LoRaSense: An Interference-aware Concurrent Transmission Model

LoRa, one of the most potential LPWAN (Low-Power Wide Area Network) techniques, has received widespread attention for its far transmission distance and long battery life. These characteristics make it successfully applied in target tracking, water level monitoring, fire alarm, smart city, etc. Since these applications mainly require tens of hundreds of access devices to collect data, different LoRa networks will overlap, so an interference-free network is badly needed. Current collision avoidance scheme, however, could cause collisions again, especially when the the duty cycle of interference source is high. In other words, current scheme is not interference-free. Motivated by the exist interference, this paper presents an interference-aware concurrent transmission model: LoRaSense. Specifically, LoRaSense estimates the idle cycles of interference source through interference-aware model based on RSSI, and then achieves concurrent transmission of access devices and interference sources combining with collision model. Our LoRaSense increases channel utilization while resisting interference. To demonstrate the utility of LoRaSense, we build a prototype of LoRaSense in one LoRa gateway and three LoRa nodes. Our real-world experiments show that LoRaSense can achieve 10%-15% packet reception ratio improvement compared to LoRaWAN.

[1]  Lei Tang,et al.  EM-MAC: a dynamic multichannel energy-efficient MAC protocol for wireless sensor networks , 2011, MobiHoc '11.

[2]  Muriel Médard,et al.  Symbol-level network coding for wireless mesh networks , 2008, SIGCOMM '08.

[3]  G. AndrewsJ. Interference cancellation for cellular systems , 2005 .

[4]  Eric Anderson,et al.  X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks , 2006, SenSys '06.

[5]  Srihari Nelakuditi,et al.  Successive interference cancellation: a back-of-the-envelope perspective , 2010, Hotnets-IX.

[6]  Yunhao Liu,et al.  Bulk Data Dissemination in Wireless Sensor Networks: Analysis, Implications and Improvement , 2016, IEEE Transactions on Computers.

[7]  Kien A. Hua,et al.  ZIGZAG: an efficient peer-to-peer scheme for media streaming , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[8]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[9]  J. G. Andrews,et al.  Interference cancellation for cellular systems: a contemporary overview , 2005, IEEE Wireless Communications.

[10]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[11]  Guoliang Xing,et al.  Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance , 2010, The 18th IEEE International Conference on Network Protocols.