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Edge Devices In Marine Industry For Real-time Ship Monitoring

These feature works represent the most advanced research in the field. A feature paper should be a substantial original article that includes several techniques or methods, provides an overview of future research directions, and describes applications of the research.

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Received: 5 October 2021 / Revised: 15 November 2021 / Accepted: 29 November 2021 / Published: 3 December 2021

In this paper, we present a comprehensive survey of time-sensitive applications deployed in fog computing environments. The aim is to investigate what applications are implemented in the fog computing architecture and how the timing requirements of these applications are met. We also comprehensively analyzed the reviewed articles and categorized them according to their patterns. Our research is very important for real-time systems because the concept of real-time feedback systems presents different perceptions and concepts. This change in concept is due to the increasing need for fast data communication and processing. Therefore, we present different concepts of real and near-real-time systems that can be found in the literature and are currently accepted by the scientific community. Finally, we analyze the article’s features and recommendations.

The widespread use of Internet-connected devices has expanded the IoT approach, changing people’s lives and lifestyles with advanced technologies. For example, in the smart home, the deployment of IoT devices aims to provide greater interoperability between home appliances. Health devices provide older or disabled people with more supervision and therefore more independence for their activities. As a result, IoT has become an important topic in the field of technology industry and science [1].

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However, IoT applications require environments that are often not present in cloud computing, such as support for mobility and geographic distribution, location awareness, low latency, and fast response to robust decision making. To meet these requirements, Bonomi et al. [3] proposed an intermediary platform called fog computing that provides computing, storage, and networking services between edge devices and cloud computing data centers.

Much of the data generated by IoT applications requires time-sensitive processing. In IoT-based applications, requirements such as low bandwidth and fast processing have emerged as key features of specialized applications, which have given rise to many definitions of real-time response systems. In this paper, we present a survey on the application of fog computing to time-sensitive applications. The aim is to investigate what applications are implemented in the fog computing architecture and how the timing requirements of these applications are met. We also performed bibliometric analysis to verify certain characteristics present in the selected articles, such as: countries and publishers that produced more publications; Types of articles published and number of publications per year. Finally, we highlight lessons learned.

The main motivation behind this research is the community’s understanding of the real-time and fog computing/edge computing concepts. In addition, it tries to analyze the most common applications for future job development.

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The paper is organized as follows: Section 2 introduces the concepts of edge computing, fog computing, and edge and fog computing, as well as materials and methods for various real-time and near-time concepts. . presented in the literature. In addition, we describe the protocol proposed and used for the study, analyze the selected articles, and categorize them according to the concepts used in real-time. Section 3 summarizes the articles by category. In Section 4, we outline lessons learned, provide an analysis of selected articles, and provide directions for future work. Finally, Section 5 summarizes the paper and suggests future work.

In this section, we present the materials and methods used to conduct our research. In the context of this paper, we discuss materials that describe the concepts and characteristics of the methods and techniques that underlie our research. Research methodology describes in detail the methods used to search for, select, and analyze articles.

This section briefly introduces concepts related to edge computing, fog computing, and real-time computing. These concepts are important because they provide an overview and understanding of the system, as well as how to structure the environment that these methods represent. The goal is better understanding, as different definitions can be found in the literature on edge computing, fog computing, and real-time topics. Therefore, we present the difference between fog and edge computing, as well as three different concepts of real-time systems.

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Over the past few years, the scientific community has needed to define new concepts for applying new computational models that drive the IoT worldview. The concept of edges is a new set of computational models. As they form the basis of several new applications, it is worth understanding them clearly.

There is no single defined definition of an edge computing terminal. Now one can find a bias that conceptualizes edge and distinguishes it from fog computing. (1) Edge computing is differentiated by user devices, and fog computing is an intermediate layer between edge and cloud computing. (2) Edge computing is considered a source of communication, computation, control, and storage close to devices and end users. In this concept, there is no need to include the fog layer because the edge and cloud do all the communication and processing [5].

For the purposes of this article, an edge refers to devices, sensors, or other data sources at the edge of a network. At the heart of the network is the data center, the cloud that processes all the data that arrives there. The management layer that manages the data between the cloud and the edge is the bottom part of the cloud [6].

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Therefore, edge computing cannot be imagined as a data center or a simple sensor that converts analog to digital and collects/sends data. Edge computing is conceptualized as a layer consisting of data providers (sensors) and mobile devices next to sensors/actuators with processing capabilities such as smartphones, tablets, and PDAs [8].

The layer between the end devices and the cloud, also called the edge layer, can be implemented in different ways, with some differences: intermediate nodes, networks, and devices that act as communication protocols used by the edge layer. as well as services provided by the edge layer [9]. Based on this context, the paper shows that three different implementations are possible: fog computing, mobile edge computing, and cloud computing. The first concerns the use of M2M devices such as gateways and wireless routers by fog computing nodes. Second, the mobile edge proposes the use of mobile networks as base stations for intermediate nodes with computing, storage, and processing capabilities. Under Cloudlet, the authors advocate the use of specialized equipment with the same capabilities as a data center, but at a lower scale than in a customer environment.

Finally, it should be emphasized that fog computing is a distributed method that derives from its edge computing nature and the need for a centralized approach to cloud computing.

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Fog computing is a platform that provides processing, storage, and networking resources between edge devices and cloud computing data centers [11]. Thus, fog computing can be seen as a complementary extension of cloud computing

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