Blockchain and Edge Computing Techniques for Emerging IoT Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 2172

Special Issue Editors

1. Haihe Laboratory of Information Technology Application Innovation, Tianjin 300350, China
2. Institute of Computing, Institute of Computing Technology Chinese Academy of Sciences, Beijing 53035, China
Interests: artificial intelligence; big data; edge computing; internet of things; computer network security
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA
Interests: blockchain; applied cryptography; data security
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Interests: IoT; wireless intelligent networking; network performance evaluation

Special Issue Information

Dear Colleagues,

As the number of Internet-connected devices is dramatically increasing, the IoT is becoming more prevalent for emerging applications to meet the demands of the efficient, flexible, reliable and powerful accessibility of cyberspace from physical systems. More devices are concerned with offering an innovative approach to quality of life, urban challenges, food production, agriculture, manufacturing, medicine, energy supply and how to offer a wide variety of smart products and services. While motivating new business models in the growing digital economies, the large volume of IoT data collected by heterogeneous devices interacting and working under IoT systems requires more network resources for enhanced transmission and processing such as AI-based analytics. Edge computing can offload computation tasks from the centralized data center to the network edges in closer proximity to IoT devices and balance resource consumption between data centers and the edge devices, thus making data processing more efficient and flexible while using less resources. Meanwhile, the blockchain provides a decentralized digital ledger and also facilitates decentralized applications of the IoT. Especially, it could enable an open distributed cloud marketplace desired for collaboration among participants in an edge-computing-assisted IoT. An edge device can join the decentralized computing ecosystem and lend computational resources when required. In view of the challenges by enabling new IoT applications and services heavily restricted by the problems of resource management, single points of failure, data privacy, security and robustness, both edge computing and the blockchain would provide attractive solutions.

The motivation of this Special Issue is to discover and promote the current advancements, techniques, innovation and real-world solutions of blockchain and edge computing techniques in IoT infrastructure. This Special Issue will focus on gathering both quantitative and qualitative research contributions from individual, academic, organizational and industry practitioners in the newly emerging area of blockchain and edge computing solutions for resource management, scalable operation, big data processing and dependability issues in IoT.

Topics of interest to the Special Issue include, but are not limited to, the following:

  • Theory and foundation research of IoT big data transmission and processing based on blockchain and edge computing;
  • Innovative blockchain and edge computing architectures for optimizing time series computational data in IoT;
  • Architecture for convergence of blockchain-based IoT supported by edge computing techniques;
  • Lightweight and secure algorithms for real-world IoT applications based on blockchain and edge computing;
  • Blockchain and edge computing assisted efficient resource management for:

    (1) IoT architectures (things-centric, data-centric and service-centric architectures);

    (2) Edge and cloud computing-based IoT designs;

    (3) IoT enabling smart applications (smart city, smart manufacturing, smart agriculture, smart transport, etc.);

  • Use of blockchain techniques for security, trust and privacy in edge-computing-assisted IoT systems.
  • Dependable policy management for securing IoT systems assisted with blockchain and edge computing;
  • Other technologies and applications that advocate techniques in the field of blockchain and edge computing in IoT.

Dr. Zhiwei Xu
Dr. Bo Chen
Dr. Qi Wang
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Research

14 pages, 1976 KiB  
Article
Towards Trust Hardware Deployment of Edge Computing: Mitigation of Hardware Trojans Based on Evolvable Hardware
by Zeyu Li, Junjie Wang, Zhao Huang, Nan Luo and Quan Wang
Appl. Sci. 2022, 12(13), 6601; https://doi.org/10.3390/app12136601 - 29 Jun 2022
Cited by 2 | Viewed by 1274
Abstract
Hardware Trojans (HTs) are malicious hardware components designed to leak confidential information or cause the chip/circuit on which they are integrated to malfunction during operation. When we deploy such hardware platforms for edge computing, FPGA-based implementations of Coarse-Grained Reconfigurable Array (CGRA) are also [...] Read more.
Hardware Trojans (HTs) are malicious hardware components designed to leak confidential information or cause the chip/circuit on which they are integrated to malfunction during operation. When we deploy such hardware platforms for edge computing, FPGA-based implementations of Coarse-Grained Reconfigurable Array (CGRA) are also currently falling victim to HT insertion. However, for CGRA, an evolvable hardware (EHW) platform, which has the ability to dynamically change its configuration and behavioral characteristics based on inputs from the environment, provides us with a new way to mitigate HT attacks. In this regard, we investigate the feasibility of using EHW to mitigate HTs that disrupt normal functionality in CGRA in this paper. When it is determined that HT is inserted into certain processing elements (PEs), the array autonomously reconfigures the circuit structure based on an evolutionary algorithm (EA) to avoid the use of HT-infected (HT-I) PEs. We show that the proposed approach is applicable to: (1) hardware platforms that support coarse-grained reconfiguration; and (2) pure combinatorial circuits. In a simulation environment built in Python, this paper reports experimental results for two target evolutionary circuits and outlines the effectiveness of the proposed method. Full article
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