Enabling pandemic-resilient healthcare: Narrowband Internet of Things and edge intelligence for real-time monitoring

被引:6
作者
Islam, Md Motaharul [1 ]
Hasan, Mohammad Kamrul [2 ]
Islam, Shayla [3 ]
Balfaqih, Mohammed [2 ]
Alzahrani, Ahmed Ibrahim [4 ]
Alalwan, Nasser [4 ]
Safie, Nurhizam [2 ]
Bhuiyan, Zaheed Ahmed [1 ]
Thakkar, Rahul [5 ]
Ghazal, Taher M. [1 ,6 ,7 ]
机构
[1] United Int Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh
[2] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi, Malaysia
[3] UCSI Univ, Inst Comp Sci & Digital Innovat, Kuala Lumpur, Malaysia
[4] King Saud Univ, Community Coll, Comp Sci Dept, Riyadh, Saudi Arabia
[5] Victorian Inst Technol, Melbourne, Vic, Australia
[6] Khalifa Univ, Ctr Cyber Phys Syst, Comp Sci Dept, Abu Dhabi, U Arab Emirates
[7] Appl Sci Private Univ, Appl Sci Res Ctr, Amman, Jordan
关键词
artificial intelligence techniques; edge detection; human-computer interfacing; intelligent information processing; TECHNOLOGIES;
D O I
10.1049/cit2.12314
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Internet of Things (IoT) in deploying robotic sprayers for pandemic-associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things (NB-IoT) technology. The core objective is establishing a seamless data transmission pipeline that bridges these devices with the nearest base station. The associated data is routed directly from the edge computing infrastructure to the Cloud storage. The proposed architecture increases data security and optimises system performance by avoiding dependencies on central data centres. The critical components of this system allow the edge node to transmit data to the cloud, where primary data processing and analysis occur. The selection of NB-IoT as the underlying protocol facilitates real-time data collection and monitoring in healthcare with boosted capacity and coverage, extended battery life, and cost efficiency.
引用
收藏
页数:18
相关论文
共 43 条
[1]   Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems [J].
Akter, Mahmuda ;
Moustafa, Nour ;
Lynar, Timothy ;
Razzak, Imran .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2022, 26 (12) :5805-5816
[2]  
Angelov GV, 2019, LECT NOTES COMPUT SC, V11369, P226, DOI 10.1007/978-3-030-10752-9_10
[3]  
[Anonymous], 2014, Bangladesh
[4]  
[Anonymous], About us
[5]  
Awotunde J.B., 2021, Intelligent Interactive Multimedia Systems for EHealthcare Applications, P191
[6]  
B. B. O. STATISTICS, 2013, District Statistics 2011
[7]  
Baker J., 2007, Bangladesh Development Series, V17
[8]   Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities [J].
Baker, Stephanie B. ;
Xiang, Wei ;
Atkinson, Ian .
IEEE ACCESS, 2017, 5 :26521-26544
[9]  
Casares-Giner Vicente, 2018, Information Technology - New Generations. 15th International Conference on Information Technology. Advances in Intelligent Systems and Computing (AISC 738), P149, DOI 10.1007/978-3-319-77028-4_23
[10]   A Context-Aware, Interactive M-Health System for Diabetics [J].
Chang, Shih-Hao ;
Chiang, Rui-Dong ;
Wu, Shih-Jung ;
Chang, Wei-Ting .
IT PROFESSIONAL, 2016, 18 (03) :14-22