A key enabler for the intelligent information society of 2030, 6G networks are expected to provide performance superior to 5G and satisfy emerging services and applications. In this article, we present our vision of what 6G will be and describe usage scenarios and requirements for multi-terabyte per second (Tb/s) and intelligent 6G networks. We present a large-dimensional and autonomous network architecture that integrates space, air, ground, and underwater networks to provide ubiquitous and unlimited wireless connectivity. We also discuss artificial intelligence (AI) and machine learning [1], [2] for autonomous networks and innovative air-interface design. Finally, we identify several promising technologies for the 6G ecosystem, including terahertz (THz) communications, very-large-scale antenna arrays [i.e., supermassive (SM) multiple-input, multiple-output (MIMO)], large intelligent surfaces (LISs) and holographic beamforming (HBF), orbital angular momentum (OAM) multiplexing, laser and visible-light communications (VLC), blockchain-based spectrum sharing, quantum communications and computing, molecular communications, and the Internet of Nano-Things.

6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

Reliable data connectivity is vital for the ever increasingly intelligent, automated, and ubiquitous digital world. Mobile networks are the data highways and, in a fully connected, intelligent digital world, will need to connect everything, including people to vehicles, sensors, data, cloud resources, and even robotic agents. Fifth generation (5G) wireless networks, which are currently being deployed, offer significant advances beyond LTE, but may be unable to meet the full connectivity demands of the future digital society. Therefore, this article discusses technologies that will evolve wireless networks toward a sixth generation (6G) and which we consider as enablers for several potential 6G use cases. We provide a fullstack, system-level perspective on 6G scenarios and requirements, and select 6G technologies that can satisfy them either by improving the 5G design or by introducing completely new communication paradigms.

Toward 6G Networks: Use Cases and Technologies

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

For an autonomous vehicle to operate safely and effectively, an accurate and robust localization system is essential. While there are a variety of vehicle localization techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g., cornering and intersections) and different environmental conditions (e.g., darkness and snow). This paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localization systems with high accuracy and robustness, however, their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

This chapter contains sections titled: Introduction Overview of Multicarrier CDMA Systems Channel Model Performance of MC-CDMA System Performance of Overlapping Multicarrier DS-CDMA Systems Performance of Multicarrier DS-CDMA-I Systems Performance of AMC DS-CDMA Systems Performance of SFH/MC DS-CDMA Systems Chapter Summary and Conclusion 
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Overview of Multicarrier CDMA

COVID-19 causes a global epidemic infection, which is the most severe infection disaster in human history. In the absence of particular medication and vaccines, tracing and isolating the source of infection is the best option to slow the spread of the virus and reduce infection and death rates among the population. There are three main obstacles in the process of tracing the infection: 1) Patient's electronic health record is stored in a traditional centralized database that could be stolen and tampered with the infection data, 2) The confidential personal identity of the infected user may be revealed to a third party or organization, 3) Existing infection tracing systems do not trace infections from multiple dimensions. Either the system is location-based or individual-based tracing. In this work, we propose a global COVID-19 information sharing system that utilizes the Blockchain, Smart Contract, and Bluetooth technologies. The proposed system unifies location-based and Bluetooth-based contact tracing services into the Blockchain platform, where the automatically executed smart contracts are deployed so that users can get consistent and non-tamperable virus trails. The anonymous functionality provided by the Blockchain and Bluetooth technology protects the user's identity privacy. With our proposed analysis formula for estimating the probability of infection, users can take measures to protect themselves in advance. We also implement a prototype system to demonstrate the feasibility and effectiveness of our approach.

Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System.

We propose a Stackelberg game model for Moving Target Defense (MTD) where the defender periodically switches the state of a security sensitive resource to make it difficult for the attacker to identify the real configurations of the resource. Our model can incorporate various information structures. In this work, we focus on the worst-case scenario from the defender’s perspective where the attacker can observe the previous configurations used by the defender. This is a reasonable assumption especially when the attacker is sophisticated and persistent. By formulating the defender’s problem as a Markov Decision Process (MDP), we prove that the optimal switching strategy has a simple structure and derive an efficient value iteration algorithm to solve the MDP. We further study the case where the set of feasible switches can be modeled as a regular graph, where we solve the optimal strategy in an explicit way and derive various insights about how the node degree, graph size, and switching cost affect the MTD strategy. These observations are further verified on random graphs empirically.

A Stackelberg Game and Markov Modeling of Moving Target Defense

Advanced Persistent Threats (APT) are highly motivated and persistent, and they often operate in a stealthy way to avoid detection. Moreover, an advanced attacker may choose to approach insiders within the organization. Insider information can not only reduce the attack cost significantly but also make the attack more covert. Although stealthy attacks and insider threats have been considered separately in previous works, the coupling of the two is not well understood. As both types of threats are incentive driven, game theory is an appropriate tool to reason about the strategic behavior for each participant. We propose a non-zero sum three-player game model to study the interplay between APT and insider threats. Our model is built upon the two-player FlipIt game model for stealthy takeover with two extensions. First, we consider an asymmetric feedback structure where the defender is observable, while the attacker is stealthy and obtains delayed feedback about the defender's security updates. Second, we consider a three-player setting by introducing an insider with a double role, where it can help either the attacker or the defender according to the payoffs. We characterize the subgame perfect equilibria of a sequential game with the defender as the leader, and the insider and the attacker as the followers. We derive various insights from the game model, and discuss approaches for achieving more efficient defense in the face of both a stealthy attacker and an insider with double roles.

Stealthy attacks with insider information: A game theoretic model with asymmetric feedback

Internet of things (IoT) devices make up 30% of all network-connected endpoints, introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals. To address this increasing threat surface, every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted. Among all the solutions for security risks, firmware security analysis is essential to fix software bugs, patch vulnerabilities, or add new security features to protect users of those vulnerable devices. However, firmware security analysis has never been an easy job due to the diversity of the execution environment and the close source of firmware. These two distinct features complicate the operations to unpack firmware samples for detailed analysis. They also make it difficult to create visual environments to emulate the running of device firmware. Although researchers have developed many novel methods to overcome various challenges in the past decade, critical barriers impede firmware security analysis in practice. Therefore, this survey is motivated to systematically review and analyze the research challenges and their solutions, considering both breadth and depth. Specifically, based on the analysis perspectives, various methods that perform security analysis on IoT devices are introduced and classified into four categories. The challenges in each category are discussed in detail, and potential solutions are proposed subsequently. We then discuss the flaws of these solutions and provide future directions for this research field. This survey can be utilized by a broad range of readers, including software developers, cyber security researchers, and software security engineers, to better understand firmware security analysis.

Detecting Vulnerability on IoT Device Firmware: A Survey

We propose an inter-domain protocol (IDP) that enables cooperative resource allocation for secure and impairment-aware lightpath provisioning across multiple domains. The proposed IDP is implemented and experimentally demonstrated in a China-USA multi-domain control-plane SD-EON testbed.

Xiaotao Feng

Papers

Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System.

A Stackelberg Game and Markov Modeling of Moving Target Defense

Stealthy attacks with insider information: A game theoretic model with asymmetric feedback

Detecting Vulnerability on IoT Device Firmware: A Survey

Demonstration of OpenFlow-controlled cooperative resource allocation in a multi-domain SD-EON testbed across multiple nations