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Cited by 23Blockchain Technology Applications and SecurityInternet of Things and AICloud Computing and Resource Management

Aiblock: blockchain based lightweight framework for serverless computing using ai

Open access
Golec, Muhammed, Chowdhury, Deepraj, Jaglan, Shivam, Gill, Sukhpal Singh, Uhlig, Steve·2022·2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
DOI 10.1109/ccgrid54584.2022.00106
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Abstract

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.

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Integrity signals

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Crossref · 10.1109/ccgrid54584.2022.00106

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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  • Mobile Edge Computing is centred on applications intended for large numbers of mobile devices.

    “Mobile Edge Computing as centred on applications intended for large numbers of mobile devices,”

    From Edge AI: A survey (2023) · cites this paper as [48]

    The Introduction (page unavailable) discusses the growing number of IoT devices and serverless cloud computing, but does not characterize Mobile Edge Computing. Searches for Mobile Edge Computing and applications for large numbers of mobile devices found no passage addressing that claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Micro Data Centres are optimal for industrial applications in specific locations or at temporary sites.

    “Micro Data Centres as the optimum for industrial applications in specific locations or at temporary sites”

    From Edge AI: A survey (2023) · cites this paper as [48]

    The Introduction discusses cloud and serverless computing for IoT, while the Hardware Instruments & Software Components section describes a serverless datacenter used for COVID-19 detection. Searches for micro data centres and temporary industrial sites found no passage assessing whether micro data centres are optimal for those applications. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Fog Computing is the optimal solution for IoT applications to minimize the time required for time-critical processing and Big Data analytics.

    “Fog Computing as the optimal solution for IoT applications to minimize the time required for time-critical processing and Big Data analytics;”

    From Edge AI: A survey (2023) · cites this paper as [48]

    The Introduction (page unavailable) discusses processing large amounts of IoT data using cloud and serverless computing, not fog computing. Searches for fog computing, time-critical processing, and Big Data analytics found no passage calling fog computing optimal or saying it minimizes processing time for those tasks. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

  • Cloudlets are dedicated either to small-enterprise scenarios or can be expanded to publicly funded schemes with free access and open source software.

    “while cloudlets are dedicated either (as originally envisioned) to small-enterprise scenarios or can be expanded to publicly funded schemes with free access and open source software [48].”

    From Edge AI: A survey (2023) · cites this paper as [48]

    The Introduction discusses cloud and serverless computing, while the Conclusions describe AIBLOCK; neither addresses cloudlets or their intended users. Searches for cloudlets, small-enterprise use, publicly funded access, and open-source schemes found no supporting passage in our copy. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Edge computing removes the need for long-distance data transmission when sufficient local computational resources exist.

    “Edge Computing eliminates the need for such long-reach data transmission if more local computational resources are available [48].”

    From Edge AI: A survey (2023) · cites this paper as [48]

    The Introduction discusses IoT devices' limited processing capacity and the use of cloud computing, but does not say that edge computing avoids long-distance data transmission when local resources are sufficient. The Hardware Instruments & Software Components section instead describes a gateway transmitting data to a cloud-based serverless platform. Searches for local processing and transmission did not find a passage addressing the claimed edge-computing benefit; no page numbers were available in our copy. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 20

  1. L. Yang, S. Liu, J. Liu, Z. Zhang, X. Wan, B. Huang, Y. Chen, and Y. Zhang, "Covid-19: immunopathogenesis and immunotherapeutics," Signal transduction and tar- geted therapy, vol. 5, no. 1, pp. 1-8, 2020.
  2. S. Chen, H. Xu, D. Liu, B. Hu, and H. Wang, "A vision of iot: Applications, challenges, and opportunities with china perspective," IEEE Internet of Things journal, vol. 1, no. 4, pp. 349-359, 2014.
  3. Y. K. Teoh, S. S. Gill, and A. K. Parlikad, "Iot and fog computing based predictive maintenance model for effective asset management in industry 4.0 using machine learning," IEEE Internet of Things Journal, 2021.
  4. S. S. Gill et al., "Ai for next generation comput- ing: Emerging trends and future directions," Internet of Things, vol. 19, p. 100514, 2022.
  5. B. Liu, X. L. Yu, S. Chen, X. Xu, and L. Zhu, "Blockchain based data integrity service framework for iot data," in 2017 IEEE International Conference on Web Services (ICWS). IEEE, 2017, pp. 468-475.
  6. P. Wei, D. Wang, Y. Zhao, S. K. S. Tyagi, and N. Kumar, "Blockchain data-based cloud data integrity protection mechanism," Future Generation Computer Systems, vol. 102, pp. 902-911, 2020.
  7. P. Tasatanattakool and C. Techapanupreeda, "Blockchain: Challenges and applications," in 2018 International Con- ference on Information Networking (ICOIN), 2018, pp. 473-475.
  8. N. E. Nwogbaga, R. Latip, L. S. Affendey, and A. R. A. Rahiman, "Investigation into the effect of data reduction in offloadable task for distributed iot-fog-cloud comput- ing," Journal of Cloud Computing, vol. 10, no. 1, pp. 1-12, 2021.
  9. T. Lynn, P. Rosati, A. Lejeune, and V. Emeakaroha, "A preliminary review of enterprise serverless cloud computing (function-as-a-service) platforms," in 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2017, pp. 162-169.
  10. M. S. Aslanpour, A. N. Toosi, C. Cicconetti, B. Javadi, P. Sbarski, D. Taibi, M. Assuncao, S. S. Gill, R. Gaire, and S. Dustdar, "Serverless edge computing: vision and challenges," in 2021 Australasian Computer Science Week Multiconference, 2021, pp. 1-10.
  11. N. Nasser, Q. Emad-ul Haq, M. Imran, A. Ali, I. Razzak, and A. Al-Helali, "A smart healthcare framework for detection and monitoring of covid-19 using iot and cloud computing," Neural Computing and Applications, pp. 1- 15, 2021.
  12. M. W. Hasan, "Covid-19 fever symptom detection based on iot cloud," International Journal of Electrical and Computer Engineering, vol. 11, no. 2, p. 1823, 2021.
  13. R. Mukherjee, A. Kundu, I. Mukherjee, D. Gupta, P. Ti- wari, A. Khanna, and M. Shorfuzzaman, "Iot-cloud based healthcare model for covid-19 detection: an enhanced k- nearest neighbour classifier based approach," Computing, pp. 1-21, 2021.
  14. M. Otoom, N. Otoum, M. A. Alzubaidi, Y. Etoom, and R. Banihani, "An iot-based framework for early identi- fication and monitoring of covid-19 cases," Biomedical signal processing and control, vol. 62, p. 102149, 2020.
  15. M. Rimsan, A. K. Mahmood, M. Umair, and F. Has- san, "Covid-19: A novel framework to globally track coronavirus infected patients using blockchain," in 2020 International Conference on Computational Intelligence (ICCI). IEEE, 2020, pp. 70-74.
  16. M. Siekkinen, M. Hiienkari, J. K. Nurminen, and J. Nieminen, "How low energy is bluetooth low energy? comparative measurements with zigbee/802.15. 4," in IEEE wireless communications and networking confer- ence workshops. IEEE, 2012, pp. 232-237.
  17. Y. Xiao and J. Rosdahl, "Throughput and delay limits of ieee 802.11," IEEE Communications Letters, vol. 6, no. 8, pp. 355-357, 2002.
  18. K. Xiong and H. Perros, "Service performance and analy- sis in cloud computing," in 2009 Congress on Services-I. IEEE, 2009, pp. 693-700.
  19. P. Vahidinia, B. Farahani, and F. S. Aliee, "Cold start in serverless computing: Current trends and mitigation strategies," in 2020 International Conference on Omni- layer Intelligent Systems (COINS). IEEE, 2020, pp. 1-7.
  20. A. Singh, N. Thakur, and A. Sharma, "A review of supervised machine learning algorithms," in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). Ieee, 2016, pp. 1310-1315.

Cited by

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Authors
Golec, M., Chowdhury, D., Jaglan, S., Gill, S. S., & Uhlig, S.
Affiliations
  • Queen Mary University of London (GB)
  • International Institute of Information Technology (IN)
  • Indian Institute of Technology Delhi (IN)
ORCID
Venue
2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Publisher
IEEE
Published
2022-05
Pages
886-892
DOI
10.1109/ccgrid54584.2022.00106
OpenAlex
W4286307880
Cited by
23
References
1
Citations by year
2026: 3 · 2025: 2 · 2024: 8 · 2023: 8 · 2022: 2
FWCI
4.47
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (publishedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
blockchain, serverless computing, data integrity, COVID-19 diagnosis, Google Cloud Functions, lightweight framework, classifier comparison, decision tree, memory load, Industry 4.0, healthcare data security

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