1*BISITE Research Group, University of Salamanca, Salamanca, SPAIN
2Air Institute, IoT Digital Innovation Hub, Salamanca, SP
3Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka, JPN
4Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Kelantan, MY
Corresponding Author E-mail: [email protected]Article Publishing History
Article Received on : 03-May-2019
Article Accepted on : 22-May-2019
Article Published : 24 May 2019
Edge Computing (EC) is an emerging technology that has made it possible to process the large volume of data generated by devices connected to the Internet, through the Internet of objects (IO). The article provides an introduction to EC and its definition. The integration of EC in those contexts would imply an optimisation of the processes that are normally executed in a cloud computing environment, bringing considerable advantages. The main contribution of EC is a better pre-processing of the data collected through devices before they are sent to a central server or the cloud.KEYWORDS:
Cloud Computing; Edge Computing (EC); Industry 4.0; Internet Of Things; Reference ArchitectureDownload this article as:
|Copy the following to cite this article:
Candanedo I. S, Corchado J. M.An Edge Computing Tutorial. Orient. J. Comp. Sci. and Technol; 12(2).
|Copy the following to cite this URL:
Candanedo I. S, Corchado J. M.An Edge Computing Tutorial. Orient. J. Comp. Sci. and Technol; 12(2). Available from: https://bit.ly/30JBOqn
The Internet of Things is a network or an interconnection of devices, sensors, or actuators that share information through a unified protocol. The devices use ubiquitous sensing, data analysis, information representation and the same framework to achieve this. A standard IoT network consists mainly of radio frequency identification devices (RFID) and Wireless Sensor Networks (WSN). This type of IoT network has an important number of challenges that are difficult to overcome in smart scenarios, such as: logistics, home, city, Industry 4.0 or finance-related challenges.1–3
Until very recently, cloud computing was considered the traditional approach to meeting the requirements of the Internet of Things.1,4 Cloud computing is defined as a model that allows for ubiquitous, convenient, on-demand access to a shared set of configuration computing resources (e.g., networks, servers, storage, applications and services) that can be quickly provisioned and released with minimal interaction between the management centre and the service provider.5,6 The approach of using cloud computing as a centralized server, generally geographically distant, increases the frequency of communications between the peripheral devices used by users (tablets, computers, wristbands or smartphones) becoming a limitation for applications that require a real-time response.
This challenge has given rise to Edge Computing (EC) as an emerging technology that allows applications to run on network nodes.7 In EC, the nodes can be centralized, distributed (core) or at the end of the network, in this last case they are called “edges”, allowing for a more distributed processing of all the information generated by the peripheral devices. Thewidespread interestin this technology is due to its associationwith the Internet of Things (IoT) and its disruption in diferent scenarios, as a result of the number of devices that can be connected to the Internet, generating data and requiring organizations to improve their productivity through the administration and analysis of these data.8
Consequently, lines of research have emerged to address edge computing, its challenges, oportunities and application scenarios. EC is defined by several authors as a set of devices, sensors, computer resources and computers that produce and collect data that are then sent to cloud centers. They approach the concept of edge computing in terms of its architecture, challenges, software technologies, benefits and capabilities.9–11 Some of the most commonly used edge computing concepts are presented as follow:
- Edge Computing is a technology that allows to perform computation at the network edge so that computing happens near data sources. In Edge Computing, the end device not only consumes data but also produces data.12
- Edge Computing is a new paradigm in which substantial computing and storage resources, also referred to as cloudlets, micro datacentres or fog nodes, are placed at the Internet’s edge in close proximity of mobile devices or sensors.13
- Edge computing refers to the enabling technologies that allow for computation to be performed at the network edge so that computing happens near data sources. It works on both downstream data on behalf of cloud services and upstream data on behalf of IoT services.14
In the author’s opinion, the most precise definition of Edge Computing is that established by the Edge Computing Consortium: Edge Computing is a distributed open platform at the network edge, close to the things or data sources, integrating the capabilities of networks, storage, and applications. By delivering edge intelligence services, Edge Computing meets the key requirements of digitisation for agile connectivity, real-time services, data optimisation, application intelligence, security and privacy protection.15 This definition establishes what organizations currently demand: “platforms capable of processing data in a secure and private manner, providing answers to users in real time”.
IoT applications and services must be able to support heterogeneous devices that generate large volumes of events and data. This feature makes it difficult to find the development specifications that would take advantage ofall IoT potential. Considering these concepts,Edge Computing increases IoT performance with its distributed structure, likewise network traffic can be significantly minimized; latency transmission between the edge node the cloud and end users can be improved; and thereforethe real-time response ofIoT applications compared to cloud and fog computing.
Table 1: Difference between Edge and Cloud Computing16
|Advantages|| Real-time response.|
Edge can work without cloud and improve data security.
The EC distributed structure reduces: network traffic, storage and band with a cost.
Big Data processing.
Unlimited storage capacity.
|Disadvantages|| Storage capacity is limited |
EC needs proprietary networks.
IoT devices have high power consumption.
|Response time is slow.|
Cloud does not have an offline mode.
Difficult to maintain the security of data.
High costs of data storage and transmission.
Table 1 resumes the main differences between EC and Cloud Computing, despite with all the advantages mentioned above, it is important to clarify that the process of increasing the processing or computing capabilities of IoT devices located at the edge of the network, using EC, does not replace the functions performed by Cloud services. In this regard, is important to note that cloud and edge computing are very different technologies which complement each other making it possible to deploy resources with ubiquitous accessibility. However, even when working together, they face the challenges of mobility, scalability, reliability, security, privacy or limited energy.
Figure 1 shows an edge computing ecosystem, based on the work of W. Yu, F. Liang, X. He, Hatcher, W G., Lu, C., Lin, J., Yang, X. (2017).16 It supports how these two technologies complement each other by integrating to more efficiently manage this flow of information. In this sense, the devices need to be managed and the data collected needs to be analyzed and this requires a coordination of the cloud with the network.
Figure 1: Edge and Cloud Computing.16
Figure 1 components are described as follows:
Devices and sensors: responsible to generate and collect data. This group of devices interact directly with the end user (sensors, smartphones, tablets, smart bracelets or laptops) and although some offer services and answer in real time, most of them have a limited capacity. Therefore, they need to send requests to equipment located on the Edge infrastructure.
Edge infrastructure: there are distributed data centres to provide real-time data processing, data visualization, analytics, filtering, optimization. They were located closer to end users, they process, cache storage, and perform calculations for a large volume of data. With this capability, the edge reduces data flow and costs of using cloud services, as well as reducing end-user response time and latency.
Cloud: It offers a greater density of computing, storage, networking resources. Cloud servers host applications for automatic learning, big data analysis and business intelligence.
Finally, edge computing is a new paradigm that promises to provide the required computing and storage resources with a decrease in delays due to its “proximity” to end users or devices. This tutorial included a state of the art in the field of EC with the objective of guiding readers towards current trends, challenges and future research opportunities in the area of edge computing.
This work is an edge computing state of the art review, which is a disruptive technology driven by the development of the Internet of Things and the devices of our environment permanently connected to the Internet. IoT devices generate data in real time and constantly. The growing number of sensors, connected machines, geographic heterogeneity for data storage, requests for real-time response have given rise to Edge Computing. The main advantages of edge computing as following: real-time analysis of data at the level of local devices and edge nodes and not necessarily in the cloud; reduction of operating costs, traffic and data transfer between the Edge and the cloud; increase the performance of applications for IoT scenarios by reducing network latency; and finally allows integration with Blockchain technology for security. As future lines of research, the authors propose the design of an edge computing reference architecture for IoT scenarios.
This research has been partially supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing” (Id. RTI2018-095390-B-C32). Inés Sittón-Candanedo has been supported by IFARHU – SENACYT scholarship program (Government of Panama).
Conflict of Interest
We dont have conflict of interest including any financial, personal or other relationships with other people or organizations that can influence in our work.
- Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29, 1645–1660 (2013).
- Ganz, F., Puschmann, D., Barnaghi, P. & Carrez, F. A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things. IEEE Internet Things J.2, 340–354 (2015).
- Premsankar, G., Di Francesco, M. & Taleb, T. Edge Computing for the Internet of Things: A Case Study. IEEE Internet Things J.5, 1275–1284 (2018).
- Risteska Stojkoska, B. L. & Trivodaliev, K. V. A review of Internet of Things for smart home: Challenges and solutions. J. Clean. Prod.140, 1454–1464 (2017).
- Mell, P., Grance, T. & others. The NIST definition of cloud computing. (2011).
- De La Prieta, F. & Corchado, J. M. Cloud computing and multiagent systems, a promising relationship. in Intelligent Agents in Data-intensive Computing (ed. Springer, C.) 143–161 (Springer, Cham., 2016).
- Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P. & Nikolopoulos, D. S. Challenges and Opportunities in Edge Computing. Proc. – 2016 IEEE Int. Conf. Smart Cloud, SmartCloud 2016 20–26 (2016). doi:10.1109/SmartCloud.2016.18
- Garcia, P. et al. Edge-centric computing: Vision and challenges. ACM SIGCOMM Comput. Commun. Rev. (2015); 45: 37–42.
- Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F. & Buyya, R. CloudSim: A toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. – Pract. Exp.41, 23–50
- El-Sayed, H. et al. Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment. IEEE Access6, 1706–1717 (2017).
- Zeng, F., Ren, Y., Deng, X. & Li, W. Cost-effective edge server placement in wireless metropolitan area networks. Sensors (Switzerland)19, (2019).
- Shi, W., Cao, J., Zhang, Q., Li, Y. & Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J.3, 637–646 (2016).
- Satyanarayanan, M. The Emergence of Edge Computing. Computer (Long. Beach. Calif).50, 30–39 (2017).
- Shi, W. & Schahram, D. The promise of Edge Computing. Computer (Long. Beach. Calif).49, 78–81 (2016).
- Edge Computing, C. & (AII), A. of I. I. Edge Computing Reference Architecture 2.0. (2017).
- Yu, W. et al. A Survey on the Edge Computing for the Internet of Things. IEEE Access6, 6900–6919 (2017).