Optimizing blockchain networks with artificial intelligence: Towards efficient and reliable iot applications

Furqan Jameel*, Uzair Javaid, Biplab Sikdar, Imran Khan, George Mastorakis, Constandinos X. Mavromoustakis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

1 Citation (Scopus)

Abstract

Blockchain is one of the new tools which is still experiencing a serious lack of understanding and awareness in general public. Besides, to say that blockchain is a versatile technology with many applications would be an understatement. The several features of blockchain technology have led to an immense amount of research interest in the blockchain systems, especially from the perspective of enabling the Internet-of-things (IoT). It is expected that its adoption will be gradual, starting with researchers and leading up to startups and companies who will discover its potential for change. Then the general public who will demand changes would use it as an everyday technology. Finally, organizations that had until then resisted change would adopt it at a large scale, thus, paving the way for globalization of blockchain. In this regard, it is important to understand that the blockchain is not a single object, a single trend or a particular feature, but a composition and collection of several autonomous as well as manually operated entities. Because of these modular features, the blockchain offers an infinite range of application choices. Another paradigm which is emerging in parallel is the so-called artificial intelligence. The evolution of artificial intelligence is an incredible tale which started with neural networks in the last century and continued its way to the development of complex deep learning techniques. From the perspective of IoT networks, it has the potential to provide services like ultra-reliable and low-latency communications (uRLLC), long-distance and high-mobility communications (LDHMC), and ultra-massive machine-type communications (umMTC) which can be the key to realizing tactile Internet for next generation of wireless networks. Moreover, artificial intelligence techniques do not require a mathematically tractable model to optimize the performance of the devices. This means that they can be used for a number of environments that includes both indoor and outdoor communication scenarios. Although the applications of blockchain in IoT networks have much potential, there are several open issues and research challenges that need to be addressed first. In this context, one of the main issues in blockchain networks is branching, i.e., forking event. The forking not only introduces excessive overhead in the network but can also lead to potential security attacks. Thus, to overcome this interesting problem, we use deep neural networks in a distributed IoT setup. More specifically, the communication delay between the IoT miner and communication point is directly related to the rate of the link. We aim to maximize the rate of the communication link from IoT miner to the communication point with the help of neural networks. The numerical results show a significant improvement in the rate which can lead to a reduction in transmission delays. We anticipate that this work on artificial intelligence-enabled blockchain system would pave the way for future studies and research.

Original languageEnglish
Title of host publicationConvergence of Artificial Intelligence and the Internet of Things
Pages299-321
Number of pages23
ISBN (Electronic)978-3-030-44907-0
DOIs
Publication statusPublished - 1 Jan 2020
MoE publication typeA3 Part of a book or another research book

Publication series

NameInternet of Things
ISSN (Print)2199-1073
ISSN (Electronic)2199-1081

Keywords

  • Artificial intelligence
  • Blockchain
  • Deep learning
  • Internet-of-things (IoT)
  • Neural networks
  • umMTC
  • uRLLC

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  • Cite this

    Jameel, F., Javaid, U., Sikdar, B., Khan, I., Mastorakis, G., & Mavromoustakis, C. X. (2020). Optimizing blockchain networks with artificial intelligence: Towards efficient and reliable iot applications. In Convergence of Artificial Intelligence and the Internet of Things (pp. 299-321). (Internet of Things). https://doi.org/10.1007/978-3-030-44907-0_12