About this Research Topic
IoTs became an intrinsic part of our lives, generating, collecting, exchanging, and processing massive amounts of data, which can be highly valuable or sensitive for their owners and users. Providing SPT for IoT presents a significant challenge for at least two main reasons. First, physical locations of IoT devices are often easily accessible to potential adversaries, and beyond the scope of effective control of their users or owners.
Second, the traditional SPT solutions, preceding the IoT era, were not designed to address the unique IoT characteristics, including their relatively small processing power, a high rate of data generation, and a huge potential to significantly affect human life or well-being. Hence, SPT solutions for IoT must often be reinvented to match the specifics of IoT itself or even particular types of IoT infrastructure (e.g., an underwater IoT).
Two new technologies hold a promise of delivering a powerful and comprehensive combination for successful SPT solutions: Artificial Intelligence and Distributed Ledger Technologies. AI –including Machine Learning and its human-mimicking Deep Learning subcategory– can be used, for example, to analyze data from IoT devices and to identify potential threats. DLTs –including linear blockchains, network-based hashgraphs, and other-directed acyclic graph structures, as well as hybrid (linear-network) data structures– can be used, among others, to securely store and authenticate data and to authorize devices.
Synergistic solutions utilizing both AI and DLT, being both intelligent and highly distributed, are especially promising. The multi-faceted roles of AI need no elaboration. As far as DLT is concerned, among others it can facilitate providing trust in novel ways, such as the “trust-to-trust” principle (generalizing the “end-to-end” principle); this gives the end user explicit control over trust decisions, removing the incorrect assumption equating “the end node” with “the trusted node.”
The goal of this Research Topic is to bring together the leading researchers, scholars, and experts from around the world to share their insights, discoveries, and innovations in the applications of AI and DLTs for enhancing security, privacy, and trust in and for IoT ecosystems.
We welcome original articles, reviews, case studies, and theoretical papers that explore, among others, the following topics in the area of SPT solutions for IoT:
• AI- and DLT-based real-time anomaly and threat intelligence, prediction, prevention, detection, and adaptation, dealing with unauthorized access attempts by both users and devices
• Use of AI and DLTs for –especially decentralized– authentication, authorization and access control to devices and data
• Building SPT solutions for IoT upon DLT-based infrastructures such as Web3 (“the Internet owned by the builders and users, orchestrated with tokens”)
• DLT-based SPT-enhancing decentralized mechanisms for recording device transactions as well as for storing and sharing data with authorized users and devices
• Protecting privacy of data, including AI-based anonymization and sanitization
• Trust models and architectures using AI and DLTs, incl. trust-to-trust architectures
• Case studies and real-world applications of AI and DLTs, including applications in resource-constrained and special (e.g., underwater or space) environments
Second, the traditional SPT solutions, preceding the IoT era, were not designed to address the unique IoT characteristics, including their relatively small processing power, a high rate of data generation, and a huge potential to significantly affect human life or well-being. Hence, SPT solutions for IoT must often be reinvented to match the specifics of IoT itself or even particular types of IoT infrastructure (e.g., an underwater IoT).
Two new technologies hold a promise of delivering a powerful and comprehensive combination for successful SPT solutions: Artificial Intelligence and Distributed Ledger Technologies. AI –including Machine Learning and its human-mimicking Deep Learning subcategory– can be used, for example, to analyze data from IoT devices and to identify potential threats. DLTs –including linear blockchains, network-based hashgraphs, and other-directed acyclic graph structures, as well as hybrid (linear-network) data structures– can be used, among others, to securely store and authenticate data and to authorize devices.
Synergistic solutions utilizing both AI and DLT, being both intelligent and highly distributed, are especially promising. The multi-faceted roles of AI need no elaboration. As far as DLT is concerned, among others it can facilitate providing trust in novel ways, such as the “trust-to-trust” principle (generalizing the “end-to-end” principle); this gives the end user explicit control over trust decisions, removing the incorrect assumption equating “the end node” with “the trusted node.”
The goal of this Research Topic is to bring together the leading researchers, scholars, and experts from around the world to share their insights, discoveries, and innovations in the applications of AI and DLTs for enhancing security, privacy, and trust in and for IoT ecosystems.
We welcome original articles, reviews, case studies, and theoretical papers that explore, among others, the following topics in the area of SPT solutions for IoT:
• AI- and DLT-based real-time anomaly and threat intelligence, prediction, prevention, detection, and adaptation, dealing with unauthorized access attempts by both users and devices
• Use of AI and DLTs for –especially decentralized– authentication, authorization and access control to devices and data
• Building SPT solutions for IoT upon DLT-based infrastructures such as Web3 (“the Internet owned by the builders and users, orchestrated with tokens”)
• DLT-based SPT-enhancing decentralized mechanisms for recording device transactions as well as for storing and sharing data with authorized users and devices
• Protecting privacy of data, including AI-based anonymization and sanitization
• Trust models and architectures using AI and DLTs, incl. trust-to-trust architectures
• Case studies and real-world applications of AI and DLTs, including applications in resource-constrained and special (e.g., underwater or space) environments
Keywords: Artificial Intelligence, blockchain, cybersecurity, Distributed Ledger Technologies, Internet of Things, privacy, security
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
