Elimination of Data Modification in Sensor Nodes of WSN Using Deep Learning Model

Wireless sensor networks (WSNs) are usually made up of small computers that measure things like temperature, pressure, light, and vibration, as well as medical devices that have signal transceivers that work on a certain radio display. It is often used in large-scale sensor networks because it can be built in different ways and doesn't cost much to set up. Other wireless networks use smart sensors to send data interfaces. The vast majority of devices that are connected to each other are given wireless networks. On the other hand, the u200b touch network can connect up to 65,000 devices. As the price of wireless solutions keeps going down and their features keep getting better, you may start to use wired solutions again for telemetry data collection systems and long-distance communication. This study showed how to use deep learning to stop sensor nodes from changing data. The sensor nodes store estimates and parameters. If these expected data values change, the network won't work right, which will also shorten the life of the node. When the sensor nodes were given out, data protection became more important. The proposed algorithm uses 98.82% as much energy as it needs to.