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Wireless Networks with Machine Learning Routing Enabled
Based on the network's modifications and the type of data it perceives; the machine learning system was specifically created to save energy and extend sensor life. Additionally, by combining similar types of data that are sensed inside the network coverage region, the sensor network is created using machine learning and the BAT computational approach to minimise the network under redundancy. Additionally, the network uses very little energy from the sensors, therefore feature sets in fuzzy-neuron machine learning mode are employed to choose neighbours. Furthermore, the packet speed sensed and the detecting intervals between packets are used to calculate the energy used by the sensor in the network. Additionally, the neural network is used to open and aggregate the data. The approach designed to reduce the aggregation method's energy use. The same data is then aggregated once that is finished (removing the noise from the data). This lowers energy consumption and increases the use of network resources. Furthermore, a routing path that optimises during the routing path development process is created using BAT computation, resulting in a consistent and energy-efficient path.
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