A Comprehensive Framework for Remote Patient Monitoring Through Hybrid IoT-Fog-Cloud Architectures Optimizing Signal Processing, Resource Management, and QoS Metrics
Keywords:
IoT, cloud computing, fog computing, signal processing, quality of service (QoS), resource management,, data accuracy, energy efficiency, system reliability, remote patient monitoring (RPM).Abstract
This study optimizes signal processing, resource management, and Quality of Service (QoS)
metrics to improve remote patient monitoring (RPM) through the development and application of
a hybrid IoT-Fog-Cloud architecture. IoT devices for smooth data collecting, fog computing for
real-time processing, and cloud computing for massive storage and sophisticated analytics are all
integrated into the suggested framework. The methodology strives for optimal energy efficiency,
excellent data accuracy, and low latency. Through the use of sophisticated signal processing
methods like adaptive filtering and wavelet transformations, the system efficiently lowers noise
and enhances data clarity. Resource management techniques that guarantee effective use of
computational resources include dynamic job scheduling and workload allocation. Additionally,
QoS optimization maximizes bandwidth consumption and reduces latency, which improves
system responsiveness. In comparison to current models, the results show notable gains in system
reliability (99.8%), data accuracy (99.9%), and energy efficiency (0.012J). This system offers a
scalable, effective, and trustworthy method for monitoring patient health in real-time, which helps
to enhance healthcare results.
References
Abdelmoneem, R. M., Benslimane, A., & Shaaban, E. (2020). Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures. Computer networks, 179, 107348.
Abdel-Basset, M., Ding, W., & Abdel-Fatah, L. (2020). The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model. Information Fusion, 61, 84-100.
Kumar, M. S., & Dhulipala, V. S. (2020). Fuzzy allocation model for health care data management on IoT assisted wearable sensor platform. Measurement, 166, 108249.
Patan, R., Ghantasala, G. P., Sekaran, R., Gupta, D., & Ramachandran, M. (2020). Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system. Sustainable Cities and Society, 59, 102141.
Basir, R., Qaisar, S., Ali, M., Aldwairi, M., Ashraf, M. I., Mahmood, A., & Gidlund, M. (2019). Fog computing enabling industrial internet of things: State-of-the-art and research challenges. Sensors, 19(21), 4807.
Shosha, W. M., Mostafa, R. R., & Elfetoh, A. A. (2019). Empowering healthcare IoT systems with hierarchical fog-based computing architecture. Int. J. Sci. Eng. Res, 10(6), 471-482.
Devarajan, M. V. (2020). ASSESSING LONG-TERM SERUM SAMPLE VIABILITY FOR CARDIOVASCULAR RISK PREDICTION IN RHEUMATOID ARTHRITIS. International Journal of Information Technology and Computer Engineering, 8(2), 60-74.
Dondapati, K. (2020). Robust Software Testing for Distributed Systems Using Cloud Infrastructure, Automated Fault Injection, and XML Scenarios. International Journal of Information Technology and Computer Engineering, 8(2), 75-94.
Allur, N. S. (2019). Genetic Algorithms for Superior Program Path Coverage in software testing related to Big Data. International Journal of Information Technology and Computer Engineering, 7(4), 99-112.
Rajeswaran, A. (2020). Big data analytics and demand-information sharing in ECommerce supply chains: mitigating manufacturer encroachment and channel conflict. International Journal of Applied Science Engineering and Management, 14(2), ISSN2454-9940.
Poovendran, A. (2019). Analyzing the Covariance Matrix Approach for DDOS HTTP Attack Detection in Cloud Environments. International Journal of Information Technology & Computer Engineering, 7(1), ISSN-2347.
Poovendran, A. (2020). Implementing AES Encryption Algorithm to Enhance Data Security in Cloud Computing. International Journal of Information technology & computer engineering, 8(2), 2347-3657.
Sreekar, P. (2020). Cost-effective cloud-based big data mining with K-means clustering: An analysis of Gaussian data. International Journal of Engineering & Science Research, 10(1), 229-249.
Karthikeyan, P. (2020). Real-time data warehousing: performance insights of semi-stream joins using Mongodb. Int J Manag Res & Rev, 10(4), 38-49.
Sitaraman, S. R. (2020). Optimizing Healthcare Data Streams Using Real-Time Big Data Analytics and AI Techniques. International Journal of Engineering Research and Science & Technology, 16(3), 9-22.
Panga, N. K. R. (2020). Leveraging heuristic sampling and ensemble learning for enhanced insurance big data classification. International Journal of Financial Management, 9(1). ISSN (P): 2319-491X; ISSN (E): 2319-4928.
Gudivaka, R. L. (2020). Robotic Process Automation meets Cloud Computing: A Framework for Automated Scheduling in Social Robots. International Journal of Business and General Management (IJBGM), 8(4), 49-62.
Gudivaka, R. K. (2020). Robotic process automation optimization in cloud computing via two-tier MAC and Lyapunov techniques. International Journal of Business and General Management (IJBGM), 9(5), 75-92.
Gudivaka, B. R. (2019). BIG DATA-DRIVEN SILICON CONTENT PREDICTION IN HOT METAL USING HADOOP IN BLAST FURNACE SMELTING. International Journal of Information Technology and Computer Engineering, 7(2), 32-49.
Allur, N. S. (2020). Enhanced performance management in mobile networks: A big data framework incorporating DBSCAN speed anomaly detection and CCR efficiency assessment. Journal of Current Science, 8(4).
Deevi, D. P. (2020). Real-time malware detection via adaptive gradient support vector regression combined with LSTM and hidden Markov models. Journal of Science and Technology, 5(4).
Kodadi, S. (2020). Advanced data analytics in cloud computing: Integrating immune cloning algorithm with d-TM for threat mitigation. International Journal of Engineering Research and Science & Technology, 16(2), 30-42.
Dondapati, K. (2020). Integrating neural networks and heuristic methods in test case prioritization: A machine learning perspective. International Journal of Engineering & Science Research, 10(3).
Dondapati, K. (2020). Leveraging backpropagation neural networks and generative adversarial networks to enhance channel state information synthesis in millimetre wave networks. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(3)
Gattupalli, K. (2020). Optimizing 3D printing materials for medical applications using AI, computational tools, and directed energy deposition. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(3).
Allur, N. S. (2020). Phishing website detection based on multidimensional features driven by deep learning: Integrating stacked autoencoder and SVM. Journal of Science and Technology, 5(6).
Naga, S. A. (2020). Big data-driven agricultural supply chain management: trustworthy scheduling optimization with DSS and MILP techniques. J Curr Sci Humanities, 8, 1-16.
Peddi, S., Narla, S., & Valivarthi, D. T. (2018). Advancing geriatric care: Machine learning algorithms and AI applications for predicting dysphagia, delirium, and fall risks in elderly patients. International Journal of Information Technology and Computer Engineering, 6(4), 62-76.
Peddi, S., Narla, S., & Valivarthi, D. T. (2019). Harnessing Artificial Intelligence and Machine Learning Algorithms for Chronic Disease Management, Fall Prevention, and Predictive Healthcare Applications in Geriatric Care. International Journal of Engineering Research and Science & Technology, 15(1), 1-15.
Narla, S., Valivarthi, D. T., & Peddi, S. (2019). Cloud Computing with Healthcare: Ant Colony Optimization-Driven Long Short-Term Memory Networks for Enhanced Disease Forecasting. International Journal of HRM and Organizational Behavior, 7(3), 12-26.
Kethu, S. S. (2020). AI and IoT-driven CRM with cloud computing: Intelligent frameworks and empirical models for banking industry applications. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(1).
Vasamsetty, C. (2020). Clinical decision support systems and advanced data mining techniques for cardiovascular care: Unveiling patterns and trends. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(2).
Kadiyala, B. (2020). Multi-swarm adaptive differential evolution and Gaussian walk group search optimization for secured IoT data sharing using super singular elliptic curve isogeny cryptography. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(3).
Natarajan, D. R. (2019). OPTIMIZED CLOUD MANUFACTURING FRAMEWORKS FOR ROBOTICS AND AUTOMATION WITH ADVANCED TASK SCHEDULING TECHNIQUES. International Journal of Information Technology and Computer Engineering, 7(4), 113-127.
Basani, D. K. R. (2020). Hybrid Transformer-RNN and GNN-based robotic cloud command verification and attack detection: Utilizing soft computing, rough set theory, and grey system theory. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(1).
Jadon, R. (2020). Improving AI-driven software solutions with memory-augmented neural networks, hierarchical multi-agent learning, and concept bottleneck models. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(2)
Boyapati, S. (2020). Assessing digital finance as a cloud path for income equality: Evidence from urban and rural economies. International Journal of Modern Electronics and Communication Engineering (IJMECE), 8(3).
Gaius Yallamelli, A. R., Mamidala, V., & Yalla, R. K. M. (2020). A cloud-based financial data modeling system using GBDT, ALBERT, and Firefly Algorithm optimization for high-dimensional generative topographic mapping. Int J Mod Electron Communication Eng (IJMECE), 8(4).
Yalla, R. K. M., Yallamelli, A. R. G., & Mamidala, V. (2020). Comprehensive approach for mobile data security in cloud computing using RSA algorithm. J Curr Sci Humanit, 8(3), 13-33.
Dondapati, K. (2019). Lung cancer prediction using deep learning. International Journal of HRM and Organizational Behavior, 7(1).
Kethu, S. S. (2019). AI-Enabled Customer Relationship Management: Developing Intelligence Frameworks, AI-FCS Integration, and Empirical Testing for Service Quality Improvement. International Journal of HRM and Organizational Behavior, 7(2), 1-16.
Kadiyala, B. (2019). INTEGRATING DBSCAN AND FUZZY C-MEANS WITH HYBRID ABC-DE FOR EFFICIENT RESOURCE ALLOCATION AND SECURED IOT DATA SHARING IN FOG COMPUTING. International Journal of HRM and Organizational Behavior, 7(4), 1-13.
Nippatla, R. P. (2019). AI and ML-Driven Blockchain-Based Secure Employee Data Management: Applications of Distributed Control and Tensor Decomposition in HRM. International Journal of Engineering Research and Science & Technology, 15(2), 1-16.
Devarajan, M. V. (2019). A Comprehensive AI-Based Detection and Differentiation Model for Neurological Disorders Using PSP Net and Fuzzy Logic-Enhanced Hilbert-Huang Transform. International Journal of Information Technology and Computer Engineering, 7(3), 94-104.
Jadon, R. (2019). Integrating Particle Swarm Optimization and Quadratic Discriminant Analysis in AI-Driven Software Development for Robust Model Optimization. International Journal of Engineering Research and Science & Technology, 15(3), 25-35.
Jadon, R. (2019). Enhancing AI-Driven Software with NOMA, UVFA, and Dynamic Graph Neural Networks for Scalable Decision-Making. International Journal of Information Technology and Computer Engineering, 7(1), 64-74.
Boyapati, S. (2019). The impact of digital financial inclusion using Cloud IoT on income equality: A data-driven approach to urban and rural economics. Journal of Current Science, 7(4).
Yalla, R. K. M., Yallamelli, A. R. G., & Mamidala, V. (2019). Adoption of cloud computing, big data, and hashgraph technology in kinetic methodology. Int J Curr Sci, 7(3), 9726-001X.
Samudrala, V. K. (2020). AI-powered anomaly detection for cross-cloud secure data sharing in multi-cloud healthcare networks. Journal of Current Science & Humanities, 8(2)
Ayyadurai, R. (2020). Smart surveillance methodology: Utilizing machine learning and AI with blockchain for bitcoin transactions. World Journal of Advanced Engineering Technology and Sciences, 1(1), 110-120. https://doi.org/10.30574/wjaets.2020.1.1.0023
Vasamsetty, C., Kadiyala, B., & Arulkumaran, G. (2019). Decision Tree Algorithms for Agile E-Commerce Analytics: Enhancing Customer Experience with Edge-Based Stream Processing. International Journal of HRM and Organizational Behavior, 7(4), 14-30.
Sareddy, M. R., & Hemnath, R. (2019). Optimized Federated Learning for Cybersecurity: Integrating Split Learning, Graph Neural Networks, and Hashgraph Technology. International Journal of HRM and Organizational Behavior, 7(3), 43-54.
Parthasarathy, K., & Ayyadurai, R. (2019). IoT-Driven Visualization Framework for Enhancing Business Intelligence, Data Quality, and Risk Management in Corporate Financial Analytics. International Journal of HRM and Organizational Behavior, 7(3), 27- 42.
Bobba, J., & Bolla, R. L. (2019). Next-Gen HRM: AI, Blockchain, Self-Sovereign Identity, and Neuro-Symbolic AI for Transparent, Decentralized, and Ethical Talent Management in the Digital Era. International Journal of HRM and Organizational Behavior, 7(4), 31-51.
Chauhan, G. S., & Jadon, R. (2020). AI and ML-powered CAPTCHA and advanced graphical passwords: Integrating the DROP methodology, AES encryption, and neural network-based authentication for enhanced security. World Journal of Advanced Engineering Technology and Sciences, 1(1), 121–132. https://doi.org/10.30574/wjaets.2020.1.1.0027
Narla, S. (2020). Transforming smart environments with multi-tier cloud sensing, big data, and 5G technology. International Journal of Computer Science Engineering Techniques, 5(1), 1-10. http://www.ijcsejournal.org
Pulakhandam, W., & Samudrala, V. K. (2020). Automated threat intelligence integration to strengthen SHACS for robust security in cloud-based healthcare applications. International Journal of Engineering & Science Research, 10(4), 71-84. ISSN 2277-2685.
Natarajan, D. R., Narla, S., & Kethu, S. S. (2019). An intelligent decision-making framework for cloud adoption in healthcare: Combining DOI theory, machine learning, and multi-criteria approaches. International Journal of Engineering Research & Science & Technology, 15(3). https://www.ijerst.com
