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2021 | Book

Confluence of 4G LTE, 5G, Fog, and Cloud Computing and Understanding Security Issues Read chapter Read first chapter

Fog/Edge Computing For Security, Privacy, and Applications

Editors: Assist. Prof. Wei Chang, Prof. Jie Wu

Publisher: Springer International Publishing

Book Series : Advances in Information Security

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book provides the state-of-the-art development on security and privacy for fog/edge computing, together with their system architectural support and applications. This book is organized into five parts with a total of 15 chapters. Each area corresponds to an important snapshot. The first part of this book presents an overview of fog/edge computing, focusing on its relationship with cloud technology and the future with the use of 5G communication. Several applications of edge computing are discussed. The second part of this book considers several security issues in fog/edge computing, including the secure storage and search services, collaborative intrusion detection method on IoT-fog computing, and the feasibility of deploying Byzantine agreement protocols in untrusted environments. The third part of this book studies the privacy issues in fog/edge computing. It first investigates the unique privacy challenges in fog/edge computing, and then discusses a privacy-preserving framework for the edge-based video analysis, a popular machine learning application on fog/edge. This book also covers the security architectural design of fog/edge computing, including a comprehensive overview of vulnerabilities in fog/edge computing within multiple architectural levels, the security and intelligent management, the implementation of network-function-virtualization-enabled multicasting in part four. It explains how to use the blockchain to realize security services. The last part of this book surveys applications of fog/edge computing, including the fog/edge computing in Industrial IoT, edge-based augmented reality, data streaming in fog/edge computing, and the blockchain-based application for edge-IoT. This book is designed for academics, researchers and government officials, working in the field of fog/edge computing and cloud computing. Practitioners, and business organizations (e.g., executives, system designers, and marketing professionals), who conduct teaching, research, decision making, and designing fog/edge technology will also benefit from this book The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems, but also applies to students in business, education, and economics, who would benefit from the information, models, and case studies therein.

Table of Contents

Frontmatter

Overview of Fog/Edge Computing

Frontmatter
Confluence of 4G LTE, 5G, Fog, and Cloud Computing and Understanding Security Issues
Abstract
“Cloud” is a commonly used word for multiple developments and possibilities. This is not an idea, but a “practical innovation,” merging many prior innovations into something fresh and convincing. In addition, cloud computing will help companies convert their existing server resources into complex ecosystems, increasing server capacity as necessary. Since “Fog Computing” as an extension of cloud computing, by placing resources to the edge of the network that minimizes latency and network congestion may be a comparatively a recent pattern. Although growing cloud and fog computing offer identical infrastructure and facilities, the latter is distinguished by low latency, with larger distribution and globally dispersed nodes to support contact with quality and time. In the near future, and with very fast network bandwidth as they call it 5G cloud computing and fog computing going to run the world and become more important and secure them is highly important too. People will no longer use PlayStation and X-box as devices but play digitally via cloud storage. Many self-drive cares often focus on cloud and fog computing to collect and communicate with the world through telecommunication.
Khaldoon Alshouiliy, Dharma P. Agrawal
An Overview of the Edge Computing in the Modern Digital Age
Abstract
The explosive growth of the Internet of Things (IoT) devices and the growing computing power of these devices has resulted in unprecedented volumes of data. Which will continue to crest as communication networks increase the number of connected mobile devices. Edge computing is an open and distributed architecture that features decentralized processing power, enabling mobile computing technologies, as well as the Internet of Things (IoT) devices or local edge servers. It offers a more efficient alternative by having the data processed and analyzed closer to the point at which it was created. This proximity to the data at its source can result in real business benefits related to better response times, faster insights, and improved bandwidth availability. Since data is not transmitted over a network to a cloud or data center to be processed, causing latency to be significantly reduced. At its core, edge computing technology simply means processing raw data from the sensor as close as possible to the endpoint that generated the data without going to the cloud to use the heavy computing capacity of high-end servers. Therefore, this chapter aims to provide an updated review and overview of Edge Computing, addressing its evolution and fundamental concepts, showing its relationship as well as approaching its success, with a concise bibliographic background, categorizing and synthesizing the potential of technology.
Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur, Yuzo Iano

Security in Fog/Edge Computing

Frontmatter
Secure Search and Storage Services in Cloud and Fog/Edge Computing
Abstract
As an extension of cloud computing, fog/edge computing migrates enormous computing and storage resources to the network edge, which forms an edge layer that is close to end devices. However, security concerns hinder the adoption of fog/edge computing. This book chapter researches the problem of secure storage and search services in fog/edge computing. From the aspect of storage security, we first propose a hierarchical-attribute-based encryption (HABE) scheme to efficiently achieve fine-grained access control in clouds, and then we combine HABE and Proxy Re-Encryption (PRE) by incorporating time concept into PRE to achieve user revocation automatically. From the aspect of search privacy, we propose a dynamic attribute-based keyword search (DABKS) scheme to achieve fine-grained search authorization in cloud computing. DABKS delegates the policy updating operations to the cloud, by incorporating proxy re-encryption (PRE) and a secret sharing scheme (SSS) into attribute-based keyword search (ABKS). The research results will play an important role in attribute-based encryption and searchable encryption, which could help create new situations for providing secure services in cloud computing and fog/edge computing.
Qin Liu
Collaborative Intrusion Detection Schemes in Fog-to-Things Computing
Abstract
The adoption of the Internet of Things (IoT) has raised a significant concern of cyber-attacks at the edge of the network. As the existing traditional intrusion detection (IDS) solutions cannot be applied for the IoT, lightweight IDS schemes are essential to address the security challenges in severely resource-constrained and heterogeneous IoT systems. Three requirements are critical for designing and implementing such schemes successfully: handling distribution (scalability), managing resource constraints, and designing accurate and robust algorithms. The large-scale IoT traffic is so massively distributed in nature that centralized IDS architectures such as the cloud do not scale up and suffer from a high delay for the real-time requirements of the IoT. In this regard, the emergence of fog computing provides a tremendous opportunity to detect suspicious events closer to things in distributed manner, and enables to offload processing, storage and communication overheads from the IoT for intrusion monitoring operations. Apart from employing fog nodes as a collaborative spot for intrusion detection, it is essential to adopt recent algorithms that provide lightweight, robust and autonomous operations for intrusion detection at the fog level since the existing intrusion detection systems fail to provide these requirements. In this case, deep learning (DL) approaches have been found to be promising in securing the IoT by providing compressed data representations and fast processing. Thus, the proliferation of fog nodes coupled with DL techniques could provide lightweight, autonomous and efficient schemes with an improved level of robustness.
However, DL models can be victims of cyber-attacks during training and detection. If adversarial attacks need to be controlled from the training and inference models, blockchain has a bright future in the realm of intrusion detection at the fog network level. In order to overcome adversarial attacks that can poison parameters or models, secure information exchange will be followed using the blockchain concept among fog nodes. In this chapter, we explore the application of emerging trends and enablers such as fog computing architecture, blockchain and DL algorithms for intrusion detection in the IoT/fog network.
Abebe Diro, Abdun Mahmood, Naveen Chilamkurti
On the Feasibility of Byzantine Agreement to Secure Fog/Edge Data Management
Abstract
Fog/Edge computing improves the latency and security of data by keeping storage and computation close to the data source. Nevertheless, this raises other security challenges against malicious, a.k.a, Byzantine, attacks that can exploit the isolation of nodes, or when access to distributed data is required in untrusted environments. In this work, we study the feasibility of deploying Byzantine Agreement protocols to improve the security of fog/edge systems in untrusted environments. In particular, we explore existing Byzantine Agreement protocols, heavily developed in the Blockchain area, emphasizing the Consistency, Availability, and Partition-Tolerance tradeoffs in a geo-replicated system. Our work identifies and discusses three different approaches that follow the Strong Consistency, Eventual Consistency, and Strong Eventual Consistency models. Our conclusions show that Byzantine Agreement protocols are still immature to be used by fog/edge computing in untrusted environment due to their high finality latency; however, they are promising candidates that encourage further research in this direction.
Ali Shoker, Houssam Yactine

Privacy in Fog/Edge Computing

Frontmatter
Privacy Issues in Edge Computing
Abstract
While edge computing extends the computational power to the edge of networks, it helps to fix bottleneck of the bandwidth limitation and network latency problems in cloud computing. However, more edge servers and communication between edge servers and end devices bring more challenges to user’s privacy. Since edge computing causes tremendous exchange of user’s data, identity and location to edge server than cloud computing, private information is leaked easily, which makes privacy concern severe more than ever. In this chapter, we explain why privacy problems are becoming more severe than cloud computing, summarize the current privacy challenges and techniques against those privacy threats and discuss some future work about privacy issues in edge computing.
Qi Xia, Zeyi Tao, Qun Li
Privacy-Preserving Edge Video Analytics
Abstract
Edge video analytics (EVA) emerges as a promising paradigm to decentralize video analytics tasks to the edge of the network and thus improve user experience by enabling faster task execution for users. However, it is challenging to enable edge-based model training while still satisfying the requirement of user privacy. A faster model training process is commonly at the cost of more disclosure of user privacy. We propose a federated learning driven privacy-preserving model training framework called FedEVA for edge video analytics, which can protect user privacy and ensure a fast convergence rate. Instead of directly sending gradients to the parameter server, users conduct a local perturbation operation on users’ update information, and then send the perturbed gradients to the parameter server. The parameter server at the edge can update the neural network model directly over the perturbed gradients. We carefully design the perturbation function to conceal partial information about data while efficiently performing computation over the gradients from multiple users. Different from crypto-based methods, our perturbation process is a light-weight operation. We conduct extensive evaluations using large-scale real-world datasets to verify the effectiveness of our FedEVA framework and compare with other baseline algorithms. The results show that our FedEVA framework can improve the degree of privacy preservation, and still maintain the same level of convergence rate.
Miao Hu, Yao Fu, Di Wu

Architectural Design in Fog/Edge Computing

Frontmatter
Vulnerabilities in Fog/Edge Computing from Architectural Perspectives
Abstract
Recently, the emerging IoTization paradigm generates a tremendous amount of heterogeneous data offloaded from digital devices to networks. The heterogeneity of these big IoT data requires the networks to expand their computational capability from the cloud to the edge, realizing a new fog/edge computing (FEC) system. The FEC system provides multiple satisfactory levels in terms of performance, latency, security, etc. to user devices according to service demands. Because the FEC system is in between the cloud and user devices, this supplemental part introduces several vulnerabilities on both north and south interfaces as well as among internal FEC components. This chapter analyzes open security and privacy issues in FEC from architectural perspectives. First, a comprehensive overview of computational cloudization is presented, which leads to a hierarchical computing architecture in the network. From that, vulnerabilities within each architectural model, such as intrinsic FEC, the standard reference FEC architecture, FEC virtualization, and FEC integration into the 5G network, are discussed in detail. Finally, we summarize the chapter in the last section.
Nhu-Ngoc Dao, Ngoc-Thanh Dinh, Quoc-Viet Pham, Trung V. Phan, Sungrae Cho, Torsten Braun
Security and Intelligent Management for Fog/Edge Computing Resources
Abstract
In 5G and 6G ear, sensing data from huge amount of heterogeneous sensors will generate big data at the edge of IoT. Fog/Edge computing technology is proposed to resolve the edge big data analysis and processing. However, the security and intelligent management for fog/edge computing resources are still open issues. First, because fog/edge computing is usually deployed in large-scale IoT, it faces various threats from untrusted distributed geographic multi-sources and differentiated layer of the networks. Second, content threats will be generated at the communication layer, because software-defined networking/information-centric networking (SDN/ICN) technologies has been introduced into networked fog/edge computing nodes. Third, at the edge of the networks, there is unbalance between the users and providers of fog/edge computing resources, which means on-demand resource scheduling and balance are the must for fog/edge computing. Based on aforementioned motivations, this chapter aims to study the lightweight security and intelligent scheduling approaches for fog/edge computing resources. Collaborative trust, intrusion detection and security isolation, storage resource intelligent orchestration and service popularity-based smart resources partitioning technologies are studied for edge/fog computing. The works are significant to improve the intelligence and security level for novel fog/edge computing systems.
Jun Wu
Algorithms for NFV-Enabled Multicasting in Mobile Edge Computing
Abstract
Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the edge of core networks. This provides users with powerful computing and storage capacities, energy efficiency, and mobility—and context-aware supporting. Multicasting in MEC is a fundamental functionality of many network applications of mobile users, including online conferencing, event monitoring, video streaming, and so on. To guarantee the security and privacy of each multicast traffic session, a service chain that consists of security network functions usually is associated with each multicast request to process its traffic. In this chapter, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network. We first devise approximation algorithms for the cost minimization problem of admitting a single NFV-enabled multicast request, by assuming that the virtualized network functions may or may not be consolidated into a single location. We then devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.
Zichuan Xu, Weifa Liang
Blockchain-Based Security Services for Fog Computing
Abstract
Fog computing is a paradigm for distributed computing that enables sharing of resources such as computing, storage and network services. Unlike cloud computing, fog computing platforms primarily support non-functional properties such as location awareness, mobility and reduced latency. This emerging paradigm has many potential applications in domains such as smart grids, smart cities, and transport management.
Most of these domains collect and monitor personal information through edge devices to offer personalized services. A centralized server either at the level of cloud or fog, has been found ineffective to provide a high degree of security and privacy-preserving services.
Blockchain technology supports the development of decentralized applications designed around the principles of immutability, cryptography, consistency preserving consensus protocols and smart contracts. Hence blockchain technology has emerged as a preferred technology in recent times to build trustworthy distributed applications.
The chapter describes the potential of blockchain technology to realize security services such as authentication, secured communication, availability, privacy and trust management to support the development of dependable fog services.
Arvind W. Kiwelekar, Pramod Patil, Laxman D. Netak, Sanjay U. Waikar

Applications of Fog/Edge Computing

Frontmatter
Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing: A Review and Future Directions
Abstract
With rapid technological advancements within the domain of Internet of Things (IoT), strong trends have emerged which indicate rapid growth in the number of smart devices connected to IoT networks and this growth cannot be supported by traditional cloud computing platforms. In response to the high volume of data being transferred over these networks, the edge and fog computing paradigms have emerged. These paradigms are extremely viable frameworks that shift computational and storage resources from the centralized cloud servers to distributed LAN resources and powerful embedded devices at the edge of the network. These computing paradigms, therefore, have the potential to support massive IoT networks of the future and have fueled the advancement of IoT systems within industrial settings, leading to the creation of the Industrial Internet of Things (IIoT). IIoT is revolutionizing industrial processes in a variety of domains. In this chapter, we elaborate on the impact and viability of edge and fog computing paradigms in IIoT through a use-case approach. Finally, we conclude with the future research directions like security and privacy for edge and fog computing in IIoT, relevance of Blockchain for IIoT, programmability and task partitioning, virtualization, etc.
G. S. S. Chalapathi, Vinay Chamola, Aabhaas Vaish, Rajkumar Buyya
Leveraging Edge Computing for Mobile Augmented Reality
Abstract
Augmented reality (AR) applications are becoming increasingly popular for personal and commercial use, thanks to dropping costs of AR-enabled hardware and commercially viable libraries and APIs. AR applications’ ability to insert virtual content into the real world is a product of always-on environmental sensors such as cameras and microphones, coupled with powerful machine learning logic to recognize and respond to a user’s surroundings and behavior. The virtual content generated by the system is then displayed on a hand-held screen such as a smartphone or tablet, a head-mounted display such as the Microsoft Hololens, or a statically mounted display, such as an augmented windshield in a smart vehicle. While the input, transformation, and output phases of the AR processing pipeline are susceptible to certain adversarial attacks, incorporating edge computing into these applications can help to alleviate some of the security concerns. In this chapter, we provide a brief introduction into augmented reality systems and their security concerns, as well as how edge computing can be utilized to help address those concerns.
Sarah M. Lehman, Chiu C. Tan
Towards a Security-Aware Deployment of Data Streaming Applications in Fog Computing
Abstract
Emerging fog and edge computing environments enable the analysis of Big Data collected from devices (e.g., IoT sensors) with reduced latency compared to cloud-based solutions. In particular, many applications deal with continuous data flows in latency-sensitive domains (e.g., healthcare monitoring), where Data Stream Processing (DSP) systems represent a popular solution. However, the highly heterogeneous nature of fog/edge platforms poses several challenges for efficiently deploying DSP applications, including security and privacy issues. As data streams flow through public networks and are possibly processed within multi-tenant computing platforms, new metrics must be considered for deployment, accounting for security and privacy related concerns, besides traditionally adopted performance and cost aspects. In this chapter, we present the most relevant existing solutions for deploying DSP applications in fog/edge environments, discussing—in particular—how they address security and privacy concerns. Then, we present Security-aware DSP Placement (SDP), a formulation of the optimal deployment problem for DSP applications in fog/edge environments. Specifically, we introduce security-related application requirements in addition to non-functional ones, and show how the resolution of SDP allows us to trade-off cost and performance with privacy and data integrity objectives.
Gabriele Russo Russo, Valeria Cardellini, Francesco Lo Presti, Matteo Nardelli
Blockchain of Finite-Lifetime Blocks for Edge-IoT Applications
Abstract
In the recent past, many studies proposed the use of blockchain technology among edge computing nodes for the decentralized management and access control of IoT devices and data. Unlike cryptocurrency transactions in bitcoin blockchain that are stored indefinitely, IoT data has finite-lifetime. This difference in the lifetime of the IoT data provides an opportunity to reduce the storage costs of blockchain for Edge-IoT systems. This chapter reviews LiTiChain—a specialized architecture published in the literature and presents μ-LiTiChain—a generalized version of the LiTiChain. Both these architectures aim at reducing the storage costs of blockchain of finite-lifetime blocks for Edge-IoT systems. μ-LiTiChain offers a degree of freedom in the design of blockchain of finite-lifetime blocks in terms of a tradeoff between storage cost, security and computational cost. To provide the same level of security as conventional blockchain, in LiTiChain, some blocks are stored longer than their lifetime, which incurs additional storage cost. With extensive simulations and analysis, it is demonstrated that the μ-LiTiChain has the potential to decrease the additional storage cost incurred by LiTiChain to zero and improve security at the expense of computational cost. Two variants of μ-LiTiChain architecture i.e. p-LiTiChain and s-LiTiChain are also discussed.
Shravan Garlapati
Metadata
Title
Fog/Edge Computing For Security, Privacy, and Applications
Editors
Assist. Prof. Wei Chang
Prof. Jie Wu
Copyright Year
2021
Electronic ISBN
978-3-030-57328-7
Print ISBN
978-3-030-57327-0
DOI
https://doi.org/10.1007/978-3-030-57328-7

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