The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Leveraging Context-Aware Fogging for Advanced Remote Patient Monitoring and Healthcare in Modern Life
Abstract
This paper examines the integration of fog computing in current RPM systems with a focus on latency and real-time response capabilities. Unlike typical cloud models, fog computing layers computation closer to the edge devices therefore enhancing decentralization. This work provides an overview of how fog layers are incorporated in RPM for data pre-processing, context computations, and security. These are features such as the ability to generate rules dynamically, facilitating high accuracy of the health status determined by physiological, behavioral as well as environmental parameters. With the help of the implemented context-aware access control (CAAC) mechanism, which is backed up by the utilization of public key infrastructure, data security is reached. The design strategy discussed in this paper and implemented using NS-2 shows enhanced performance not only in relationships of packet delivery ratio, throughput, and end-to-end delay but also in other important measures. The comparative analysis shows that the integration of fog computing improves system performance, in media transmission, and responses are computed faster than those of cloud-only solutions. These discoveries strengthen the impact of fog computing to overcome latency, scalability, and security issues in IoT-based healthcare systems making it an enabler of real-life, life-critical applications.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Professor, G., & Roy, V. (2023) · Journal of Cybersecurity and Information Management · doi:10.54216/jcim.130103 · Cited by 187
Self-citationAn Advance Implementation of Machine Learning Techniques for the Prediction of Cervical Cancer
Roy, V., Roy, L., Ahluwalia, R., et al. (2023) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig59752.2023.10456347 · Cited by 181
Self-citationA Wavelet Features and Machine Learning Founded Error Analysis of Sound and Trembling Signal
Shukla, P. K., Roy, V., Chandanan, A. K., et al. (2023) · SN Computer Science · doi:10.1007/s42979-023-02189-y · Cited by 156
Self-citationArtificial Intelligence Systems in Aviation
Kashyap, R. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-7588-7.ch001 · Cited by 86
A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data
Roy, V., & Shukla, S. (2017) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2017100105 · Cited by 65
Self-citationManickam, A., Ezhilmaran, D., Manogaran, G., Chilamkurti, N., Vijayan, V., Saraff, S., Samuel, R. D. J., & Krishnamoorthy, R. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1152-1 · Cited by 45
Kavitha, P., Ayyappan, G., Jayagopal, P., Mathivanan, S. K., Mallik, S., Al‐Rasheed, A., Alqahtani, M. S., & Soufiene, B. O. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05584-7 · Cited by 42
Efficient Multi-Port Power Converter Design Using a Single Inductor for Electric Vehicle Systems
Thati, R., Rajagiri, A. K., Veerla, V. T., et al. (2024) · IEEE International Conference on Sustainable Energy and Future Electric Transportation (SEFET) · doi:10.1109/sefet61574.2024.10717991 · Cited by 33
Kidney Impairment Prediction Due to Diabetes Using Extended Ensemble Learning Machine Algorithm
Devasenapathy, D., Vidhya, K., Alphy, A., et al. (2023) · Journal of Machine and Computing · doi:10.53759/7669/jmc202303027 · Cited by 32
Electronic band-gap integrated low mutual coupling dual-band MIMO antenna
Maturi, T., & Harikrishna, B. (2020) · International Journal of Electronics · doi:10.1080/00207217.2020.1726483 · Cited by 31
Research works printed but not linked: 3
- Agarwal, 2019, Design of self-management aware autonomic resource scheduling scheme in cloud, International Journal of Computer Information Systems and Industrial Management Applications, 11, 8
- Bhupathi, 2023, Deep Learning and EV Charging: Battery Life and Performance, ESP International Journal of Advancements in Science & Technology (ESP-IJAST), 1, 29
- Dixit, 2019, Creation and Data Analysis of Women Safety Index of Delhi And It’s Neighbouring Cities, SCOPUS listed, International Journal of Recent Technology and Engineering (IJRTE), 7, 432
Cited by
Sorted by citations
Optimizing Edge Computing Performance using Machine Learning for Low-Latency Applications
Karthiga, M., Paramasivam, J., Raja, P. V., Suman, S. K., Bhagyalakshmi, L., & Poddar, H. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11198929 · Cited by 37
“Such an updating process will allow edge nodes to operate using better models, enhancing the sensitivity and accuracy of their decision making [19].”
Self-citationAI-Powered Adaptive Systems for Fault Detection in Distributed Networks
Bhagyalakshmi, L., Lingeswaran, G., Madhavi, P., et al. (2025) · doi:10.1109/worldsuas66815.2025.11199095 · Cited by 31
Predictive Modeling in Economics: The Role of AI and Deep Learning
Jayanthi, L., Kasireddy, L. C., Velmurugan, P. R., Sushma, G., Bhimaavarapu, K., & Reddy, P. V. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11199198 · Cited by 31
“The right model selection fits the characteristics of the chosen algorithm to the data and the objectives of the model in order to produce accurate predictions for economic modelling [23-24].”
Enhancing Edge Computing with Machine Learning for Low-Latency Applications
Dhumpati, R., Kasireddy, L. C., Gunavardini, V., Suman, S. K., Bhagyalakshmi, L., & Talwar, S. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11280761 · Cited by 28
“Specialized hardware, like AI accelerators, is often required to support complex models, which can be costly to implement and maintain across numerous edge devices [20-21].”
Intelligent Mobility Model for Sustainable Urban Transport using Remote Sensing and AI
Natrayan, L., Kaliappan, S., Arputharaj, B. S., et al. (2025) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc65114.2025.11212477 · Cited by 4
“LSTM is particularly effective at capturing long - range temporal dependencies, enabling it to forecast traffic patterns several time steps into the future based on historical trends [19][20].”
Metadata
- Authors
- Poddar, H., Roy, V., Bhoyar, M., Gangrade, A., Venkata Kowshik, K. N., & Rathod, P.
- Affiliations
- Easwari Engineering College (IN)
- Gyan Ganga Institue of Technology and Sciences,DoEC,Jabalpur,MP
- Jagadambha College of Engineering and Technology,Yavatmal,Maharashtra,India
- IES College of Technology,Department of Computer Science & Engineering,Bhopal,Madhya Pradesh,India,462044
- CMR Engineering College,Dept. of Computer Science and Engineering,Hyderabad,Telangana,India
- RK University (IN)
- Venue
- 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0
- Publisher
- IEEE
- Published
- 2025-04-09
- Pages
- 1-6
- OpenAlex
- W4412404297
- Semantic Scholar
- c57f3d783729deca467b98fa932fe1d2e0abfe92
- Cited by
- 15
- References
- 23
- Citations by year
- 2026: 7 · 2025: 8
- FWCI
- 11.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- fog computing, remote patient monitoring, context-aware access control, public key infrastructure, Internet of Medical Things, edge computing, latency reduction, data security, packet delivery ratio, end-to-end delay
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Leveraging Context-Aware Fogging for Advanced Remote Patient Monitoring and Healthcare in Modern Life. https://app.cochat.ai/papers/10.1109/otcon65728.2025.11070467
- The paper
- Poddar, H., Roy, V., Bhoyar, M., Gangrade, A., Venkata Kowshik, K. N., & Rathod, P. (2025). Leveraging Context-Aware Fogging for Advanced Remote Patient Monitoring and Healthcare in Modern Life. 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. https://doi.org/10.1109/otcon65728.2025.11070467