Post‐Quantum Blockchain–Enabled Services in Scalable Smart Cities

Prateek, Kumar; Maity, Soumyadev

doi:10.1002/9781119836728.ch11

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…Future studies are necessary to bridge the gap between standard multiscale implementations and QPU-based strategies in order to address excited states description and a proper rationalization of the possible approaches to better understand which solution among the variational methods is more scalable. Lastly, it is important to specify that most of the mentioned studies benefit from software development kits that emulate quantum processors on classical architectures. ,,− To this end, we envisage that the continuous progresses in engineering highly efficient quantum emulators will be driven and simultaneously will drive new advances in multiscale modeling research for the coming decades.…”

Section: State Of the Artmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

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The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

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Section: State Of the Artmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

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show abstract

“…21 Also, the advent of quantum computing has reduced the time complexity of all difficult problems like the discrete log problem and integer factorization from millions of years to a few seconds in amalgamation with blockchain to achieve better outcomes for streaming data analysis respectively. 22 However, based on the nature and complexity of the application, the selection of a permissioned, private, or permissionless cloud-based environment may add up to the cost factor if the requirements are not thoroughly analyzed. 23 If the number of messages is drastically increasing due to technology awareness among social media users, then traditional approaches may fail.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of IoT sensors are used to overcome traffic difficulties in heavily populated places that result in unforeseen delays in providing services either in terms of a medical emergency or a supply‐chain‐based Inventory delivery system 21 . Also, the advent of quantum computing has reduced the time complexity of all difficult problems like the discrete log problem and integer factorization from millions of years to a few seconds in amalgamation with blockchain to achieve better outcomes for streaming data analysis respectively 22 …”

Section: Related Workmentioning

confidence: 99%

Secure data access using blockchain technology through IoT cloud and fabric environment

Gupta,

Chithaluru,

El Barachi

et al. 2023

Security and Privacy

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In the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.

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“…By using QKD, the protocol could ensure that the authentication information is protected from eavesdropping and tampering, providing enhanced security and privacy 7 . A post‐quantum blockchain‐enabled service in a scalable smart city would leverage the advantages of quantum computing to enhance the security and privacy of blockchain‐based services, providing a promising solution for enabling secure and privacy‐preserving services in smart cities 8 . Grid could provide enhanced security and privacy compared to traditional authentication schemes.…”

Section: Introductionmentioning

confidence: 99%

Fog computing security: A review

Jumani

Shi

Laghari

et al. 2023

Security and Privacy

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Fog computing, also known as edge computing, is a decentralized computing architecture that brings computing and data storage closer to the users and devices that need it. It offers several advantages over traditional cloud computing, such as lower latency, improved reliability, and enhanced security. As the Internet of Things continues to grow, the demand for fog computing is also increasing, making it an important topic for research and development. However, the deployment of fog computing also brings new technical challenges and security risks. For example, fog nodes are often deployed in resource‐constrained environments and are exposed to potential security threats, such as malware and attacks on devices connected to the network. In addition, the decentralized nature of fog computing creates new challenges in terms of privacy, security, and data management. This survey aims to address these technical challenges and research gaps in the field of fog computing security. It provides an overview of the current state of fog computing and its security challenges, and identifies key areas for future research. The survey also highlights the importance of fog computing security and the need for continued investment in this area in order to fully realize the potential of this promising technology.

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Post‐Quantum Blockchain–Enabled Services in Scalable Smart Cities

Cited by 8 publications

References 41 publications

A Vision for the Future of Multiscale Modeling

A Vision for the Future of Multiscale Modeling

Secure data access using blockchain technology through IoT cloud and fabric environment

Fog computing security: A review

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