Edge-Computing Architectures for Internet of Things Applications: A Survey

Abstract

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

A Fully Open-Source Approach to Intelligent Edge Computing: AGILE’s Lesson

“…Edge computing, in its most general meaning, pushes memory and computational power out of traditional data centers, getting them as close as possible to the location where they are needed. Often, this means personal devices or everyday home appliances, hence realizing the original vision of the consumer IoT [ 28 ]. However, it can also mean advanced industrial equipment or, more generally, physical units that are distributed across different industrial IoT factories in the future [ 31 ].…”

“…Users can visualize and manage their data, create reports, and export the data from the gateway or even import past data from other cloud sources. Furthermore, based on the specific goals for each user and the collected data, the application produces personalized messages and recommendations [ 28 ].…”

Real-Time Compression for Tactile Internet Data Streams

“…One potential approach is to explore flexible low-delay network coding that may interleave the tactile data stream with related data streams [ 53 , 54 , 55 , 56 ]. A broad future research direction is to explore how tactile data stream compression can cooperate with distributed computing, e.g., through multi-access edge computing or fog computing [ 57 , 58 , 59 , 60 ], of the tactile signals close to the sensing and actuation sites to achieve ultra-short round-trip computing for tactile applications.…”

Method to Increase Dependability in a Cloud-Fog-Edge Environment

“…In view of the foregoing, we may state that to provide reliable IoT solutions, efficient and scalable resource delivery where and when necessary, the two paradigms need to merge, and Cloud-Computing features are critical to IoT. Therefore, new security issues arise [ 7 , 8 ], but the security research of Cloud-Computing systems is far from being mature [ 9 ]. What should be mentioned is that the specific security features in the Cloud are not yet known.…”