Smart City Gnosys
Smart city article details
| Title | Edge Computing For Smart-City Human Habitat: A Pandemic-Resilient, Ai-Powered Framework |
|---|---|
| ID_Doc | 21753 |
| Authors | Choudhury A.; Sarma K.K.; Misra D.D.; Guha K.; Iannacci J. |
| Year | 2024 |
| Published | Journal of Sensor and Actuator Networks, 13, 6 |
| DOI | http://dx.doi.org/10.3390/jsan13060076 |
| 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 infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an 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 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents 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 (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies. © 2024 by the authors. |
| Author Keywords | contact tracing; cyber-attack; deep learning; deep transfer learning; edge computing; face mask; federated learning; social distancing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
12841  | 0.891 | Righi R.d.R.; Rodrigues V.F.; da Costa C.A.; Eskofier B. | Breaking Down The Data Path In Digital Health: From Edge To Fog And Beyond | IEEE Pervasive Computing, 24, 1 (2025) |
| 26782 | 0.889 | Alrashdi I. | Fog-Based Deep Learning Framework For Real-Time Pandemic Screening In Smart Cities From Multi-Site Tomographies | BMC Medical Imaging, 24, 1 (2024) |
| 21728 | 0.887 | Hanifa M.S.M.; Alnaamani K.S.H.; Natarajan G.; Elango E. | Edge Ai For Connected Healthcare In Internet Of Medical Things For Smart Cities | Edge AI for Industry 5.0 and Healthcare 5.0 Applications (2025) |
| 3531 | 0.879 | Ezugwu A.E.; Hashem I.A.T.; Oyelade O.N.; Almutari M.; Al-Garadi M.A.; Abdullahi I.N.; Otegbeye O.; Shukla A.K.; Chiroma H. | A Novel Smart City-Based Framework On Perspectives For Application Of Machine Learning In Combating Covid-19 | BioMed Research International, 2021 (2021) |
| 34257 | 0.879 | Kakulapati V.; Gupta P. | Issues And Challenges Of Intelligent Iomt For Edge Computing | Health 5.0: Concepts, Challenges, and Solutions (2025) |
| 28858 | 0.878 | Roy D.S.; Hussain M.W.; Reddy K.H.K.; Gupta D. | Healthcare-Driven Intelligent Computing Paradigms To Secure Futuristic Smart Cities | Healthcare-Driven Intelligent Computing Paradigms to Secure Futuristic Smart Cities (2024) |
| 33115 | 0.875 | Herath H.M.K.K.M.B. | Internet-Assisted Data Intelligence For Pandemic Prediction: An Intelligent Framework | Studies in Computational Intelligence, 994 (2022) |
| 39157 | 0.874 | Kamruzzaman M.M. | New Opportunities, Challenges, And Applications Of Edge-Ai For Connected Healthcare In Smart Cities | 2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings (2021) |
| 30778 | 0.873 | Cruz Castañeda W.A.; Bertemes Filho P. | Improvement Of An Edge-Iot Architecture Driven By Artificial Intelligence For Smart-Health Chronic Disease Management | Sensors, 24, 24 (2024) |
| 28844 | 0.871 | Liu Y.-Y.; Zhang Y.; Wu Y.; Feng M. | Healthcare And Fitness Services: A Comprehensive Assessment Of Blockchain, Iot, And Edge Computing In Smart Cities | Journal of Grid Computing, 21, 4 (2023) |