基于NLP的突发消息优先级排序用于机会路由：以2015年尼泊尔地震应急通信为例
NLP-based Message Prioritisation for Opportunistic Routing: A Case Study of Nepal Earthquake Emergency Communication
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这项研究以2015年尼泊尔地震为案例，验证基于NLP的语义消息优先级能否改善机会式应急通信中紧急消息的处理。TF-IDF加Logistic Regression与BERT将社交媒体消息分为九个人道主义类别并映射到P1至P4四个路由优先级，接入ONE模拟器的Kathmandu地震场景中修改版Binary Spray and Wait路由。
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