as a message and cloud computing generator. In addition, the network facilitates signals pre-processing, root finding and auto regressive model fitting [12]. The presence of the consumer and the wearable network to the Fog server forms a triangular relation between the nodes. The triangular assembly can be used to improve the program's stability and efficiency. Equipment publication protocols [2] are also enabled by the proposed framework. Device protection is a big concern of the all-round computer network and it is enforced by using the safety procedures on the transport layer.

Edge

computing

Cloudlets

Fog

computing

Big Data Analysis

Application Execution Security &Privacy Resource Management Cloudlet Placement Security &Privacy Resource Management

Big Data Analysis

Collaborative Computing

Mobile Edge

Computing

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Figure 2. Classifications of Edge Computing. RESOURCE MANAGEMENT

In industrial automation, Fog Computing offers a range of services because it allows real-time analyzes that can detect device defects [4]. However, fog nodes have insufficient memory and communication capacity, requiring successful frameworks for utilisation of energy. Implementing Fog virtualization technologies can help maximize the usage of resources and prevent the impact of rivalry of resources. Despite the higher bootup and contract enforcement, virtual machines (VM) are known to be one of the most commonly deployed virtual systems. The findings have shown that the algorithms suggested help to boost the usage of energy with a reduction of latency [14].

SECURITY AND PRIVACY

The multitasking device runs in two stages. During the first step, when a sensory task is needed in a given region, the Central Manager (CM) assigns sensing tasks between involved parties. The participant who executes the algorithms are controlled successfully. Conserving anonymity by brand management however requires extra costs because the reputation manager wants to work reputation values and measure the members' rating of differences. The signaling role needs to be encrypted and delivers its sensory data to the CM [6].

4. EVALUATION OF EDGE COMPUTING

We take the historical evolution and characteristics of numerous paradigms of the computer industry, including computer systems, miniature processing, systems administration computer technology, cloud computing, MCC, and edge computing. Like [4], two paradigms are taken in this paper to expand our results through functionality and dialectics.

METRICS

In order to test various computational concepts, we identify six performance measures: atomicity, versatility, cost improvement, integration, bandwidth and accessibility assistance.

▪ Atomicity: This parameter reflects a device's willingness to extend its offerings to satisfy rising consumer demands.

▪ Versatility: The program's potential to have elastic data centers.

▪ Cost Improvement: This applies to a service's capacity to offer consumers with computational services on availability for cost savings. It also applies to the costs of purchasing computational group consisting with devices and network.

▪ Integration: Cloud upgrades can be done despite client end involvement.

▪ Bandwidth: This factor is for calculating the cumulative runtime of strategic infrastructure frameworks. ▪ Accessibility Assistance: It focuses on delivering the opportunity to run IoT-based programs effortlessly. EVOLUTION

The figure described describes the revolutionary development of cutting edge computing with other computing innovations, including such micro, computer systems technology, customer database, desktop cloud and mobile cloud computing (MCC). More information is given below.

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Figure 3. Evolution of Edge Computing

MAINFRAME, MINI, AND CLIENT-SERVER COMPUTING

Mostly in early days of the internet, mainframes like ENIAC, Markl, BINAC, Whirlwind, UNIVAC, IBM 701 and IBM 360 were used mainly for the production of large bandwidth. In comparison, IBM-developed microcomputers provide shorter then computer systems workstations and laptops for research purposes. they are most often used as semi computers. User technology is focused on supplying services to customers on demand - separate from mainframe computers and mini computes’.

DESKTOP CLOUD COMPUTING

When the various organizations continued to be using the concept of cloud computing to deliver resources for end-users [13], they were more involved. In 1999, the sales people introduced cloud computing effectively to offer Web service providers. The end-users were able to import a request from a distant location on request in an expense manner. In 2002 Amazon provided the web-based shopping site. Amazon began offering on-demand internet services to its customers in 2006.

The European Telecommunication Standards Institute (ETSI) adopted the term Mobile Edge Computing, which permits the use of computer technology tools on the network and mobile telecom network. In Table 1, the distinctions among cloud computing, mobile edge computing, cloud and fog computing are described. In its main context, telephone networks, mobile edge computing varies from cloud and fog computation. Mobile edge computing has the greatest value in terms of context - aware although cloud computing is not context conscious. Fog and cloudlets are mildly and lowly conscious of the background.

5. DESIGNING AI TO THE EDGE

The biggest explanation for AI and edge computing is that, given the gradual growth in its computer scale, cloud computing is too centralized to address all of their issues with increasing the number of smaller edge systems and modern machining [3]. There will be several players in the potential to know the statistics generated by a number of situations, who can answer different forms of data, technology or request.

The products gained by the integration of AI into edge computing are:

▪ The AI on the outside reacts pretty easily. Particularly in comparison to a computer-based mobile cloud, it operates on almost real time to improve consumer trust or kindness.

▪ Enhanced protection can be accomplished by mitigating the probability of decreasing the set of processes or measures for data transfer/receipt [5].

You may explain the architecture of an AI-based e-computing environment below. As seen below, certain features such as large network connectivity, on-demand self-support, efficiency, certifying and resources redistributing in a web used throughout cloud technology are primarily tasked with providing a particular customer. Even then, the architecture of big data in the cloud can be expanded to perform the role of a central framework that can conduct a seamless AI study in a leading system. A computer can also be deployed.

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Figure 4. A Design of Edge Computing Environment Supported By AI.

It is therefore possible to run a design for classification tasks in a model environment where inferences can then be locally sourced by using before the cognitive development and information can be communicated between these smart sensors. Therefore, a significant role for IA engineering was already performed by edge computing to slowly penetrate our daily lives and minimize the teaching technique or responding rapidly. APPLICATION FIELDS AND TECHNOLOGIES

Current technology can be established by edge computing and innovative materials are being based entirely on cutting-edge computing. This paper explains innovations for edge computing and also how edge computing can alter the technologies.

CLOUD OFFLOADING

Most part is measured in the cloud, and this takes longer than edge computing (for example, long tail waiting time). Data apart in a mobile private cloud has been discussed as a side of the renewable energy balance. Edge estimation has a certain computing asset on its edges and offloads a certain portion of the job in the cloud. The advent of distributed systems has led to the emergence of several techniques that allow tasks to be separated into many geographic areas.

VIDEO CONTENT ANALYSIS

The study of visual scenes is based on VCA, also called the intelligent video analysis (such as CCTV images). The VCA's target is the comprehension of image sequences such that substantial data can be gained from of the video sequence, translated and extracted. VCA uses are video searching, human tracking and monitoring, irregular behavior detection, recording and traffic control of real-time cars, security, image recognition and fire detection.

The following are the details for the performance of the VCA. EASE OF DEPLOYMENT

The device users can quickly customize the VCA program and can perfectly change the output calculation and framework variables. The platform should really be simple for consumers in using, particularly while video files sources, such as alerts and triggers, are continuously tracked.

COMPUTATIONAL EFFICIENCY

This demonstrates the opportunities to realize good accuracy and to produce better efficiency and flexibility despite raising the load of video stream research works in many respects. This changes the CPU and scalability of the video stream with expanded limited form, frequency and signal power.

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REAL-TIME PROCESSING

This feature refers to the actual reactions to several other apps' scenarios. For instance, the required analysis should be completed in a short period of time and a particular alert should be set up to warn the individual involved about both the event in order to evaluate the video path and take action required to stop criminals. AUTONOMOUS VEHICLE

Automatic vehicles are automobiles which, without human involvement, understand the road conditions, assess threat, prepare a travel path about themselves and run. It consists of hazard detection equipment, central command structures and sensors, and uses the latest advanced instruments such as automation and computing technology, a social economy of mapping, terrain, accurate detectors and power conditioning. The requisite tasks of self-contained automobiles are split into two major functions: threat identification and driver assistance.

6. RESEARCH CHALLENGES

This section presents open research challenges in the realization of edge computing in smart cities [15]. Challenges, their causes, and possible guidelines is given in below table.

Issues Source Instructions

Smart Cache. 1. Transport Reliability.

2. Increased Congestion In The Back-Course.

3. Good Information Supply Latency.

1. Deep Cache Database Predicting Aided Learning.

2. Strengthening Mobility-Conscious Learning Caching. Computing Collaborative Edge. 1. Low Processing And Storage

Space Limits Embedded In Edge Server.

2. The Scale, Quantity And Availability Of Edge Servers Reflect On Benefit And Qos. 3. Lack Of Increased Reliability And Trust.

1. Collaboration Of Horizontal Edge Processors.

2. Collaboration Of Vertical Edge Machines.

3. Collaboration Between Domains. 4. Collaboration Intra-Domain. 5. Intelligent Networking Collaborative.

6. Design Of Rewards For Cooperation Concerned With Social Trust.

Smart Edge

1. Lack In Big Data Processing Instantaneously.

2. Explosion And Independent Judgments With High Latency. 3.Non-Interoperability Of Semantics

1. Diagnostic Analytics And Predictive Based On AI.

2. Integrated Interfaces Focused On Artificial Intelligence.

3. Fresh Regulations On Sensitive Data Sets In Terms Of Privacy. Slicing Of The Network. 1. Various Smart City Frameworks

Criteria.

2. Most Network Operators Lack Good Capital Control.

3. Challenges For Safety Slice.

1. Monitor Plane 2.Differentiation. Data Plane Separation.

3. Centralized Communication End-To-End Divisions.

4. The Orchestrator Protection Dependent On SDN.

5. Chaining Of The Flexible Service Feature.

Table 1: Research Challenges and Their Guidelines CASE STUDY-SMART CITY

In the Smart City, various sorts of electronic monitoring detectors used to provide the needed data for strategic property and risk allocation. This involves information collected from people, facilities and properties and stored or analyzed for road and communications infrastructure, electric utilities, water infrastructure, waste disposal, law enforcement, electronic systems, colleges, libraries, medical and other municipal resources to track and regulate [18]. This requires information compiled from of the public. The smart city idea entails combining internet and ICTs that require numerous electronic components access to the internet to maximize the performance of municipal activities and utilities and to communicate with residents.

Scalability & Reliability 1. Resilient to Hardware Failures

2. Resilient to Software Flaws

Sustainability

1. Hybrid Energy Sources 2. Energy Harvesting

3. Renewable Energy Sources 4. Energy Efficient Design

Resources Management 1. User-defined Utility based Resource Management 2. Adaptive Resource Management

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Figure 5. Edge Computing Enabled Smart Cities Requirements

Edge analyzes continuously measure and analyze results obtained by using edge analysis algorithm from detectors, access points and other tools to classify knowledge importance and transfer or discarded data on the cloud. This decreases the linked phone's choice time.

Applications Of Smart City

Delay Reliability Time Complexity

Health Monitoring System

Real Time High Up To Minutes

Vehicle Tracking Low Low Up To Minutes

Transportation Systems. Medium Medium Up To Minutes

Power Plants Real Time Low Up To Seconds

Water Supply Networks Real Time High Up to Hours

Waste Management High Medium Up to Days

Law Enforcement High Low Up to Week

Information Systems Real Time Low Up to Seconds

Schools, Libraries High Medium Up to Week

Other Community

Services

High Medium Up to Days

Table 2: Analysis on different applications using Edge Computing

Edge analysis alone is not enough to realize the smart city with state-of-the-art intelligence. Edge Intelligence incorporates state-of-the-art computer vision technology, which will save time and effort by minimizing energy storms and connectivity gaps via having nearer datasets by data preparation and decisions. In evaluating all the data on the slope and all the steps taken to improve the standard of living of people, the importance of edge computing in the smart city is tremendous.

7. CONCLUSION

Edge Computing is innovative field, there is plenty of space for progress to achieve effective cloud applications. Those services are converted through network to cloud by data acquisition on borders, decreasing connection speeds, and increasing efficiency. Cloud delays, procedures, and data attacks are the review key that will tackle these problems using state-of-the-art computer technologies. This survey examines thoroughly the implementation of edge computing in clever towns. We also introduced the development of advanced informatics for the first time, addressing the evolutionary changes of informatics technology to advanced informatics. The technologies concerned are also discussed, along with the advantages of multiple measurement frameworks. Secondly, substantial recent progress has been made and a thorough review of various performance criteria has been carried out.

Edge Enabled Smart

City Requirements

Context-awareness 1. Distribution 2. Network Load and Capacity

3. Smart Device Elasticity

1. Prediction of User Demands 2. Elastic Services Security 1. Decentralized Security 2. Cyber Security Interoperability 1. Interoperable Interfaces 2. Open Source Frameworks Business Model 1. Pricing Model 2. Services and products 3. Distribution

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8. REFERENCES

[1] Habak, K., Ammar, M., Harras, K.A., Zegura, E., Femto (2015), ‘Clouds: Leveraging Mobile Devices to Provide Cloud Service at The Edge’, IEEE 8th International Conference on Cloud Computing, Pp. 9–16. [2] Pravin Kshirsagar And Sudhir Akojwar (2016), ‘Hybrid Heuristic Optimization for Benchmark Datasets’,

International Journal of Computer Applications, 146(7), Pp.11-16

[3] Ziming, Z., L. Fang, L., and Nong, X. (2018), ‘Edge Computing: Platforms, Applications and Challenges’, Journal of Computer Research and Development, Vol. 55, No. 2, P. 327.

[4] Kshirsagar, P. Akojwar, S. Nidhi, D., Bajaj. (2018), ‘A Hybridised Neural Network and Optimisation Algorithms for Prediction and Classification of Neurological Disorders’, International Journal of Biomedical Engineering and Technology, Vol. 28, Issue 4.

[5] Stolfo, S.J., Salem, M.B., Keromytis, A.D. (2012), ‘Fog Computing: Mitigating Insider data Theft Attacks in The Cloud’, IEEE Symposium on Security and Privacy Workshops, IEEE, Pp. 125–128

[6] Pravin, K., Akojwar, S. (2016), ‘Prediction of neurological disorders using PSO with GRNN’, IEEE SCOPES International Conference, Paralakhemundi, Odisha, India.

[7] Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G., and Vakali, A. (2009), ‘Cloud Computing: Distributed Internet Computing for it and Scientific Research’, IEEE Internet Comput., Vol. 13, No. 5, Pp. 10-13. [8] Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., and Sabella, D. (2017), ‘On Multi-Access Edge

Computing: A Survey of The Emerging 5G Network Edge Architecture & Orchestration’, IEEE Commun. Surveys Tuts., Vol. 19, No. 3, Pp. 1657-1681.

[9] Kshirsagar, P., Akojwar, Ds. (2016), ‘Prediction of Neurological Disorders Using Optimized Neural

Network’, In the Proceeding of International Conference on Signal Processing, Communication- Power and Embedded System.

[10] Liu, D., Zhu, G., Zhang, J., and Huang, K. (2018), ’Wireless Data Acquisition for Edge Learning: Importance

Aware Retransmission’, Corr, Vol. Abs/1812.02030.

[11] Akojwar, SG., Kshirsagar, P. (2016), ‘A Novel Probabilistic-PSO Based Learning Algorithm for Optimization of Neural Networks for Benchmark Problems’, WSEAS International conference on Neural Network.

[12] Wang, K., Yin, H., Quan, W., and Min, G. (Sep 2018), ‘Enabling Collaborative Edge Computing for Software Defined Vehicular Networks’, IEEE Network, Vol. 32, No. 5, Pp. 112–117.

[13] Zhang, H., Dong, P., Quan, W., and Hu, B. (2015), ‘Promoting Efficient Communications for High-Speed Railway Using Smart Collaborative Networking’, IEEE Wireless Communications, Vol. 22, No. 6, Pp. 92– 97.

[14] Kshirsagar, P., Akojwar, S. (2016), ‘Novel Approach for Classification and Prediction of Non-Linear Chaotic Databases’, International Conference on Electrical, Electronics, and Optimization Techniques IEEE. [15] Volkan, G., Jumyung, U., Martin, R. (2018), ‘An Introduction to Edge Computing and A Real-Time Capable Server Architecture’, International Journal on Advances in Intelligent Systems, Vol. 11, No. 1&2, Pp. 105-114.

[16] Zhu, G., Wang, Y., and Huang, K. (2018), ‘Low-Latency Broadband Analog Aggregation for Federated Edge Learning’, Corr, Vol. Abs/1812.11494.

[17] Kshirsagar, P., Deshmukh, V. (2013), ‘National Conference on Innovative Paradigms in Engineering &