Edge Computing: Vision and Challenges Shi, Weisong; Cao, Jie; Zhang, Quan ...
IEEE internet of things journal,
10/2016, Volume:
3, Issue:
5
Journal Article
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the ...edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature ...sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.
The Industrial Internet of Things (IIoT) that introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to ...collect the data of industrial devices. These data are analyzed to improve the manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal ciphertext. Therefore, encrypted data sharing should be implemented to analyze the devices' data. In this article, the cloud storage solution for sensors is considered. To achieve a secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication first. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with a less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.
Deterministic networking has recently drawn much attention by investigating deterministic flow scheduling. Combined with artificial intelligent (AI) technologies, it can be leveraged as a promising ...network technology for facilitating automated network configuration in the Industrial Internet of Things (IIoT). However, the stricter requirements of the IIoT have posed significant challenges, that is, deterministic and bounded latency for time-critical applications. This article incorporates deep reinforcement learning (DRL) in cycle specified queuing and forwarding and proposes a DRL-based deterministic flow scheduler (Deep-DFS) to solve the deterministic flow routing and scheduling problem. Novel delay aware network representations, action masking and criticality aware reward function design are proposed to make deep-DFS more scalable and efficient. Simulation experiments are conducted to evaluate the performances of deep-DFS, and the results show that deep-DFS can schedule more flows than the other benchmark methods (heuristic- and AI-based methods).
As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial environment has generated a significant amount of network traffic. In this type of IoT industrial ...network where there is a large equipment heterogeneity, security is a fundamental issue; thus, it is very important to detect likely intrusion behaviors. Furthermore, since the proportion of labeled data records is small in the IoT environment, it is challenging to detect various attacks and intrusions accurately. This investigation builds a semisupervised ladder network model for intrusion detection in the Industrial IoT. This model considers the manifold distribution of high-dimensional data and incorporates a manifold regularization constraint in the decoder of the ladder network. Meanwhile, the feature propagation between layers is strengthened by adding more cross-layer connections in this model. On this basis, a random attention-based data fusion approach is proposed to generate global features for intrusion detection. The experiments on the CIC-IDS2018 dataset show that the proposed approach can recognize the intrusion with less false alarm rate, while model training is time efficient.
Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still ...faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.
The growing convergence among information and operation technology worlds in modern Industrial Internet of Things (IIoT) systems is posing new security challenges, requiring the adoption of novel ...security mechanisms involving light architectures and protocols to cope with IIoT devices resource constraints. In this article, we investigate the adoption of physically unclonable functions (PUFs) in the IIoT context, and propose the design of a PUF-based architecture (Pseudo-PUF), obtained by suitably combining a weak PUF and an encryption module, that can be successfully adopted to implement advanced security primitives while meeting the existing requirements of IIoT devices in terms of cost and resource demand. To demonstrate the feasibility of our proposal, we analyzed the overall quality of different Pseudo-PUF instances with respect to well-known PUF quality metrics, and found that it is possible to obtain good results with a negligible impact on the devices, thus making our approach suited to IIoT deployments.
The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data ...acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.
The 21st century is witnessing a fast-paced digital revolution. A significant trend is that cyber and physical environments are being unprecedentedly entangled with the emergence of Internet of ...Things (IoT). IoT has been widely immersed into various domains in the industry. Among those areas where IoT would make significant impacts are building construction, operation, and management by facilitating high-class services, providing efficient functionalities, and moving towards sustainable development goals. So far, IoT itself has entered an ambiguous phase for industrial utilization, and there are limited number of studies focusing on the application of IoT in the building industry. Given the promising future impact of IoT technologies on buildings, and the increasing interests in interdisciplinary research among academics, this paper investigates the state-of-the-art projects and adoptions of IoT for the development of smart buildings within both academia and industry contexts. The wide-ranging IoT concepts are provided, covering the necessary breadth as well as relevant topic depth that directly relates to smart buildings. Current enabling technologies of IoT, especially those applied to buildings and related areas are summarized, which encompasses three different layers based on the conventional IoT architecture. Afterwards, several recent applications of IoT technologies on buildings towards the critical goals of smart buildings are selected and presented. Finally, the priorities and challenges of successful and seamless IoT integration for smart buildings are discussed. Besides, this paper discusses the future research questions to advance the implementation of IoT technologies in both building construction and operation phases. The paper argues that a mature adoption of IoT technologies in the building industry is not yet realized and, therefore, calls for more attention from researchers in the relevant fields from the application perspective.
•The common technologies of Internet of Things (IoT) used in the building industry on a layering basis are summarized.•The potentials of IoT technology application towards the development of smart buildings are recognized and highlighted.•An outline for developing IoT architecture to implement critical functionalities of smart buildings is provided.•Current trends and priorities, and future research areas of IoT application in the building industry are presented.
In the era of Industry 4.0, the Internet-of-Things (IoT) performs the driving position analogous to the initial industrial metamorphosis. IoT affords the potential to couple machine-to-machine ...intercommunication and real-time information-gathering within the industry domain. Hence, the enactment of IoT in the industry magnifies effective optimization, authority, and data-driven judgment. However, this field undergoes several interoperable issues, including large numbers of heterogeneous IoT gadgets, tools, software, sensing, and processing components, joining through the Internet, despite the deficiency of communication protocols and standards. Recently, various interoperable protocols, platforms, standards, and technologies are enhanced and altered according to the specifications of the applicability in industrial applications. However, there are no recent survey papers that primarily examine various interoperability issues that Industrial IoT (IIoT) faces. In this review, we investigate the conventional and recent developments of relevant state-of-the-art IIoT technologies, frameworks, and solutions for facilitating interoperability between different IIoT components. We also discuss several interoperable IIoT standards, protocols, and models for digitizing the industrial revolution. Finally, we conclude this survey with an inherent discussion of open challenges and directions for future research.
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