Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913339886534656 |
|---|---|
| author | Nguyen, Trung Thanh Nguyen, Truong Thao Nguyen, Dinh Tuan Anh Nguyen, Thanh Hung Nguyen, Phi Le |
| author_facet | Nguyen, Trung Thanh Nguyen, Truong Thao Nguyen, Dinh Tuan Anh Nguyen, Thanh Hung Nguyen, Phi Le |
| contents | We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems Nguyen, Trung Thanh Nguyen, Truong Thao Nguyen, Dinh Tuan Anh Nguyen, Thanh Hung Nguyen, Phi Le Networking and Internet Architecture We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold. |
| title | Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.01609 |