Intelligent Information Retrieval Using a Mobile Agents: A Proximal Policy Optimization Approach in Dynamic Networks
Year: 2025
Type: preprint
Abstract: not-yet-known not-yet-known not-yet-known unknown Efficient information retrieval in distributed and dynamic networks remains a critical challenge, as data is often scattered across multiple nodes with varying topologies and conditions. Mobile Intelligent Agents (MIAs) require adaptive and robust routing mechanisms to optimize performance metrics such as latency, energy consumption, and throughput. This study introduces the Information Retrieval Mobile Agent (IRMA) framework, which leverages Proximal Policy Optimization (PPO), a deep reinforcement learning (RL) algorithm, for adaptive routing of Mobile Intelligent Agents (MIAs) to dynamically navigate networks and retrieve data. PPO’s stable and efficient policy updates make it ideal for continuous state-action spaces in complex environments. We address critical challenges in distributed information retrieval, demonstrating significant improvements over traditional methods. We address critical challenges in distributed information retrieval, demonstrating significant improvements over traditional methods. Our novel application of PPO to MIA routing offers superior adaptability and performance compared to conventional techniques. Experimental results show that IRMA achieves lower latency, reduced energy consumption, and higher success rates in various network scenarios significantly outperforms competing methods in various metrics, making it robust solution for information retrieval in modern distributed networks.
Author: Nermine Mahmoud
Institution: SAIT Polytechnic
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407084552