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Smart city article details
| Title | Energy-Efficient And Delay Tolerant Prediction Of Heart Disease In Iot-Enabled Mist Computing Platforms |
|---|---|
| ID_Doc | 23451 |
| Authors | Tripathy S.S.; Tripathy B.; Bebortta S.; Modibbo U.M. |
| Year | 2024 |
| Published | Healthcare-Driven Intelligent Computing Paradigms to Secure Futuristic Smart Cities |
| DOI | http://dx.doi.org/10.1201/9781032631738-20 |
| Abstract | In recent times, the Internet of Things (IoT) along with the cloud computing continuum have demonstrated considerable potential in a variety of smart city applications, including healthcare. In this study, we have investigated the potential of integrating deep reinforcement learning (DRL) algorithms to mist computing in an IoT environment to predict cardiac disease. Heart disease is still a major concern for global health, thus early and precise prediction is necessary to allow for prompt therapies and better patient outcomes. However, handling the massive amounts of real-time data generated by IoT devices in healthcare settings presents difficulties for conventional prediction algorithms. The mist computing framework proposed herein overcomes latency and bandwidth challenges by enabling processing closer to the data source, providing a distributed approach. The proposed system in this case makes use of DRL algorithms which are quite popular when it comes to interpretation of complex traffic patterns and learning from the sequential data. To this end, we strive to improve the scalability and accuracy of heart disease prediction using DRL as well as mist computing in an IoT-based smart city environment. Exhaustive experiments and assessments are used to evaluate the performance of our model in comparison with traditional cloud-based approaches. Initial results demonstrate promising outcomes in energy consumption and latency values as well as the decrease of processing load. This framework is centered on the implementation of early diagnosis and intervention for heart disease since it will provide breakthroughs in smart city technologies that will bring about improved patient care and health outcomes. © 2025 selection and editorial matter, Diptendu Sinha Roy, Mir Wajahat Hussain, K. Hemant Kumar Reddy, Deepak Gupta; individual chapters, the contributors. |
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