Development Of Wireless Sensor Network Middleware Using Machine Learning Approach

Security challenges with wireless sensor networks (WSNs) have yet to be entirely resolved, despite their extensive usage in a number of applications. To solve these limits, middleware is often employed as an intermediary layer among WSNs and end users. Most existing middleware, on the other hand, has yet to safeguard data during transmission against malicious or unexpected assaults. This research proposes an intelligent middleware based on the unsupervised learning methodology of Generative Adversarial Networks (GANs). A GAN has two networks: a generator (G) and a detector (D) (D). To fool the attacker, the G creates fake data that looks like actual sampling and blends it with real sensor data. The D is made up of many layers that can distinguish between true and false input. This approach produces a real-world analysis of the data that is safely sent over the WSN. Python is used to develop the platform and keras is used to run the experiments. The findings demonstrate that the suggested strategy not only enhances data accuracy but also data security by avoiding data manipulation. When compared to traditional data transfer, transmitting data from the WSN to the end user becoming significantly better secure and efficient.