Experimental Characterization of ESP-Mesh Performance for Resilient Medical IoT Monitoring Systems - Achyar, Zulfikar; Indrawati, Indrawati; Safar, Ilham; Wahyuni, Dewi (Sinkron : jurnal dan penelitian teknik informatika, 2026)
The reliability of medical Internet of Things (IoT) systems is critically dependent on network resilience, particularly in indoor hospital environments where conventional Wi-Fi infrastructures are vulnerable to single points of failure. Although ESP-Mesh has emerged as a promising self-healing communication protocol, its performance characteristics in medical IoT monitoring contexts remain insufficiently explored. This study aims to experimentally characterize the performance of ESP-Mesh networks for resilient medical IoT monitoring systems by analyzing multi-hop latency behavior, signal degradation, and communication stability under indoor medical-like conditions. A multi-parameter monitoring prototype integrating infusion volume, drip rate, and heart rate sensors was deployed as an experimental platform. Network performance was evaluated through controlled measurements of RSSI, end-to-end latency, and self-healing behavior, while MQTT was employed to assess cloud-based transmission efficiency. The results demonstrate that ESP-Mesh maintains stable self-healing communication with an average multi-hop latency of 0.714 s across distances up to 5 m, with latency increasing consistently as RSSI decreases. MQTT cloud transmission achieved a lower average latency of 0.247 s with zero packet loss, confirming its suitability for lightweight medical data delivery. Sensor evaluation revealed high accuracy for infusion volume monitoring (95.42%), while heart rate and drip rate measurements exhibited lower reliability due to signal interference and environmental sensitivity. These findings provide empirical insights into the performance limits and trade-offs of ESP-Mesh networks in medical IoT environments. The study confirms the feasibility of ESP-Mesh as a resilient communication backbone for medical monitoring, while highlighting the necessity of advanced signal processing to achieve clinical-grade sensing reliability.