Experimental Analysis of Throughout Maximization and Delay Reduction over Wireless Sensor Network Through Neural Route Mapping

The challenge of establishing an information-sharing network to optimize network capacity has arisen with the emergence of sensor nodes with enhanced communication and sensing capabilities. Interfering issues arise as a result of channel sharing for increased data transmission. The importance of interferences increases as simultaneous transmissions are implemented to enhance the capacity of wireless networks. In such instances, it is challenging to achieve a low latency and a high throughput. In this paper, a novel routing protocol, the Neural Route Mapping Protocol (NRMP), is developed to resolve this issue. The proposed scheme is cross-validated against the conventional routing model, Dynamic Source Routing (DSR), to assess its efficacy. In this proposed routing technique, NRMP, multiple transmitters jointly encode their signals to intended receivers, thereby separating and eliminating interfering signals. The proposed method considerably enhances the network's performance, as demonstrated by the simulation results, by lowering latency and increasing throughput, in comparison to the DSR protocol. In real-world situations when wireless sensor networks face obstacles in their coverage area, our experimental results indicate that the suggested method can surpass prior heuristic solutions in the literature.