INTELLIGENT LSTM-DRIVEN RPA FRAMEWORK FOR PREDICTIVE TASK MANAGEMENT IN 5G EDGE NETWORKS

Engineering notes

Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The high velocity of 5G networks and edge computing has produced new requirements on low-latency and high-throughput services never before seen, and resource management has become a very significant issue. The conventional Robotics Process Automation (RPA) systems are designed in a way that is reactive, and they can only execute fixed rules once an unforeseen event happens like failure or overload, and therefore they are not effective in dynamically changing edge cases. A smart hybrid architecture is introduced in this paper which combines the Long Short-Term Memory (LSTM) networks with RPA to provide predictive task management in 5G edge networks. The LSTM models are used to predict the edge node overloads, network congestion, and possible task failures by analyzing the historical traffic patterns. According to these predictions, RPA agents automatically do task reallocation, load balancing, and priority adjustments thus changing their automation approach to reactive to proactive. The MATLAB based simulation architecture can simulate the multi-node edge environment with the dynamic number of users to validate the proposed approach in realistic conditions. The outcomes of the simulations indicate that, the hybrid LSTM-RPA framework has the ability to reduce latency, achieve better throughput, and better utilization of resources in comparison to the traditional reactive RPA systems. With predictive analytics and autonomous process management, the proposed framework provides a scalable and self-learning framework to guarantee the reliability of service delivery, costeffective operations, and real-time responsiveness in edge computing networks.

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