arXiv cs.AI· Yihang Cao, Milena Radenkovic·· 14 小时前AI 评分24
基于 NLP 的消息优先级排序如何用于机会路由：尼泊尔地震应急通信案例研究
NLP-based Message Prioritisation for Opportunistic Routing: A Case Study of Nepal Earthquake Emergency Communication
AI 导读
该研究以 2015 年尼泊尔地震为案例，检验基于 NLP 的语义消息优先级排序能否改善机会式应急通信中的紧急消息处理。研究用 TF-IDF+Logistic Regression 与 BERT 将社交媒体消息分为九类人道主义类别并映射为 P1–P4 四级路由优先级，接入 ONE 模拟器的加德满都地震场景与改进型 Binary Spray and Wait 路由器。
正文 · 原文
Abstract:This dissertation investigates whether Natural Language Processing (NLP) based semantic message prioritisation can improve the treatment of urgent disaster messages in opportunistic emergency communication, using the 2015 Nepal earthquake as a case study. TF IDF with Logistic Regression and BERT are used to classify social media messages into nine humanitarian categories, which are mapped to four routing priorities from P1 to P4. These priorities are integrated into the ONE simulator within a controlled Kathmandu earthquake scenario using a modified Binary Spray and Wait router. BERT achieves higher priority accuracy and P1 recall than Logistic Regression, while reducing under prioritisation and severe priority errors. However, improved NLP performance does not produce uniform network gains. P1 delivery probability remains high and similar across routing conditions, and no consistent latency reduction is observed. The clearest difference appears under buffer pressure, where the BERT condition shows fewer observed ground truth P1 buffer losses than Logistic Regression. The results indicate that semantic prioritisation is most valuable when limited transmission and storage resources are contested, although it cannot overcome mobility and contact constraints in opportunistic networks.
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37933 [cs.NI] |
| (or arXiv:2610.37933v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37933 arXiv-issued DOI via DataCite |
Submission history
From: Milena Radenkovic [view email]
[v1]
Tue, 27 Oct 2026 20:25:01 UTC (6,310 KB)
来源：arXiv cs.AI · arxiv.org