Dynamic Replica Management in Fog-enabled IoT using Enhanced Data Mining Technique

With the fast growth of IoT devices and Sensors, producing a massive amount of data. It causes various problems such as storage overhead, high latency, and network congestions. Fog computing includes fog nodes which are heterogeneous and provide solutions on latency sensitive data services to place data on fog nodes closer to data generators. Unfortunately, existing cloud technology cannot meet minimum latency and data management for IoT devices and end-users. Simultaneously, related work focuses only on data placement on a fog node. In this research paper, a novel solution is presented for dynamic replica management in Fog enabled IoT by considering data mining called Enhanced data mining dynamic replication (EDMDR). Using maximal frequent pattern mining not only improves replication but help to diminish data management cost. In the end, the results of simulation reduced total latency issues by 62%,83% and 79% as compared to FC analytical model,iFogStorM and DMDR. Simultaneously, the response time of EDMDR is less than existing methods, which proves the better optimization for Fog infrastructure. Keywords: Data Mining,Fog Computing,Dynamic Replication,Latency,iFogSi