The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004. FAA is required to license or permit commercial space launches, but to allow the space tourism industry to develop, the act prohibited FAA from regulating crew and spaceflight participant safety before 2012—a moratorium that was later extended but will now expire on September 30, 2015. Since October 2014, there have been three mishaps involving FAA licensed or permitted launches.

美聯邦航空廳(Federal Aviation Administration)의 航空機 製作檢査 制度의 現況

The Internet of Drones (IoD) recently gained momentum due to its high adaptability to a wide variety of complex scenarios. Indeed, Unmanned Aerial Vehicles (UAVs) can successfully be employed in different applications, such as agriculture, search and rescue missions, surveillance systems, mission-critical services, among others, thanks to some technological and practical advantages: high mobility, capability to extend wireless coverage areas, or ability to reach places inaccessible to humans. Moreover, the employment of drones promisingly improves the performance parameters of different network architectures, e.g., reliability, connectivity, throughput, and delay. Nevertheless, the adoption of networks of drones gives rise to several issues related to the intrinsic unreliability of the wireless medium, the duration of batteries, and the high mobility degree, which may cause frequent topology changes. Also security and privacy issues need to be properly investigated. This explains the very large number of works produced in the recent literature on IoD-related topics. With respect to other surveys on IoD-related topics, the goal of the present work is to categorize the multifaceted aspects of IoD, proposing a classification approach of the IoD environment that develops along two main directions. The IoD is initially introduced according to the possible applications and reference operating scenarios. On top of that, this work discussed with its applicability with a dedicated focus on the economical/social implications that the adoption of drones at scale may have. At a macroscopic level, it follows the structure of the Internet protocol stack, starting from the physical layer and extending to the upper layers, without neglecting cross-layer and optimization approaches. At a finer level, all the most relevant works belonging to each layer of the stack are further classified, according to the different issues that propose a peculiar challenges for each layer, and highlighting the most relevant differences with the other surveys present in literature. To provide a deeper insight, the present work embraces many facets of the IoD, with a thorough examination of privacy and security considerations. Finally, a discussion on the main research challenges and possible future directions is carried out, focusing on both open issues and the most promising technologies that deserve to be further developed in the IoD field.

An extensive survey on the Internet of Drones

The blockchain-enabled internet of medical things (IoMT) is an emerging paradigm that could provide strong trust establishment and ensure the traceability of data sharing in the IoMT networks. One of the fundamental building blocks for Blockchain is Elliptic Curve Digital Signature Algorithm (ECDSA). Nevertheless, when processing a large number of transactions, the verification of multiple signatures will incur cumbersome overhead to the nodes in Blockchain. Although batch verification is able to provide a promising approach that verifies multiple signatures simultaneously and efficiently, the upper bound of batch size is limited to small-scale and the efficiency will drop rapidly as the batch size grows in the state-of-the-art ECDSA batch schemes. Meanwhile, most of the existing researches only focus on improving the efficiency of batch verification algorithms in various cryptosystem while ignoring the identification of invalid signatures, which could cause severe performance degradation when the batch verification fails. Motivated by these observations, this paper proposes an efficient and large-scale batch verification scheme with group testing technology based on ECDSA. The application of the presented protocols in Bitcoin and Hyperledger Fabric has been analyzed as supportive and effective. When the batch verification returns a false result, we utilize group testing technology to improve the efficiency of identifying invalid signatures. Comprehensive simulation results demonstrate that our protocol outperforms the related ECDSA batch verification schemes. 

On the Design of Blockchain-Based ECDSA With Fault-Tolerant Batch Verification Protocol for Blockchain-Enabled IoMT

Unmanned aerial vehicles (UAVs), also known as drones, once centric to military applications, are presently finding their way in many civilian and commercial applications. If national legislations permit UAVs to operate autonomously, one will see the skies become populated with many small UAVs, each one performing various tasks such as mail and package delivery, traffic monitoring, event filming, surveillance, search and rescue, and other applications. Thus, advancing to multiple small UAVs from a single large UAV has resulted in a new clan of networks known as flying ad-hoc networks (FANETs). Such networks provide reliability, ease of deployment, and relatively low operating costs by offering a robust communication network among the UAVs and base stations (BS). Although FANETs offer many benefits, there also exist a number of challenges that need to be addressed; the most significant of these being the communication one. Therefore, the article aims to provide insights into the key enabling communication technologies through the investigation of data rate, spectrum type, coverage, and latency. Moreover, application scenarios along with the feasibility of key enabling technologies are also examined. Finally, challenges and open research topics are discussed to further hone the research work.

A Review on Communications Perspective of Flying Ad-Hoc Networks: Key Enabling Wireless Technologies, Applications, Challenges and Open Research Topics

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services.

/pdf/zero-bias-deep-learning-for-accurate-identification-of-rppz79eg0e.pdf

Zero-Bias Deep Learning for Accurate Identification of Internet-of-Things (IoT) Devices

Transfer learning (TL) has been successfully applied to many real-world problems that traditional machine learning (ML) cannot handle, such as image processing, speech recognition, and natural language processing (NLP). Commonly, TL tends to address three main problems of traditional machine learning: (1) insufficient labeled data, (2) incompatible computation power, and (3) distribution mismatch. In general, TL can be organized into four categories: transductive learning, inductive learning, unsupervised learning, and negative learning. Furthermore, each category can be organized into four learning types: learning on instances, learning on features, learning on parameters, and learning on relations. This article presents a comprehensive survey on TL. In addition, this article presents the state of the art, current trends, applications, and open challenges.

A Decade Survey of Transfer Learning (2010–2020)

Unmanned aircraft systems (UAS), or unmanned aerial vehicles, often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure, and privacy-respecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying, and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or UAS. Over the past five years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this tutorial, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UAS-based threats. The objective of this tutorial is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.

Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges, and Future Trends

Lightweight blockchain assisted secure routing of swarm UAS networking

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices’ identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, non-cryptographic IoT device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Non-cryptographic approaches require more effort and are not yet adequately investigated. In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT device identification and detection into four categories: device-specific pattern recognition, Deep Learning enabled device identification, unsupervised device identification, and abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

The ubiquitous deployment of 5G New Radio (5G NR) accelerates the evolution in many fields. With the enhancement of 5G NR, unmanned aerial vehicle (UAV) swarm networking can gain more flexibility, reliability, and elasticity to assist residents or workers to finish missions of high complexity and risks remotely. The beamforming of 5G NR can improve the accuracy and the flexibility of connections between the mobile devices. With a high throughput guaranteed in the UAV swarm networking, the remote can deliver specific instructions with the high accuracy of requirement. Further, a reliable and high volume throughput assures multiple UAV swarm networking can exchange information to collaborate and cooperate underneath different mission allocations. In this article, we propose a cell wall paradigm to escalate the flexibility and the throughput for the heterogeneous 5G-enabled UAV swarm networking. With the construction of the cell wall, we formulate and summarize the throughput optimization into the capacity maximal problem to achieve the max–min throughput. Thereafter, we implement an optimal edge coloring solution with the upper bound and the lower bound of color selection to scheduling the orders of active links. Based on the optimal scheduling for each separate networking, we focus on the balance of the inter- and intra-active links to mitigate collisions on the UAVs to strengthen the throughput of UAV swarm networking for the collaboration and corporation of the heterogeneous UAV swarms on a large scale. The evaluation shows the optimal edge coloring approach can improve the throughput for UAV swarm networking at 73.8% maximally. With the optimal setting of $t_{s} = 0.01$ s and $t_{g} = 0.01$ s, we can achieve the optimal throughput for the collaboration of the heterogeneous UAV swarms globally.

Jian Wang

Papers

A Decade Survey of Transfer Learning (2010–2020)

Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges, and Future Trends

Lightweight blockchain assisted secure routing of swarm UAS networking

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

Extensive Throughput Enhancement For 5G-Enabled UAV Swarm Networking