Neural network-based secured routing protocol for IoT environment using DE algorithm

In wireless sensor networks (WSN), routing is a crucially important stimulating topic because the system's success mostly depends on how effectively data is delivered from one place to another as the destination. Designers must be familiar with common routing strategies and their effects to make routing more efficient. Certain features in physical layers are asymmetric and can be used to improve system performance. Design that supports automatic routing can increase the quality of service (QoS) by reducing latency and increasing the delivery ratio. Designers must consider utilisation ratio measures based on link quality and lower-layer parameters to provide an efficient routing protocol. Each design paradigm has its challenges depending on the user's specified applications. All issues are classified and categorised into wireless multi-hop networks with unique features. Basic routing functions and components are essential in multi-hop routing protocols to provide better solutions to the challenges present in wireless sensor networks. The proposed system is an efficient secured routing protocol for nodes present in multi-homing IoT environments by adapting a backpropagation neural network and adaptive modulation scheme for industrial-based applications.