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Register a new userPreventing and Controlling Epidemics through Blockchain-Assisted AI-Enabled Networks
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Added by
Safa Otoum
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in next-generation networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users health records and other sensitive information. Through the adaptation of Artificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health-authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. Such a technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions.
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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
Efficient testing and vaccination protocols are critical aspects of epidemic management. To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies. Within our framework, we find that optimal intervention policies first target high-degree nodes before shifting to lower-degree nodes in a time-dependent manner. Using such optimal policies, it is possible to delay outbreaks and reduce incidence rates to a greater extent than uniform and reinforcement-learning-based interventions, particularly on certain scale-free networks.
Blockchain offers traceability and transparency to supply chain event data and hence can help overcome many challenges in supply chain management such as: data integrity, provenance and traceability. However, data privacy concerns such as the protection of trade secrets have hindered adoption of blockchain technology. Although consortium blockchains only allow authorised supply chain entities to read/write to the ledger, privacy preservation of trade secrets cannot be ascertained. In this work, we propose a privacy-preservation framework, PrivChain, to protect sensitive data on blockchain using zero knowledge proofs. PrivChain provides provenance and traceability without revealing any sensitive information to end-consumers or supply chain entities. Its novelty stems from: a) its ability to allow data owners to protect trade related information and instead provide proofs on the data, and b) an integrated incentive mechanism for entities providing valid proofs over provenance data. In particular, PrivChain uses Zero Knowledge Range Proofs (ZKRPs), an efficient variant of ZKPs, to provide origin information without disclosing the exact location of a supply chain product. Furthermore, the framework allows to compute proofs and commitments off-line, decoupling the computational overhead from blockchain. The proof verification process and incentive payment initiation are automated using blockchain transactions, smart contracts, and events. A proof of concept implementation on Hyperledger Fabric reveals a minimal overhead of using PrivChain for blockchain enabled supply chains.
Securing safe-driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. Motivated by this, in this article, we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then, we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.
Artificial intelligence (AI) will play an increasing role in cellular network deployment, configuration and management. This paper examines the security implications of AI-driven 6G radio access networks (RANs). While the expected timeline for 6G standardization is still several years out, pre-standardization efforts related to 6G security are already ongoing and will benefit from fundamental and experimental research. The Open RAN (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with AI control. Considering this architecture, we identify the critical threats to data driven network and physical layer elements, the corresponding countermeasures, and the research directions.
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