LAUSR

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 Dynamic Multi-Attribute Caching mechanism for NDN-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 Cache Hit Ratio 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 Latency is about 23.8% lower than FIFO and LRU, 46.6% lower compared with CPFC, and 35.4% for CTDICR.