Artificial self-attention rabbits battle royale multiscale network based robust and secure data transmission in mobile Ad Hoc networks

Abstract

In a Mobile Ad hoc Network (MANET), the network structure constantly evolves due to the movement of devices, users, or nodes. The Black Hole Attack (BHA) is recognized as the most impactful threat in MANETs, where an attacker node intentionally discards all data traffic, hence minimizing the network performance. Therefore, developing a secure and robust routing protocol is vital for efficient data transmission in a MANET. Although existing solutions have been designed to counteract malicious nodes, these solution presents drawbacks such as the need for additional hardware, notable delivery delays, limited throughput, increased energy consumption, and minimum packet delivery ratio. Thus, this research work introduces a new updated Ad hoc On-demand Distance Vector (AODV) routing protocol that integrates the benefits of the Artificial Rabbits Battle Royale Optimization (ARBRO), and Self-attention Multiscale Network (SMNet). The role of the ARBRO algorithm is to optimize the route by selecting the most appropriate node properties. Then, SMNet identifies the BHA node using these node properties. The proposed scheme is implemented in MATLAB. Experimental results determines that the introduced approach attains a delay of 0.039 s, a throughput of 94.57 Kbps, and a packet delivery ratio of 98.76 %, which are better than the existing techniques. Simulation results demonstrate that the introduced method improves network performance by enhancing the quality of service in the presence of a BHA.