Enhancing Reliability in Delay Tolerant Networks with Hybrid AI Algorithms in Remote Areas

DTNs function as critical communication infrastructure in remote regions occupied by insufficient resources because traditional networks break down when resources run short and latency is high. The proposed research implemented a combination of Artificial Intelligence (AI)-based techniques to boost Delay Tolerant Networks' operational reliability along with performance enhancement. The proposed framework unites LSTM models for movement prediction with tactical route optimization through Genetic Algorithms and integrates reinforcement learning for operational resource oversight. These AI-based approaches solve download and transmission problems in addition to power efficiency challenges that affect Delay-Tolerant Networks. The proposed methodology underwent testing compared to standard Epidemic and PROPHET DTN protocols across diverse network configurations. The implementation outcome presented a baseline Packet Delivery Ratio (PDR) of 96.2 percent as well as revealed a mix of a 40 percent lower Average Latency and reduced Energy Consumption by 25 percent and that which enhanced a 25 percent greater Throughput. Such monitoring potentials of hybrid AI methods prove their efficiency to optimize the employment of resources and the efficiency of routing in order to minimize problematic zones within networks. The proposed framework is well-designed in regard to providing suitable application of disaster responses and environmental monitoring along with remote communication requirements due to its scalability and reliable features. Future studies will examine how to deploy for the real world as well as explore how to integrate the emerging edge computing and blockchain technology to achieve better security and scale.