Abstract
Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.
| Original language | English |
|---|---|
| Pages (from-to) | 4800-4805 |
| Number of pages | 6 |
| Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Rio de Janeiro, Brazil Duration: 4 Dec 2022 → 8 Dec 2022 |
Keywords
- Aerial base station (ABS)
- anti-collision
- anti-interference
- data transmission
- medical-care sensor networks
Fingerprint
Dive into the research topics of 'Joint Anti-Interference and Anti-Collision for ABS-Assisted Medical-Care Sensor Networks'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver