{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:16:35Z","timestamp":1773015395870,"version":"3.50.1"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T00:00:00Z","timestamp":1746921600000},"content-version":"vor","delay-in-days":130,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>With the continuous enhancement of heart disease monitoring systems, awareness of cardiac diseases has increasingly captured public attention, leading to an exponential rise in demand for intelligent health status monitoring systems. Currently, these cardiac status monitoring systems largely depend on heavy equipment, which is costly and lacks flexibility and scalability. Recently, wireless sensor network\u2013enabled Internet of things (WSN\u2010IoT) has played a crucial role in integrating intelligent device capabilities into diagnosis and clinical treatment by replacing traditional equipment. Additionally, edge computing is a novel paradigm that facilitates cognitive and analytical applications in WSN\u2010IoT devices, enhancing monitoring and prognosis. However, these intelligent devices still suffer from limited lifetime and resources, making it essential to optimize resource usage without compromising monitoring performance. By applying an intelligent approach, resource optimization can be achieved, resulting in superior classification performance. This paper proposes a novel resource\u2010constrained attention map using gated recurrent neural networks (GRNNs) for deployment on edge\u2010supported WSN devices, which classifies electrocardiogram (ECG) signals without compromising optimal resource usage. In this model, attention layers embedded in the GRNN are deployed on edge devices to enhance intelligence for effective ECG signal classification. Extensive experimentation was conducted with 50 patients using real\u2010time evaluation boards with embedded microcontrollers connected to ECG devices and NVIDIA Jetson Nano boards as edge computing devices. To highlight the strengths of the proposed framework, efficiency evaluations were made with other deep learning (DL) approaches for edge devices. Studies indicate that the proposed model\u2014relied on edge\u2010enabled WSN devices\u2014achieved better computational cost and classification outcomes (99% accuracy) compared to state\u2010of\u2010the\u2010art learning methods and was 23% less costly than other learning methods.<\/jats:p>","DOI":"10.1155\/dsn\/5651009","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:09:16Z","timestamp":1747008556000},"source":"Crossref","is-referenced-by-count":1,"title":["SAGE\u2010NET\u2014Reconceiving Edge Intelligence Using Attention\u2010Based Deep Learning Framework for Effective Classification of Electrocardiogram (ECG) in WSN\u2010IoT Environment"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4374-448X","authenticated-orcid":false,"given":"P. Vinoth","family":"Kumar","sequence":"first","affiliation":[]},{"given":"C. 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