ArticlePhysical and engineering sciences in medicine2026
A novel IoT-aware wearable sensor device for healthcare data management framework using hybrid optimization algorithm.
Article in Physical and engineering sciences in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Internet of Things (IoT) has provided a lot of support for patient care by connecting technologies and clinicians through a high-speed network. Data management in IoT healthcare requires a lot of support, since most of the networks are designed for continuous monitoring through the sensor devices, and this leads to challenges in managing sensor data. As the resources in IoT are limited, it is necessary to explore data management strategies so as to achieve an energy-efficient IoT healthcare setup. The classical transmission networks implemented for human-aided applications have various problems, such as limited battery life and resources. Therefore, an effective healthcare data management system using IoT-aided wearable sensor devices is implemented in this work to address the challenges encountered in traditional models. To facilitate robust communication for an IoT-assisted healthcare device, a cloud layer is established, which is used for health data collection and transmission and health monitoring. Here, optimal resource allocation is done using the proposed Hybrid Tasmanian Devil Archery Algorithm (HTDAA), and it is developed from the existing concepts of Tasmanian Devil Optimization (TDO) and Archery Algorithm (AA) to achieve high multi-objective constraint balance and convergence power. This optimal resource allocation process helps in reducing energy consumption in the usual data transmission. For an effective resource allocation process, an objective function is formulated based on the normalized energy ratio value, the buffer memory ratio, and the packet arrival data rate. The proposed HTDAA is established in the IoT setup and used for allocating the optimal channel resources to be involved in the health data transmission, and minimizes the energy requirement. The performance of the suggested HTDAA is estimated by comparing its numerical outcomes with recent optimization techniques. According to the analysis, the packet arrival ratio of HTDAA is 5.6%, 31.7%, 3.3%, and 18.1% enhanced than the recent ESO, SLO, TDO, and AA techniques. Simulations under different cases confirm that the designed HTDAA-based healthcare data management system achieves more robust and energy-efficient solutions than the traditional models.
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