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SpoVis: Decision Support System for Site Selection of Sports Facilities in Digital Twinning Cities

Journal
Ke Zhang, Hao Chen, Hong-Ning Dai, Hongbo Liu, Zhongrui Lin
IEEE Transactions on Industrial Informatics (early access), 2021
Publication year: 2021

Abstract:

The site selection of sports facilities is a pivotal link in the construction of city livable environment and the development of sports business in digital-twinning cities. Recent years have witnessed data mining and visualization technologies bringing the convenience as well as opportunities for intelligent site selection. However, the lack of effective and reliable systematic analysis leads to difficulties in developing sports facilities planning schemes and constructing the site-selection system. In this paper, we design SpoVis, an interactive visual analysis system for planning sports facilities as well as site selection. SpoVis provides users with the distribution status and statistical analysis of various sports facilities. Based on a comprehensive consideration of city population distribution, construction cost, existing sports facilities, traffic situation and development potential, SpoVis provides users with a reasonable site-selection scheme of sports facilities from both macro and micro perspectives and recommends results through topology and map. Meanwhile, based on the distribution of existing sports facilities and city influencing factors, a set of visual analysis components are designed to facilitate users to evaluate the status and information of existing sports facilities. We have carried out extensive experiments on a real platform with real-world data. The experimental results show that the proposed site-selection models and algorithms have excellent accuracy and operation efficiency.

Bibtex

@ARTICLE{9456031,
  author={Zhang, Ke and Chen, Hao and Dai, Hong-Ning and Liu, Hongbo and Lin, Zhongrui},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={SpoVis: Decision Support System for Site Selection of Sports Facilities in Digital Twinning Cities}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TII.2021.3089330}
}

MFF-AMD: Multivariate Feature Fusion for Android Malware Detection

Conference
Guangquan Xu, Meiqi Feng, Litao Jiao, Jian Liu, Hong-Ning Dai, Ding Wang, Emmanouil Panaousis, Xi Zheng
EAI CollaborateCom 2021
Publication year: 2021

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy.

Kubernetes in IT administration and serverless computing: An empirical study and research challenges

Journal
Subrota Kumar Mondal, Rui Pan, H M Dipu Kabir, Tan Tian, Hong-Ning Dai
The Journal of Supercomputing, 2021
Publication year: 2021

Abstract:

Today’s industry has gradually realized the importance of lifting efficiency and saving costs during the life-cycle of an application. In particular, we see that most of the cloud-based applications and services often consist of hundreds of micro-services; however, the traditional monolithic pattern is no longer suitable for today’s development life-cycle. This is due to the difficulties of maintenance, scale, load balance, and many other factors associated with it. Consequently, people switch their focus on containerization—a lightweight virtualization technology. The saving grace is that it can use machine resources more efficiently than the virtual machine (VM). In VM, a guest OS is required to simulate on the host machine, whereas containerization enables applications to share a common OS. Furthermore, containerization facilitates users to create, delete, or deploy containers effortlessly. In order to manipulate and manage the multiple containers, the leading Cloud providers introduced the container orchestration platforms, such as Kubernetes, Docker Swarm, Nomad, and many others. In this paper, a rigorous study on Kubernetes from an administrator’s perspective is conducted. In a later stage, serverless computing paradigm was redefined and integrated with Kubernetes to accelerate the development of software applications. Theoretical knowledge and experimental evaluation show that this novel approach can be accommodated by the developers to design software architecture and development more efficiently and effectively by minimizing the cost charged by public cloud providers (such as AWS, GCP, Azure). However, serverless functions are attached with several issues, such as security threats, cold start problem, inadequacy of function debugging, and many other. Consequently, the challenge is to find ways to address these issues. However, there are difficulties and hardships in addressing all the issues altogether. Respectively, in this paper, we simply narrow down our analysis toward the security aspects of serverless. In particular, we quantitatively measure the success probability of attack in serverless (using Attack Tree and Attack–Defense Tree) with the possible attack scenarios and the related countermeasures. Thereafter, we show how the quantification can reflect toward the end-to-end security enhancement. In fine, this study concludes with research challenges such as the burdensome and error-prone steps of setting the platform, and investigating the existing security vulnerabilities of serverless computing, and possible future directions.

Bibtex

@ARTICLE{Kubernetes:JSC21,
  author={Subrota Kumar Mondal and Rui Pan and H M Dipu Kabir and Tan Tian and Hong-Ning Dai},
  journal={The Journal of Supercomputing}, 
  title={Kubernetes in IT administration and serverless computing: An empirical study and research challenges}, 
  year={2021},
  volume={},
  number={},
  pages={1-51},
  doi={https://doi.org/10.1007/s11227-021-03982-3}
}

 

Jamming Schemes to Secure Ultra-Reliable and Low-Latency Communications in 5G and Beyond Communications

Journal
Xuran Li, Hong-Ning Dai, Mahendra K. Shuklay, Dengwang Li, Huaqiang Xu, and Muhammad Imran
Computer Standards & Interfaces, Volume 78, October 2021
Publication year: 2021

Abstract:

The security vulnerabilities are becoming the major obstacle to prevent the wide adoption of ultra-reliable and low latency communications (URLLC) in 5G and beyond communications. Current security countermeasures based on cryptographic algorithms have a stringent requirement on the centralized key management as well as computational capabilities of end devices while it may not be feasible for URLLC in 5G and beyond communications. In contrast to cryptographic approaches, friendly jamming (FJ) as a promising physical layer security method can enhance wireless communications security while it has less resource requirement on end devices and it can be applied to the full distribution environment. In order to protect wireless communications, FJ signals are introduced to degrade the decoding ability of eavesdroppers who maliciously wiretap confidential information. This article presents a state-of-the-art survey on FJ schemes to enhance network security for IoT networks with consideration of various emerging wireless technologies and different types of networks. First, we present various secrecy performance metrics and introduce the FJ method. The interference caused by FJ signals on legitimate communication is the major challenge of using FJ schemes. In order to overcome this challenge, we next introduce the integration of FJ schemes with various communication technologies, including beamforming, multiple-input multiple-output, full duplex, and relay selection. In addition, we also integrate FJ schemes with different types of communication networks. Finally, a case study of FJ schemes is illustrated and future research directions of FJ schemes have been outlined.

Bibtex

@article{LI2021103540,
    title = {Friendly-jamming schemes to secure ultra-reliable and low-latency communications in 5G and beyond communications},
    authors = {Xuran Li and Hong-Ning Dai and Mahendra K. Shukla and Dengwang Li and Huaqiang Xu and Muhammad Imran}
    journal = {Computer Standards & Interfaces},
    volume = {78},
    pages = {103540},
    year = {2021},
    issn = {0920-5489},
    doi = {https://doi.org/10.1016/j.csi.2021.103540},
    url = {https://www.sciencedirect.com/science/article/pii/S0920548921000350},
}

Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?

Journal
Xuran Li, Bishenghui Tao, Hong-Ning Dai, Muhammad Imran, Dehuan Wan, Dengwang Li
Pervasive and Mobile Computing
Publication year: 2021

Abstract:

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

Bibtex

@article{LI2021101434,
   title = {Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?},
   journal = {Pervasive and Mobile Computing},
   volume = {75},
   pages = {101434},
   year = {2021},
   issn = {1574-1192},
   doi = {https://doi.org/10.1016/j.pmcj.2021.101434},
   url = {https://www.sciencedirect.com/science/article/pii/S1574119221000808},
   author = {Xuran Li and Bishenghui Tao and Hong-Ning Dai and Muhammad Imran and Dehuan Wan and Dengwang Li},
}

Industrial Blockchain: A state-of-the-art Survey

Journal
Zhi Li, Ray Y. Zhong, Zong-Gui Tian, Hong-Ning Dai, Ali Vatankhah Barenji, George Q. Huang
Robotics and Computer-Integrated Manufacturing, 2021
Publication year: 2021

Abstract

As an underlying and backbone technology of Bitcoin, Blockchain attracted extensive attention worldwide in recent years due to its unique characteristics of decentralization, openness, immutability, anonymity, etc., which enables it to build a trust basis through recording the point-to-point decentralized transactions in an immutable way via the attached timestamp, thereby improving system efficiency and reducing the cost without relying on the central agent. As it is considered to be a potentially revolutionary technology, Blockchain has been introduced into various industrial fields including finance, supply chain, manufacturing, healthcare, energy, and smart city. In this paper, we conduct a state-of-the-art survey of industrial Blockchain in terms of published articles between 2017 and 2020, and worldwide Blockchain movement including North America, Europe, and the Asia Pacific region so far. We conduct a statistic analysis of the collected articles in terms of three dimensions, which are year of publication, leading research institutes and researchers, and article classification to present a multi-dimensional trend or conclusion. Besides, we analyse articles that are cited over a certain number of times in detail to investigate the hot research directions. Finally, the challenges, opportunities, and future perspectives are discussed to summarize the main obstacles of industrial Blockchain and identify the open research questions in the near future.

Bibtex

@ARTICLE{ZLi:RCIM21,
   author={Zhi Li and Ray Y. Zhong and Zong-Gui Tian and Hong-Ning Dai and Ali Vatankhah Barenji and George Q. Huang},
   journal={Robotics and Computer-Integrated Manufacturing}, 
   title={Industrial Blockchain: A state-of-the-art Survey}, 
   year={2021},
   volume={},
   number={},
   pages={},
}

IEEE Access Special Section Editorial: Blockchain-Enabled Trustworthy Systems

Journal
Dai, Hong-Ning and Maharjan, Sabita and Zheng, Zibin and Hung, Patrick C. K. and Xu, Quanqing and Sun, Wen
IEEE Access 9: 67680-67683 (2021)
Publication year: 2021

We are enjoying the benefits brought by the accelerated development of computing systems and the Internet. However, we are also facing a few security and privacy vulnerabilities caused by the increasing system complexity, heterogeneity, dynamicity, and decentralized nature. These security and privacy vulnerabilities may prevent the wide adoption of information and communications technology (ICT). Therefore, trust management has become a crucial aspect of developing trustworthy systems with the preservation of security and privacy. The recent advances in blockchain technologies are bringing opportunities in fully realizing trustworthy systems. Blockchain technologies can enable anonymous and trustful transactions in decentralized and trustless environments. As a result, blockchain-enabled trust management must help to reduce system risks, mitigate financial fraud, and cut down the operational cost of computing systems. Blockchain-enabled trustworthy systems can apply to diverse areas, such as financial services, social management, the Internet of Things, and supply chain management.

Guest Editorial: Blockchain Solutions for Industrial Internet of Things

Journal
Yan Zhang, Zibin Zheng, Hong-Ning Dai
IEEE Transactions on Industrial Informatics, Volume: 17, Issue: 11, Nov. 2021
Publication year: 2021

There is a growing trend of adopting blockchain technologies to the industrial Internet of Things (IIoT) due to the traceability, nonrepudiation, and immutability of blockchain systems. The proliferation of IIoT to industrial systems is fostering the fourth industrial revolution (aka Industry 4.0) while IIoT also confronts several challenges exhibiting in the following two perspectives: 1) security and privacy protection of IIoT data; 2) interoperability absence across IIoT systems. Blockchain and blockchain-enabled smart contracts can essentially offer solutions to address the emerging challenges in IIoT. The objective of this special section in IEEE Transactions on Industrial Informatics is to explore the state-of-the-art advances in adopting blockchain technologies for IIoT.

Ground-to-UAV Communication Network: Stochastic Geometry-based Performance Analysis

Conference
Yalin Liu, Hong-Ning Dai, Muhammd Imran, Nidal Nasser
IEEE International Conference on Communications (ICC 2021)
Publication year: 2021

In this paper, we employ stochastic geometry to analyze ground-to-unmanned aerial vehicle (UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with uplink transmissions, where the distribution of UEs follows the Poisson Cluster process (PCP) and each UAV is dedicated to a specific cluster. In particular, we characterize the Laplace transform of the interference caused by multiple UEs in terms of the distribution of UEs as well as the transmission probability of each UE. We then derive analytical expressions of the successful transmission probability. We next conduct a comprehensive numerical analysis with consideration of different system parameters. The results show that four factors (i.e., the geographical surroundings, the transmission powers, the Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, and the UAV height) have main influences on ground-to-UAV communications.

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Journal
Yulei Wu, Hong-Ning Dai, Haina Tang
IEEE Internet of Things Journal (early access), 2021
Publication year: 2021

Abstract:

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoT-enabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

Bibtex:

@ARTICLE{9471816,
  author={Wu, Yulei and Dai, Hong-Ning and Tang, Haina},
  journal={IEEE Internet of Things Journal}, 
  title={Graph Neural Networks for Anomaly Detection in Industrial Internet of Things}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JIOT.2021.3094295}
}

Forecasting Cryptocurrency Price Using Convolutional Neural Networks with Weighted and Attentive Memory Channels

Journal
Zhuorui Zhangy, Hong-Ning Dai, Junhao Zhouy, Subrota Kumar Mondal, Miguel Martínez García, Hao Wang
Expert Systems With Applications, Volume 183, 30 November 2021
Publication year: 2021

Abstract:

After the invention of Bitcoin as well as other blockchain-based peer-to-peer payment systems, the cryptocurrency market has rapidly gained popularity. Consequently, the volatility of the various cryptocurrency prices attracts substantial attention from both investors and researchers. It is a challenging task to forecast the prices of cryptocurrencies due to the non-stationary prices and the stochastic effects in the market. Current cryptocurrency price forecasting models mainly focus on analyzing exogenous factors, such as macro-financial indicators, blockchain information, and social media data – with the aim of improving the prediction accuracy. However, the intrinsic systemic noise, caused by market and political conditions, is complex to interpret. Inspired by the strong correlations among cryptocurrencies and the powerful modelling capability displayed by deep learning techniques, we propose a Weighted & Attentive Memory Channels model to predict the daily close price and the fluctuation of cryptocurrencies. In particular, our proposed model consists of three modules: an Attentive Memory module combines a Gated Recurrent Unit with a self-attention component to establish attentive memory for each input sequence; a Channel-wise Weighting module receives the price of several heavyweight cryptocurrencies and learns their interdependencies by recalibrating the weights for each sequence; and a Convolution & Pooling module extracts local temporal features, thereby improving the generalization ability of the overall model. In order to validate the proposed model, we conduct a battery of experiments. The results show that our proposed scheme achieves state-of-the-art performance and outperforms the baseline models in prediction error, accuracy, and profitability.

Bibtex

@article{ZHANG2021115378,
      title = {Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels},
      journal = {Expert Systems with Applications},
      volume = {183},
      pages = {115378},
      year = {2021},
      issn = {0957-4174},
      doi = {https://doi.org/10.1016/j.eswa.2021.115378},
      url = {https://www.sciencedirect.com/science/article/pii/S0957417421008046},
      author = {Zhuorui Zhang and Hong-Ning Dai and Junhao Zhou and Subrota Kumar Mondal and Miguel Martínez García and Hao Wang},
}

EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Journal
Tian Wang, Yucheng Lu, Jianhuang Wang, Hong-Ning Dai, Xi Zheng, Weijia Jia
IEEE Transactions on Computers, 2021
Publication year: 2021

Abstract

Nowadays, IoT systems can better satisfy the service requirements of users by effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

Bibtex

@ARTICLE{TWang:TC21,
   author={Tian Wang and Yucheng Lu and Jianhuang Wang and Hong-Ning Dai and Xi Zheng and Weijia Jia},
   journal={IEEE Transactions on Computers}, 
   title={EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems}, 
   year={2021},
   volume={},
   number={},
   doi={10.1109/TC.2021.3060484},
   pages={1-1},
}

Edge-Based Communication Optimization for Distributed Federated Learning

Journal
Tian Wang, Yan Liu, Xi Zheng, Hong-Ning Dai, Weijia Jia, and Mande Xie,
IEEE Transactions on Network Science and Engineering, (early access) 2021
Publication year: 2021

Abstract

Federated learning can achieve the purpose of distributed machine learning without sharing privacy and sensitive data of end devices. However, high concurrent access to the server increases the transmission delay of model updates, and the local model may be an unnecessary model with the opposite gradient from the global model, thus incurring a large number of additional communication costs. To this end, we study a framework of edge-based communication optimization to reduce the number of end devices directly connected to the server while avoiding uploading unnecessary local updates. Specifically, we cluster devices in the same network location and deploy mobile edge nodes in different network locations to serve as hubs for cloud and end devices communications, thereby avoiding the latency associated with high server concurrency. Meanwhile, we propose a model cleaning method based on cosine similarity. If the value of similarity is less than a preset threshold, the local update will not be uploaded to the mobile edge nodes, thus avoid unnecessary communication. Experimental results show that compared with traditional federated learning, the proposed scheme reduces the number of local updates by 60%, and accelerates the convergence speed of the regression model by 10.3%.

Bibtex

@ARTICLE{9446648,
  author={Wang, Tian and Liu, Yan and Zheng, Xi and Dai, Hong-Ning and Jia, Weijia and Xie, Mande},
  journal={IEEE Transactions on Network Science and Engineering}, 
  title={Edge-Based Communication Optimization for Distributed Federated Learning}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TNSE.2021.3083263}
}

Ear in the Sky: Terrestrial Mobile Jamming to Prevent Aerial Eavesdropping

Conference
Qubeijian Wang, Yalin Liu, Hong-Ning Dai, Muhammad Imran, Nidal Nasser
The 2021 IEEE Global Communications Conference (GLOBECOM), 2021, Madrid, Spain (On-site & Virtual)
Publication year: 2021

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme.

Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

Journal
Wuhui Chen, Xiaoyu Qiu, Ting Cai, Hong-Ning Dai, Zibin Zheng, Yan Zhang
IEEE Communications Surveys & Tutorials (early access)
Publication year: 2021

Abstract

The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications. In particular, we first briefly review the state-of-the-art DRL algorithms and present a comprehensive analysis on their advantages and challenges. We then discuss on applying DRL algorithms to a wide variety of IoT applications including smart grid, intelligent transportation systems, industrial IoT applications, mobile crowdsensing, and blockchain-empowered IoT. Meanwhile, the discussion of each IoT application domain is accompanied by an in-depth summary and comparison of DRL algorithms. Moreover, we highlight emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.

Bibtex

@ARTICLE{9403369,
  author={W. {Chen} and X. {Qiu} and T. {Cai} and H. -N. {Dai} and Z. {Zheng} and Y. {Zhang}},
  journal={IEEE Communications Surveys   Tutorials}, 
  title={Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/COMST.2021.3073036}
}

Coverage Analysis of Blockchain-enabled Wireless IoMT Networks

Conference
Xuran Li, Hong-Ning Dai, Jie Tian, Dehuan Wan, Dengwang Li
2021 IEEE Globecom Workshops (GC Wkshps): Workshop on Scalable, Secure and Intelligent Blockchain for Future Networking and Communications
Publication year: 2021

The blockchain-enabled wireless Internet of medical things (IoMT) system has recently drawn extensive attention due to the provision of highly-secured healthcare services. However, multiple simultaneous transmissions in blockchain-enabled IoMT (BC-IoMT) networks can cause interference, consequently degrading the overall performance of the system. It is necessary to investigate the performance of BC-IoMT networks. In this paper, we propose a novel analytical framework for wireless BC-IoMT networks with consideration of both the spatial model on geographical random distribution of IoMT users and the temporal model on stochastic nature of data block transmission. With this framework, we derive a closed-form expression of the coverage probability in the wireless BC-IoMT network. Our extensive simulation results verify the accuracy of our theoretical analysis. From the analytical framework, we find that the path loss effect, the number of interfering medical sensor devices, and the threshold of successful transmission have a significant impact on the coverage performance of wireless BC-IoMT networks.

Connectivity Analysis of UAV-To-Satellite Communications in Non-Terrestrial Networks

Conference
Yalin Liu, Hong-Ning Dai, Ning Zhang
The 2021 IEEE Global Communications Conference (GLOBECOM), 2021, Madrid, Spain (On-site & Virtual)
Publication year: 2021

Non-terrestrial Networks (NTNs) refer to the networks, where either satellites or unmanned aerial vehicles (UAVs) are deployed to extend the current terrestrial networks for serving the growing mobile broadband and machine-type communications. With the advantages of UAVs’ flexibility and satellites’ global coverage, the solution of UAV-To-satellite communications (U2SC) can provide promising global communication services for the emerging NTNs. Previous literature has explored many potential directions of U2SC, including channel tracking, deployment design, and link analysis. However, as a vital role in system performance, the connectivity of U2SC has not been well investigated yet. This research gap motivates us to present an analytical model to evaluate the connectivity of U2SC. In particular, we first present the system model of the U2SC by considering the distribution model of UAVs, antenna models, and the path loss model. We then utilize stochastic geometry to derive a theoretical formulation of the successful connection probability of U2SC. The comprehensive numerical results are given to evaluate the received power, the interference, and the successful connection probability of U2SC and analyze the impacts of system parameters, such as the number of frequency carriers, the type of frequency bands, the number of UAVs, and the satellite altitude.

Complex Network Analysis of the Bitcoin Blockchain Network

Conference
Bishenghui Tao, Ivan Wang Hei Ho, Hong-Ning Dai
IEEE International Symposium on Circuits & Systems (ISCAS), Daegu, Korea, 2021 (On-site \& Virtual)
Publication year: 2021

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

Journal
Ke Zhang, Hanbo Ying, Hong-Ning Dai, Lin Li, Yuangyuang Peng, Keyi Guo, Hongfang Yu
IEEE Internet of Things Journal
Publication year: 2021

Abstract

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

@ARTICLE{KZhang:IoTJ21,
   author={Ke Zhang and Hanbo Ying and Hong-Ning Dai and Lin Li and Yuangyuang Peng and Keyi Guo and Hongfang Yu},
   journal={IEEE Internet of Things Journal}, 
   title={Compacting Deep Neural Networks for Internet of Things: Methods and Applications}, 
   year={2021},
   volume={},
   number={},
   doi={10.1109/JIOT.2021.3063497},
   pages={1-25},
}

 

 

Blockchain-empowered Edge Intelligence for Internet of Medical Things Against COVID-19

Journal
Hong-Ning Dai, Yulei Wu, Hao Wang, Muhammad Imran, Noman Haider,
IEEE Internet of Things Magazine, 2021
Publication year: 2021

Abstract:

We have witnessed an unprecedented public health crisis caused by the new coronavirus disease (COVID-19), which has severely affected medical institutions, our common lives, and social-economic activities. This crisis also reveals the brittleness of existing medical services, such as over-centralization of medical resources, the hysteresis of medical services digitalization, and weak security and privacy protection of medical data. The integration of the Internet of Medical Things (IoMT) and blockchain is expected to be a panacea to COVID-19 attributed to the ubiquitous presence and the perception of IoMT as well as the enhanced security and immutability of the blockchain. However, the synergy of IoMT and blockchain is also faced with challenges in privacy, latency, and context-absence. The emerging edge intelligence technologies bring opportunities to tackle these issues. In this article, we present a blockchain-empowered edge intelligence for IoMT in addressing the COVID-19 crisis. We first review IoMT, edge intelligence, and blockchain in addressing the COVID-19 pandemic. We then present an architecture of blockchain-empowered edge intelligence for IoMT after discussing the opportunities of integrating blockchain and edge intelligence. We next offer solutions to COVID-19 brought by blockchain-empowered edge intelligence from 1) monitoring and tracing COVID-19 pandemic origin, 2) traceable supply chain of injectable medicines and COVID-19 vaccines, and 3) telemedicine and remote healthcare services. Moreover, we also discuss the challenges and open issues in blockchain-empowered edge intelligence.

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