Cooperative Dew Computing for Computational Offloading in Healthcare Monitoring
In modern healthcare monitoring, wearable sensors play a crucial role in collecting patient data, especially for individuals with disabilities. However, analyzing this data presents significant challenges, such as high energy consumption, latency, and task allocation inefficiencies. Healthcare monit...
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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/10753614/ |
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| Summary: | In modern healthcare monitoring, wearable sensors play a crucial role in collecting patient data, especially for individuals with disabilities. However, analyzing this data presents significant challenges, such as high energy consumption, latency, and task allocation inefficiencies. Healthcare monitoring systems face computational demands due to the processing of data from wearable sensors, which are constrained by limited resources and often affected by connectivity issues for data offloading. To address these challenges, this paper proposes a Cooperative Dew-Computing for Healthcare (CCH) framework, which integrates wearable sensors and dew devices within a cloud-fog-dew infrastructure. The CCH framework ensures efficient task distribution and computational offloading by selecting appropriate anchor devices using probabilistic and fuzzy logic techniques, taking into account energy efficiency and computational capabilities. Cooperative communication among anchor devices enables efficient task management, allowing tasks to be offloaded or distributed among multiple anchor devices. The proposed framework enhances healthcare monitoring systems by improving computational efficiency, optimizing resource utilization, and mitigating connectivity issues. This is particularly beneficial for disabled patients who rely on continuous monitoring and efficient data processing. Our evaluations demonstrate that the CCH framework outperforms existing techniques in terms of energy efficiency, latency, and task delay reduction, underscoring its effectiveness in providing timely and efficient data processing for healthcare monitoring systems. |
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| ISSN: | 2169-3536 |