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Alotaibi, B. IoT/IIoT and Edge/Fog Computing. Encyclopedia. Available online: https://encyclopedia.pub/entry/49237 (accessed on 07 July 2024).
Alotaibi B. IoT/IIoT and Edge/Fog Computing. Encyclopedia. Available at: https://encyclopedia.pub/entry/49237. Accessed July 07, 2024.
Alotaibi, Bandar. "IoT/IIoT and Edge/Fog Computing" Encyclopedia, https://encyclopedia.pub/entry/49237 (accessed July 07, 2024).
Alotaibi, B. (2023, September 15). IoT/IIoT and Edge/Fog Computing. In Encyclopedia. https://encyclopedia.pub/entry/49237
Alotaibi, Bandar. "IoT/IIoT and Edge/Fog Computing." Encyclopedia. Web. 15 September, 2023.
IoT/IIoT and Edge/Fog Computing
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This entry is adapted from the peer-reviewed paper 10.3390/s23177470

The Internet of Things (IoT) can be defined as a paradigm that utilizes intelligent devices that can communicate through the internet. The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected by an IoT network, along with a collection of enabling technologies, such as artificial intelligence (AI) and edge/fog computing. Edge computing is an enabling paradigm that exclusively processes data on the network’s edge. Fog computing is another emerging technology that enables edge devices (i.e., end devices and edge platforms) to perform additional computations, handle data, and allocate network resources.

Internet of Things fog computing edge computing Industrial Internet of Things Industry 4.0

1. Introduction

The Internet of Things (IoT) can be defined as a paradigm that utilizes intelligent devices that can communicate through the internet [1][2]. IoT environments comprise many intelligent devices capable of collecting, processing, transmitting, and receiving data from each other [3]. These interconnected intelligent devices help us to monitor any environment and precisely control any setting [4]. By 2025, the total economic impact derived from IoT technology annually is predicted to reach USD 11.1 trillion [5]. As most of the IoT systems developed so far are consumer-centric, their nature has enabled the adoption of this technology in many industrial applications, creating the so-called IIoT technology [6]. IIoT, also known as industrial internet, can be defined as a paradigm that utilizes interconnected intelligent devices deployed in an industrial environment, in order to connect industrial components, including actuators, sensors, controllers, and intelligent control systems (i.e., for data analysis and industrial process optimization to enhance the speed of execution, decrease costs, and manage the industrial setting dynamically) [7].
As shown in Figure 1, Industry 4.0—also known as the fourth Industrial Revolution—exemplifies an unprecedented industrial evolution and complements various emerging technologies and systems, such as CPS, MCC, IoT, AI, CC, big data, and fog computing, in order to improve the adequacy of industries, in terms of heterogeneous data support, automation, high production, and integrating knowledge [8][9]. The number of embedded systems utilized in industrial applications has swiftly grown, due to the mounting availability, capability, and affordability of sensors, communication modules, and processes [10]. This has driven more interest regarding the use of IIoT in industrial domains such as smart cities, transportation, healthcare, microgrids, and smart factories, giving rise to Industry 4.0 based on CPS. By 2030, IIoT has been forecasted to be worth USD 7.1 trillion in the U.S. and to exceed USD 1.2 trillion in European countries [11].
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Figure 1. Industry 4.0 utilizes various emerging technologies to improve industrial production.
Despite all of the advantages of adopting IIoT, IoT security issues represent one of the biggest challenges hindering its perfect utilization. The poor security associated with IoT devices [12][13] makes them vulnerable to cyber-attacks (e.g., IoT devices could be targeted by adversaries to execute devastating attacks, such as DDoS) [14]. Thus, they may be susceptible to various cybersecurity threats, causing IIoT security to become a hot topic in recent years [15]. IoT also relies heavily on the CC to provide the IoT devices with limited capabilities for the desired services [16]; however, this dependency transports diverse vulnerabilities to IoT environments [17].
In this context, an emerging computing technology, known as fog computing, has attracted the attention of the research community [18]. Fog computing is a new paradigm that bridges the gap between CC and IoT by diffusing services and resources on the path between IoT environments and CC [19]. Fog computing has several advantages, which can facilitate the secure deployment of IIoT devices. However, fog computing may also bring some inherited security challenges to the table [20].

2. IoT and IIoT

Figure 2 depicts the relationships existing between the concepts introduced in this section. Although IIoT originated from IoT it has different focuses, in terms of practical applications and concepts [21], as shown in Table 1. Namely, the IoT has been designed to improve people’s quality of life and is generally considered consumption-centric. Typical IoT application examples include health monitoring, indoor localization, and smart homes [22]. On the other hand, the IIoT endeavors to enhance the production efficiency of industries (i.e., it is considered a production-centric paradigm). Typical IIoT applications include smart manufacturing, smart transportation, remote maintenance, and intelligent logistics [23]. IoT application system frameworks are generally constructed from scratch, and the utilized sensors are deployed within a small area and are not sensitive to precision [6]. High mobility is one of the main characteristics of IoT devices; the generated data of these devices are of moderate size, and delays can be tolerated to a great extent. Meanwhile, IIoT application system frameworks rely on traditional industrial infrastructures. Thus, the sensors are typically distributed over a large area, and the deployment must be highly precise. Conversely, most IIoT devices are distributed in specific locations; the data generated by these devices are large in size, and only slight delays can be tolerated.
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Figure 2. The relationships between CPS, IoT, IIoT, industrial internet, and Industry 4.0.
Table 1. Comparison of the main characteristics of IoT and IIoT.
Characteristic IoT IIoT
Application examples Smart home, health monitoring, indoor localization Smart transportation, intelligent logistics, smart manufacturing, remote maintenance
System Framework Self-reliant Industrial facility-reliant
Delay sensitivity High Low
Mobility High Low
Deployment size Small Large
Deployment preciseness Low High
Data volume Medium High
The IoT terminology relates to other famous concepts, such as CPS, Industry 4.0, and industrial internet. The CPS concept, introduced in 2006 by Helen Gill, involves the thorough integration of several technologies, such as sensing and embedded systems (i.e., combining software and hardware), in order to accomplish efficient internal information exchange, resilient real-time feedback, and positive communication between virtual and physical entities [24]. IoT is regarded as a subset of CPS, which assures communication between diverse objects through the internet, depending on unique identifiers. The IoT is supported by the internet, which provides IoT devices with availability, interoperability, universality, and socialization [25]. Another concept, introduced by the IIC and initiated by five U.S. tech companies (i.e., Cisco, Intel, IBM, AT&T, and GE) is industrial internet, which concentrates on data flow enhancement, innovative network standardization, application, construction, and industrial field automated transformation.
Industry 4.0 was introduced in Germany. This global concept utilizes CPS and emerging technologies, such as AI, IoT (i.e., forming the IIoT idea), big data, and CC, in intelligent manufacturers [26]. To recap, CPS connects objects to link the virtual and physical worlds, while IoT utilizes physical addresses in civilian and industrial settings to facilitate communication between objects. The industrial internet uses emerging technologies to depict the prospect of future trends. In this context, industrial internet and IoT are considered subsets of CPS [27][28], and intersect to form the so-called IIoT. Moreover, Industry 4.0 utilizes IIoT, among other emerging technologies, in intelligent manufacturing settings.

3. Edge and Fog Computing

Edge computing is an enabling paradigm that exclusively processes data on the network’s edge. This occurs between centralized cloud servers and end devices (e.g., sensors, actuators, and controllers). One of the main reasons for initiating edge computing is to bring computations closer to hosts, thus reducing delays. Therefore, edge computing enables data to be transferred from end devices to edge computing (i.e., close-to-end devices) and vice versa, instead of imposing that the end devices interact with cloud servers. Thus, as shown in Figure 3, edge platforms can act as clients and servers; namely, clients to cloud servers, and servers to end devices. Acting as servers, they enable end devices to gain the full benefits from edge platforms that can carry out caching, computational offloading, storage capabilities, and processing [29].
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Figure 3. The interaction between edge platforms; the upper layer (cloud servers) and the lower layer (edge devices).
Fog computing is another emerging technology that enables edge devices (i.e., end devices and edge platforms) to perform additional computations, handle data, and allocate network resources [30]. Thus, fog computing is not far from the end devices and enables the end devices/edge platforms to carry out most services (e.g., data handling, storage, network resources utilization, and processing) that cloud services can afford [31]. Therefore, edge and fog computing enable delay-sensitive end-device applications to carry out various services in real time. These two emerging technologies have become a viable supplement to CPS and applications in IIoT environments. The following requirements are satisfied by edge and fog computing:
  • System performance enhancement: Data processing can be achieved at the network’s edge, improving the system performance of end devices. Edge platforms can accomplish data processing in milliseconds, reducing the latency and communication bandwidth demand, thus enhancing the system’s performance.
  • Data security and privacy protection: Edge and fog computing can reduce privacy and security risks, as they transmit and store data in decentralized devices (i.e., near-end devices), as opposed to cloud platforms, which provide centralized data protection solutions. Additionally, data leakage at centralized cloud servers affects many end devices, compared to data leakage at edge/fog devices, involving only a limited number of devices (i.e., the end devices nearby that obtain services from edge/fog platforms).
  • Operational cost reduction: When end devices transfer data directly to the cloud, the operational costs related to migrating data, maintaining good bandwidth, and shortening delays are increased. On the other hand, when edge/fog platforms are utilized, the data migration volume, delay, and bandwidth consumption are decreased, leading to reduced operational costs.

References

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