Deep learning based secure routing and load balancing optimization in microgrid wsn

Abstract

In Micro Grids (MGs), data is transferred through communication links for long distances through Wireless Sensor Networks (WSNs). Production, utilization and conveyance of power for a wide range of communication depend on WSN. The medium of wireless communication utilized at the figuring network s edge with a number of sensor nodes joined together. The placement of sensors ought to embrace two standards as viable exposure and transmission. The sensor nodes are required to fuse a few electrical cables, electrical machines and hardware. In MGs, sensor networks are dispersed with suitable structural and functional qualities for smooth data transmission. newlineThis research proposed an FGWHO (Fog computing based Grid model with Whale Optimization) technique possessing whale optimization and a fog computing network to improve throughput, packet delivery ratio and residual energy, Low Energy Adaptive Clustering Hierarchy (LEACH) routing based C-means clustering for data collection and QoS-aware multi-hop communications network using ID based secured routing protocol with data aggregation for reducing utilization of energy and communication overhead. In FGWHO, the WSN incorporated with MicroGrid is developed with the network which is registering edge like a tree structure. The microgrid associated with the main grid shows autonomous attributes with grid connected edges. In a Microgrid, there are two feeders and one bus for various generating stations. The nodal sensors are prepared for monitoring status and detecting faults. In a network there are various energy storing gadgets and various electrical hardware that utilize power and reduce the network lifetime. newline