Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems DOI Creative Commons
Ahmed K. Jameil, H. S. Al‐Raweshidy

IET Wireless Sensor Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Abstract The integration of digital twins (DTs) in healthcare is critical but remains limited real‐time patient monitoring due to challenges achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises monitoring, providing timely solution for needs. incorporates Pyomo‐based dynamic optimisation model, which reduces latency 32% improves response time 52%, surpassing existing systems. Leveraging low‐cost, multimodal sensors, the system continuously monitors physiological parameters, including SpO2, heart rate, body temperature, enabling proactive health interventions. A definition language (Digital Twin Definition Language)‐based series analysis twin graph platform further enhance sensor connectivity scalability. Additionally, machine learning (ML) strengthens predictive accuracy, 98% accuracy 99.58% under cross‐validation (cv = 20) using XGBoost algorithm. Empirical results demonstrate substantial improvements processing time, stability, capacity, with predictions completed 17 ms. represents significant advancement offering responsive scalable constraints applications. Future research could explore incorporating additional sensors advanced ML models expand its impact

Language: Английский

Optimizing Task Offloading with Metaheuristic Algorithms Across Cloud, Fog, and Edge Computing Networks: A Comprehensive Survey and State-of-the-Art Schemes DOI
Amir M. Rahmani, Amir Haider,

Parisa Khoshvaght

et al.

Sustainable Computing Informatics and Systems, Journal Year: 2025, Volume and Issue: unknown, P. 101080 - 101080

Published: Jan. 1, 2025

Language: Английский

Citations

3
An Optimizing Geo-Distributed Edge Layering with Double Deep Q-Networks for Predictive Mobility-Aware Offloading in Mobile Edge Computing DOI
Amir M. Rahmani, Amir Haider, Saqib Ali

et al.

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: unknown, P. 103804 - 103804

Published: Feb. 1, 2025

Language: Английский

Citations

0
Self-Learning Adaptive Power Management Scheme for Energy-Efficient IoT-MEC Systems Using Soft Actor-Critic Algorithm DOI
Amir M. Rahmani, Amir Haider, Komeil Moghaddasi

et al.

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101587 - 101587

Published: March 1, 2025

Language: Английский

Citations

0
Deep Reinforcement Learning-Enabled Computation Offloading: A Novel Framework to Energy Optimization and Security-Aware in Vehicular Edge-Cloud Computing Networks DOI Creative Commons
Waleed Almuseelem

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2039 - 2039

Published: March 25, 2025

The Vehicular Edge-Cloud Computing (VECC) paradigm has gained traction as a promising solution to mitigate the computational constraints through offloading resource-intensive tasks distributed edge and cloud networks. However, conventional computation mechanisms frequently induce network congestion service delays, stemming from uneven workload distribution across spatial Roadside Units (RSUs). Moreover, ensuring data security optimizing energy usage within this framework remain significant challenges. To end, study introduces deep reinforcement learning-enabled for multi-tier VECC First, dynamic load-balancing algorithm is developed optimize balance among RSUs, incorporating real-time analysis of heterogeneous parameters, including RSU load, channel capacity, proximity-based latency. Additionally, alleviate in static deployments, proposes deploying UAVs high-density zones, dynamically augmenting both storage processing resources. an Advanced Encryption Standard (AES)-based mechanism, secured with one-time encryption key generation, implemented fortify confidentiality during transmissions. Further, context-aware caching strategy preemptively store processed tasks, reducing redundant computations associated overheads. Subsequently, mixed-integer optimization model formulated that simultaneously minimizes consumption guarantees latency constraint. Given combinatorial complexity large-scale vehicular networks, equivalent learning form given. Then learning-based designed learn close-optimal solutions under conditions. Empirical evaluations demonstrate proposed significantly outperforms existing benchmark techniques terms savings. These results underscore framework's efficacy advancing sustainable, secure, scalable intelligent transportation systems.

Language: Английский

Citations

0
A deep-reinforcement-learning-based strategy selection approach for fault-tolerant offloading of delay-sensitive tasks in vehicular edge-cloud computing DOI
Vahide Babaiyan, Omid Bushehrian

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(5)

Published: April 8, 2025

Language: Английский

Citations

0
Real-time task dispatching and scheduling in serverless edge computing DOI
Ming Li, Furong Xu, Yuqin Wu

et al.

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: unknown, P. 103854 - 103854

Published: April 1, 2025

Language: Английский

Citations

0
Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems DOI Creative Commons
Ahmed K. Jameil, H. S. Al‐Raweshidy

IET Wireless Sensor Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Abstract The integration of digital twins (DTs) in healthcare is critical but remains limited real‐time patient monitoring due to challenges achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises monitoring, providing timely solution for needs. incorporates Pyomo‐based dynamic optimisation model, which reduces latency 32% improves response time 52%, surpassing existing systems. Leveraging low‐cost, multimodal sensors, the system continuously monitors physiological parameters, including SpO2, heart rate, body temperature, enabling proactive health interventions. A definition language (Digital Twin Definition Language)‐based series analysis twin graph platform further enhance sensor connectivity scalability. Additionally, machine learning (ML) strengthens predictive accuracy, 98% accuracy 99.58% under cross‐validation (cv = 20) using XGBoost algorithm. Empirical results demonstrate substantial improvements processing time, stability, capacity, with predictions completed 17 ms. represents significant advancement offering responsive scalable constraints applications. Future research could explore incorporating additional sensors advanced ML models expand its impact

Language: Английский

Citations

3