edge networks
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2022 ◽  
Vol 22 (3) ◽  
pp. 1-25
Author(s):  
Mohammad Saidur Rahman ◽  
Ibrahim Khalil ◽  
Xun Yi ◽  
Mohammed Atiquzzaman ◽  
Elisa Bertino

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.


2022 ◽  
Vol 40 (2) ◽  
pp. 445-448
Author(s):  
Mingzhe Chen ◽  
Deniz Gunduz ◽  
Kaibin Huang ◽  
Walid Saad ◽  
Mehdi Bennis ◽  
...  

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 452
Author(s):  
Nour Alhuda Sulieman ◽  
Lorenzo Ricciardi Celsi ◽  
Wei Li ◽  
Albert Zomaya ◽  
Massimo Villari

Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.


Author(s):  
Tushar Bose ◽  
Aala Suresh ◽  
Om Jee Pandey ◽  
Linga Reddy Cenkeramaddi ◽  
Rajesh M. Hegde

2022 ◽  
Vol 71 (2) ◽  
pp. 3277-3291
Author(s):  
Siyuan Sun ◽  
Junhua Zhou ◽  
Jiuxing Wen ◽  
Yifei Wei ◽  
Xiaojun Wang
Keyword(s):  

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Nikolaos Nomikos ◽  
Spyros Zoupanos ◽  
Themistoklis Charalambous ◽  
Ioannis Krikidis

2021 ◽  
Author(s):  
Danyang Zheng ◽  
Gangxiang Shen ◽  
Yongcheng Li ◽  
Xiaojun Cao ◽  
Biswanath Mukherjee

<p>In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called <i>k</i>-connected service function slices layered graph (KC-SLG), protecting against <i>k</i>-1 fiber link failures and <i>k</i>-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: <i>k</i>-connected network slicing (KC-NS) and <i>k</i>-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is <i>2</i>-approximate. For KC-FS, we demonstrate that <i>k</i> = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.</p>


2021 ◽  
Author(s):  
Danyang Zheng ◽  
Gangxiang Shen ◽  
Yongcheng Li ◽  
Xiaojun Cao ◽  
Biswanath Mukherjee

<p>In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called <i>k</i>-connected service function slices layered graph (KC-SLG), protecting against <i>k</i>-1 fiber link failures and <i>k</i>-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: <i>k</i>-connected network slicing (KC-NS) and <i>k</i>-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is <i>2</i>-approximate. For KC-FS, we demonstrate that <i>k</i> = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.</p>


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