Hybrid trust-based secure routing protocol for detection of routing attacks in environment monitoring over MANETs

Abstract

Maintaining a healthy environment requires constant vigilance. Keeping constant tabs on environmental factors might be a useful tool in the fight against and recovery from environmental degradation. It is possible to send environmental data from strategically positioned sensors that monitor the surrounding circumstances. Environmentalists are the first to arrive at a disaster site, assess the situation, and take corrective measures. To get information from the sensors, they must coordinate with one another. Remote environmental sensing, the tracking from several physical systems, as well as risk assessment and management, have all seen significant improvements as a result of the use of these networks, which are comprised of many dispersed devices, each of which includes sensing, processing, and wireless technology capabilities. To facilitate communication between sensors and ecologists, the authors of this research suggest the usage of MANETs. To address these issues, a hybrid trust-based secure routing protocol is designed. Firstly, nodes are clustered using the Skill Optimization Technique. The Direct and Indirect (Hybrid) trust is computed using the Deep Q-Learning technique and then Routing is established using Diffusion Convolutional Neural Networks. With these methods, routing attacks such as Black Hole, Gray hole, and Wormhole attacks are identified and compared with the traditional security protocols using network simulation tools.