iFaaSBus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning

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Golec M., Ozturac R., Pooranian Z., Gill S. S., Buyya R.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, cilt.18, sa.5, ss.3522-3529, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 5
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tii.2021.3095466
  • Dergi Adı: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3522-3529
  • Anahtar Kelimeler: COVID-19, Servers, Security, Diseases, Privacy, Internet of Things, Authorization, Artificial intelligence (AI), function as a service (FaaS), Internet of Things (IoT), machine learning (ML), security and privacy, serverless computing
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.