Abstract
Low Power Wide Area Networks (LPWAN) are an emerging well-adopted platform to connect the Internet-of-Things. With the growing demands for LPWAN in IoT, the number of supported end-devices cannot meet the IoT deployment requirements. The core problem is the transmission collisions when large-scale end-devices transmit concurrently. The previous research mainly includes transmission scheduling strategies, collision detection and avoidance mechanism. The use of these existing approaches to address the above limitations in LPWAN may introduce excessive communication overhead, end-devices cost, power consumption, or hardware complexity. In this paper, we present S-MAC, an adaptive MAC-layer scheduler for LPWAN. The key innovation of S-MAC is to take advantage of the periodic transmission characteristics of LPWAN applications and also the collision behaviour features of LoRa PHY-layer to enhance the scalability. Technically, S-MAC is capable of adaptively perceiving clock drift of end-devices, adaptively identifying the join and exit of end-devices, and adaptively performing the scheduling strategy dynamically. Meanwhile, it is compatible with native LoRaWAN, and adaptable to existing Class A, B and C devices. Extensive implementations and evaluations on commodity devices show that S-MAC increases the number of connected end-devices by 4.06× and improves network throughput by 4.01× with PRR requirement of > 95%.
| Original language | English (US) |
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| Title of host publication | INFOCOM 2020 - IEEE Conference on Computer Communications |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 506-515 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781728164120 |
| DOIs | |
| State | Published - Jul 2020 |
| Event | 38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada Duration: Jul 6 2020 → Jul 9 2020 |
Publication series
| Name | Proceedings - IEEE INFOCOM |
|---|---|
| Volume | 2020-July |
| ISSN (Print) | 0743-166X |
Conference
| Conference | 38th IEEE Conference on Computer Communications, INFOCOM 2020 |
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| Country/Territory | Canada |
| City | Toronto |
| Period | 7/6/20 → 7/9/20 |
Bibliographical note
Funding Information:This work is supported by National Key R&D Program of China 2017YFB1003000, NSF China under Grants No. 61632008, 61872079, 61702096, 61702097, 61902065, 61972085, 61906040, NSF of Jiangsu Province under grant BK20170689, BK20190345, BK20190335, Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No.BM2003201, Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No.93K-9, and supported by Collaborative Innovation Center of Novel Software Technology and Industrialization and Collaborative Innovation Center of Wireless Communications Technology. We sincerely thank Qianqian Lin for her help and support in making this work possible.
Publisher Copyright:
© 2020 IEEE.