Data Security Assurance with data Aggregation and Fragmentation in WSN via Improved Cryptographic Model: A Deep Learning Assisted Cluster based Routing

Since the sensor nodes (SNs) have limited resources, developing an energy-aware routing method for extending the total lifespan of the network has become one of the most vital developments in WSNs. Cluster based routing (CBR) algorithms have shown to be useful in topological management of network, energy reduction, data aggregation, and other areas as an emerging field of routing technique. This research develops a new CBR with DAF (Data Aggregation and Fragmentation) Approach, where, the energy of nodes is predicted using squeeze net model. After energy prediction, the optimal CH is chosen using CHOI-COA Algorithm based on constraints like Energy, network lifetime, Risk, Trust, Link Quality. Then, routing is done using Dijkstra's algorithm. Further, Data Aggregation is done using improved kernel Least Mean Square method (IKLMS) and the aggregated data is fragmented using FCM clustering. Finally, data encryption is done via Improved ECC (IECC). Finally, analysis is done to validate the adopted CHOI-COA model for CBR with DAF.