Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing
The rapid expansion of Internet of Things (IoT) devices in healthcare has increased data volumes, creating challenges for the efficiency and latency of real-time monitoring systems. Traditional cloud computing often encounters high latency and network congestion, making it unsuitable for time-sensit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10794657/ |
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| Summary: | The rapid expansion of Internet of Things (IoT) devices in healthcare has increased data volumes, creating challenges for the efficiency and latency of real-time monitoring systems. Traditional cloud computing often encounters high latency and network congestion, making it unsuitable for time-sensitive healthcare applications. Although fog computing introduces intermediary nodes to mitigate these issues, existing approaches frequently lack efficient workload distribution, leading to performance bottlenecks. To address these limitations, an Optimised Load Balancing (OLB) algorithm is proposed, to allocate workloads effectively across fog nodes and to reduce communication and computational delays. The system follows a three-tier architecture: 1) Sensor data collection, where patient-worn sensors transmit vital signs; 2) a Fog layer for real-time analysis near base stations; and 3) Cloud storage and user access via mobile devices. Simulations conducted using the iFogSim toolkit demonstrate that OLB achieves a 28% reduction in latency, a 15% improvement in network usage, a 20% reduction in execution time, a 25% decrease in energy consumption, and a 22% reduction in execution cost compared to existing methods, including the Load Balancing Scheme (LBS), Fog Node Placement Algorithm (FNPA), Load-Aware Balancing (LAB) scheme, and Mobile Edge Computing (MEC). By dynamically adjusting workload distribution based on real-time traffic and computational capacity, the proposed fog-based solution provides a responsive, energy-efficient, and cost-effective approach to healthcare data management, surpassing MEC and other state-of-the-art algorithms in adaptability and resource efficiency. |
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| ISSN: | 2169-3536 |