Dynamic community evolution analysis for performance optimization in large scale RFID networks

Handling Large scale RFID network is most important for Tracking and Locating objects in the Intelligent Transportation System and IoT based RFID networks. This research work focuses to build the road map with RFID support without interference of satellite. The cluster-based RFID network can be supported to enlarge the RFID network. In this paper, proposes the Novel Community Evolution Analysis (NCEA) method for selecting cluster heads, designed to tackle both strong and weak events within the network. The traditional clustering method may miss some weak events occurrences and its leads to impact the efficiency of cluster head selection of the cluster network. To identify this problem, our method incorporates the required strong and weak events. This proposed method concentrates the energy management and cluster head selection management accurately. Also, the cluster head selection may happen through high energy node and close neighbor node to all the participated nodes in the cluster. The cluster head selection can be happened by different events such as form, disappear, shrink, expand, split, merge. The NCEA approach demonstrates an accuracy of 98%, a vulnerability rate of 20%, a success rate of 89%, a latency of 11.4 s, and a throughput of 93%. These results highlight the effectiveness of our method in enhancing the energy efficiency and overall performance of large-scale RFID networks.