DMACN: A Dynamic Multi-Attribute Caching Mechanism for NDN-Based Remote Health Monitoring System

Pushpendu Kar, Kewei Chen, Jiayi Shi

Abstract: Named Data Network (NDN) advocates the philosophy of accessing IoT data owing to its location independence feature. This enables routers to pre-cache content and serves the future requests for the same content on a local basis. Such architecture demonstrates huge application potential in the E-health field. In order to achieve efficient healthcare treatment and administration for both patients and medical professions, the optimization of storing patients’ real-time, large-scale physical data is of necessity. We propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>D</b></u> ynamic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>M</b></u> ulti- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></u> ttribute <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>C</b></u> aching mechanism for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>N</b></u> DN-Based remote health monitoring system (DMACN). In our model, we adopted a predictable consumer-driven freshness mechanism with low computation cost to satisfy the freshness-sensitive nature of health data. A novel content popularity model based on Analytic Hierarchy Process (AHP) and medical-grade parameters are proposed to handle the doctor-decision-making-coupled Interest sending mechanism of a remote health monitoring system. The final simulation results show DMACN has strong robustness against intensive requesting and complex contents. It is also shown that its performance surpasses existing mechanisms. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cache Hit Ratio</i> exceeds 37.5% to FIFO and LRU, 220% compared with CPFC, and 55% for CTDICR for both consumer and producer tests. On the other hand, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Latency</i> is about 23.8% lower than FIFO and LRU, 46.6% lower compared with CPFC, and 35.4% for CTDICR.