标题： 基于多层感知器的中继节点选择用于下一代智能容迟网络 显示英文标题

标题： Multi-Layer Perceptron-Based Relay Node Selection for Next-Generation Intelligent Delay-Tolerant Networks

摘要： 延迟容忍网络（DTNs）在高度动态和具有挑战性的场景中对于应急通信至关重要，这些场景的特点是连接间断、频繁中断和不可预测的节点移动性。 尽管一些协议因其简单性和低开销而被广泛采用，但它们的静态复制策略缺乏自适应区分高质量中继节点的能力，通常会导致消息传播效率低下且次优。 为解决这一挑战，我们提出了一种新颖的智能路由增强方法，将基于机器学习的节点评估集成到Spray and Wait框架中。 从仿真日志中提取了几种动态核心特征，并用于训练多个分类器——多层感知器（MLP）、支持向量机（SVM）和随机森林（RF）——以预测在动态条件下节点是否适合作为中继。 训练好的模型通过轻量级的基于Flask的RESTful API进行部署，实现实时、自适应的预测。 我们实现了增强的路由器MLPBasedSprayRouter，该路由器根据预测的中继质量选择性地转发消息。 引入了缓存机制以减少计算开销并确保稳定、低延迟的推理。 在现实应急移动场景下的大量实验表明，所提出的框架相比基线协议显著提高了交付率，同时降低了平均延迟。 在所有评估的分类器中，MLP表现出最稳健的性能，在准确率、适应性和推理速度方面始终优于SVM和RF。 这些结果证实了将机器学习集成到DTN路由中的新颖性和实用性，为智慧城市、灾害恢复和其他动态环境中的弹性智能通信系统铺平了道路。 显示英文摘要

摘要： Delay Tolerant Networks (DTNs) are critical for emergency communication in highly dynamic and challenging scenarios characterized by intermittent connectivity, frequent disruptions, and unpredictable node mobility. While some protocols are widely adopted for simplicity and low overhead, their static replication strategy lacks the ability to adaptively distinguish high-quality relay nodes, often leading to inefficient and suboptimal message dissemination. To address this challenge, we propose a novel intelligent routing enhancement that integrates machine learning-based node evaluation into the Spray and Wait framework. Several dynamic, core features are extracted from simulation logs and are used to train multiple classifiers - Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF) - to predict whether a node is suitable as a relay under dynamic conditions. The trained models are deployed via a lightweight Flask-based RESTful API, enabling real-time, adaptive predictions. We implement the enhanced router MLPBasedSprayRouter, which selectively forwards messages based on the predicted relay quality. A caching mechanism is incorporated to reduce computational overhead and ensure stable, low-latency inference. Extensive experiments under realistic emergency mobility scenarios demonstrate that the proposed framework significantly improves delivery ratio while reducing average latency compared to the baseline protocols. Among all evaluated classifiers, MLP achieved the most robust performance, consistently outperforming both SVM and RF in terms of accuracy, adaptability, and inference speed. These results confirm the novelty and practicality of integrating machine learning into DTN routing, paving the way for resilient and intelligent communication systems in smart cities, disaster recovery, and other dynamic environments.