Affiliation:
1. Southeast University, China and City University of Hong Kong, China
2. City University of Hong Kong, China
3. Southeast University, China
4. National University of Defense Technology, China
Abstract
Low-Power Wide-Area Networks (LPWANs), extensively utilized for connecting billions of IoT devices, encounter wireless interference challenges in unlicensed frequency bands. Cutting-edge research suggests employing Received Signal Strength Indication (RSSI) sequences for error detection to mitigate interference-related issues. Nevertheless, the effectiveness of this method significantly declines under low signal-to-noise ratios (SNRs). Additionally, long-range communication often results in low SNR received signals, sometimes even below the noise floor. Targeting this fundamental issue, this article proposes the LPWAN packet technique, broadly applicable across diverse scenarios through edge–cloud collaboration. On the edge side, we propose an innovative architecture that fully exploits spatial distribution and interference independence in the field. Rather than utilizing resource-intensive RSSI-based error detection, we leverage a lightweight coding scheme for error detection at the Long Range (LoRa) edge, forwarding correct frames to the cloud. On the cloud side, packet recovery is achieved utilizing group-weighted voting. We design and implement ECRLoRa with commercially available devices (SemTech’s SX1278 and SX1302 LoRa chipsets) and assess its performance in low SNR environments. Our thorough evaluation demonstrates that our approach attains a Packet Recovery Ratio of 96% with low SNR (i.e., below −10 dB), resulting in 1.8× throughput, 7.5× faster recovery time, and 4.92× average accuracy compared to state-of-the-art cloud-optimized application layer solutions.
Funder
Science and Technology Innovation 2030 - Major Project
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Cited by
1 articles.
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1. Hitting the Sweet Spot: An SF-any Coding Paradigm for Empowering City-Wide LoRa Communications;2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN);2024-05-13