Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 13 September 2024 (08:40:12 CEST)
How to cite:
---, A. C.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat. Preprints2024, 2024091035. https://doi.org/10.20944/preprints202409.1035.v1
---, A. C.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat. Preprints 2024, 2024091035. https://doi.org/10.20944/preprints202409.1035.v1
---, A. C.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat. Preprints2024, 2024091035. https://doi.org/10.20944/preprints202409.1035.v1
APA Style
---, A. C., Sarma, K. K., Misra, D. D., Guha, K., & Iannacci, J. (2024). IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat. Preprints. https://doi.org/10.20944/preprints202409.1035.v1
Chicago/Turabian Style
---, A. C., Koushik Guha and Jacopo Iannacci. 2024 "IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat" Preprints. https://doi.org/10.20944/preprints202409.1035.v1
Abstract
The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.
Keywords
deep learning; deep transfer learning; contact tracing; facemask; social distancing; edge computing; cyber-attack; Federated Learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
