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- Bhattacharya, Sweta, et al.
(författare)
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Blockchain for Internet of Underwater Things: State-of-the-Art, Applications, Challenges, and Future Directions
- 2022
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 14:23, s. 15659-15659
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Underwater Things (IoUT) has become widely popular in the past decade as it has huge prospects for the economy due to its applicability in various use cases such as environmental monitoring, disaster management, localization, defense, underwater exploration, and so on. However, each of these use cases poses specific challenges with respect to security, privacy, transparency, and traceability, which can be addressed by the integration of blockchain with the IoUT. Blockchain is a Distributed Ledger Technology (DLT) that consists of series of blocks chained up in chronological order in a distributed network. In this paper, we present a first-of-its-kind survey on the integration of blockchain with the IoUT. This paper initially discusses the blockchain technology and the IoUT and points out the benefits of integrating blockchain technology with IoUT systems. An overview of various applications, the respective challenges, and the possible future directions of blockchain-enabled IoUT systems is also presented in this survey, and finally, the work sheds light on the critical aspects of IoUT systems and will enable researchers to address the challenges using blockchain technology.
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| 2. |
- Victor, Nancy, et al.
(författare)
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Federated learning for iout : Concepts, applications, challenges and future directions
- 2022
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Ingår i: IEEE Internet of Things Magazine (IoT). - 2576-3180 .- 2576-3199. ; 5:4
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.
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