Blockchain-Empowered Decentralized Storage in Air-to-Ground Industrial Networks

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service

Blockchain-empowered decentralized storage in air-to-ground industrial networks

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service

Blockchain-Empowered Decentralized Storage in Air-to-Ground Industrial Networks

Blockchain has raised an evolution in the cyber network by using distributed storage, cryptography algorithms and smart contract. Many areas are benefiting from this technology, such as data integrity, security as well as authentication and authorization. Internet of Things network is often suffering from such security issues, obstructing its development in scales. In this paper, we employ blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To make both air and ground sensors trading in the decentralized network, we design a mutual benefit consensus process to the uneven equilibrium distribution of resources among the participators. We use the Cournot model to optimize the active density factor set in the heterogeneous air network and then employ Nash equilibrium to balance the number of ground supporters, which is subject to the achievable average downlink rate between the air sensors and the ground sensors. Finally, numerical results are provided to demonstrate the beneficial properties of the proposed consensus process for air-to-ground network, and show the maximum active sensors density utilization of air network to achieve a higher quality of service

A survey of blockchain and artificial intelligence for 6G wireless communications

The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resource-constrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). Moreover, we thoroughly discuss operating frequencies, visions, and requirements from the 6G perspective. We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks. We hope this surve..

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization

Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies with wide ranging applications, including surveillance, mapping, remote sensing, search and rescue, and disaster management. As sophisticated Unmanned Aerial Vehicle (UAV) increasingly operate in collaborative swarms, joint optimization challenges arise, such as flight trajectories, scheduling, altitude, Aerial Base Stations (ABS), energy harvesting, power transfer, resource allocation, and power consumption. However, the widespread adoption of UAV networks has been hindered by challenges related to optimal Three-Dimensional (3D) deployment, trajectory optimization, wireless and computational resource allocation, and limited flight durations when operating as ABSs. Crucially, the broadcast nature of UAV-assisted wireless networks renders them susceptible to privacy and security threats such as Distributed Denial-of-Service (DDoS) replay, impersonation, message injection, spoofing, malware infection, eavesdropping, and line of-interference attacks. This study aims to address these privacy and security challenges by leveraging blockchain technology’s potential to secure data and delivery in UAV communication networks. With amalgamation of blockchain, this study seeks to harness its inherent immutability and cryptographic properties to ensure secure and tamper-proof data transmission, promote trust and transparency among stakeholders, enable automated Smart Contract (SC) for secure delivery, and facilitate standardization and interoperability across platforms. Specifically, blockchain can secure UAV network privacy and security through data privacy and integrity, secure delivery and tracking, access control, identity management, and resilience against cyber-attacks. Furthermore, this study explores the synergies among blockchain, UAV networks, and Federated Learning (FL) for privacy-preserving intelligent applications in healthcare and wireless networks. FL enables collaborative training of Machine Learning (ML) models without sharing raw data, ensuring data privacy. By integrating FL with blockchain-assisted UAV networks, this study aims to revolutionize future intelligent applications, particularly in time-sensitive and privacy-critical domains. Overall, this thesis contributes to the field by providing a comprehensive analysis of integrating blockchain, FL, and UAV networks, beyond Fifth-Generation (5G) communication networks. It addresses privacy and security concerns related to data and delivery, thereby enabling secure, reliable, and intelligent applications in various sectors

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

TACTICAL BLOCKCHAIN TO PROVIDE DATA PROVENANCE IN SUPPORT OF INTERNET OF BATTLEFIELD THINGS AND BIG DATA ANALYTICS

This capstone project evaluated the use of blockchain technology to address a number of challenges with increasing amounts of disparate sensor data and an information-rich landscape that can quickly overwhelm effective decision-making processes. The team explored how blockchain can be used in a variety of defense applications to verify users, validate sensor data fed into artificial intelligence models, limit access to data, and provide an audit trail across the data life cycle. The team developed a conceptual design for implementing blockchain for tactical data, artificial intelligence, and machine learning applications; identified challenges and limitations involved in implementing blockchain for the tactical domain; described the benefits of blockchain for these various applications; and evaluated this project’s findings to propose future research into a wider set of blockchain applications. The team did this through the development of three use cases. One use case demonstrated the use of blockchain at the tactical edge in a “data light” information environment. The second use case explored the use of blockchain in securing medical information in the electronic health record. The third use case studied blockchain’s application in the use of multiple sensors collecting data for chemical weapons defense to support measurement and signature intelligence analysis using artificial intelligence and machine learning.Civilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyApproved for public release. Distribution is unlimited