In-network machine learning for real-time patient monitoring on IoMT edge gateways

Journal article

The Internet of Medical Things is transforming healthcare from reactive, hospital-based care to preventive and continuous remote monitoring. However, current solutions often depend on cloud or mobile platforms for data aggregation and analysis, introducing latency, privacy risks, and financial burden. In-network machine learning offers an opportunity to address these challenges by enabling real-time analytics directly within the network infrastructure. In this work, we present VISTA, an in-network computing framework for health analytics that enables real-time patient monitoring through in-network machine learning within edge gateways. VISTA integrates P4-based data plane modules that asynchronously aggregate heterogeneous sensor data and execute machine learning inference locally within the gateway, eliminating the reliance on cloud offloading. Implemented on a Dell Edge Gateway and evaluated on a 2000-patient dataset for early detection of sepsis and heart failure, VISTA achieves up to 95% detection accuracy, 2 milliseconds average latency, and 90% reduction in communication overhead, compared with cloud-based baselines. These results demonstrate the potential of in-network machine learning for scalable, low-latency, and privacy-preserving remote patient monitoring.

Authors: Akem, A; Lu, HY; Zilberman, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Internet of Things Journal
• Publication date: 2026-04-27
• Acceptance date: 2026-04-18
• DOI: 10.1109/jiot.2026.3688022
• EISSN: 2327-4662
• Language: English
• Keywords: internet of medical things; payloads; feeds; central processing unit; electronic circuits; application specific integrated circuits; AI accelerators; system-on-chip; microprocessor chips; internet of things; communication systems; in-network computing; machine learning; P4; remote patient monitoring; vital signs; wearable devices