Abstract
Recent advances in Low-Power Wide-Area Networks have mitigated interference by using cloud assistance. Those methods transmit the RSSI samples and corrupted packets to the cloud to restore the correct message. However, the effectiveness of those methods is challenged by the high transmission data amount. This paper presents a novel method for interference mitigation in a Edge-Cloud collaborative manner, namely ECCR. It does not require transmitting RSSI sample any more, whose length is eight times of the packet’s. We demonstrate the disjointness of the bit errors of packets at the base stations via real-word experiments. ECCR leverages this to collaborate with multiple base stations for error recovery. Each base station detects and reports bit error locations to the cloud, then both error checking code and interfered packets from other receivers are utilized to restore correct packets. ECCR takes the advantages of both the global management ability of the cloud and the signal to perceive the benefit of each base station, and it is applicable to deployed LP-WAN systems (e.g. sx1280) without any extra hardware requirement. Experimental results show that ECCR is able to accurately decode packets when packets have nearly \(51.76\%\) corruption.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Knight, M., Seeber, B.: Decoding LoRa: realizing a modern LPWAN with SDR. In: Proceedings of the GNU Radio Conference, vol. 1, no. 1 (2016)
Balanuta, A., Pereira, N., Kumar, S., Rowe, A.: A cloud-optimized link layer for low-power wide-area networks. In: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, pp. 247–259 (2020)
Dongare, A., et al.: Charm: exploiting geographical diversity through coherent combining in low-power wide-area networks. In: 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 60–71 (2018)
Eletreby, R., Zhang, D., Kumar, S., Yağan, O.: Empowering low-power wide area networks in urban settings. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 309–321 (2017)
Hessar, M., Najafi, A., Gollakota, S.: NetScatter: enabling large-scale backscatter networks. In: 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2019), pp. 271–284 (2019)
Xia, X., Zheng, Y., Gu, T.: FTrack: parallel decoding for LoRa transmissions. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems, pp. 192–204 (2019)
Mikhaylov, K., Petaejaejaervi, J., Haenninen, T.: Analysis of capacity and scalability of the LoRa low power wide area network technology. In: European Wireless 2016; 22th European Wireless Conference, pp. 1–6. VDE (2016)
Li, L., Ren, J., Zhu, Q.: On the application of LoRa LPWAN technology in sailing monitoring system. In: 2017 13th Annual Conference on Wireless On-Demand Network Systems and Services (WONS), pp. 77–80 (2017)
Petäjäjärvi, J., Mikhaylov, K., Yasmin, R., Hämäläinen, M., Iinatti, J.: Evaluation of LoRa LPWAN technology for indoor remote health and wellbeing monitoring. Int. J. Wireless Inf. Netw. 24(2), 153–165 (2017)
Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017)
Sartori, D., Brunelli, D.: A smart sensor for precision agriculture powered by microbial fuel cells. In: 2016 IEEE Sensors Applications Symposium (SAS), pp. 1–6 (2016)
Ilie-Ablachim, D., Pătru, G.C., Florea, I.M., Rosner, D.: Monitoring device for culture substrate growth parameters for precision agriculture: acronym: MoniSen. In: 2016 15th RoEduNet Conference: Networking in Education and Research, pp. 1–7. (2016)
MATHWORKS Help Center. https://ww2.mathworks.cn/help/. Accessed 5 Apr 2021
WLAN Waveform Generator. https://ww2.mathworks.cn/help/wlan/. Accessed 5 Apr 2021
Wi-Fi Alliance. Wi-fi halow. http://www.wi-fi.org/discover-wi-fi/wi-fi-halow. Accessed 5 Apr 2021
LoRa Alliance. LoRaWAN. https://lora-alliance.org/about-lorawan/. Accessed 5 Apr 2021
Hu, B., Yin, Z., Wang, S., Xu, Z., He, T.: SCLoRa: leveraging multi-dimensionality in decoding collided LoRa transmissions. In: 2020 IEEE 28th International Conference on Network Protocols (ICNP), pp. 1–11. IEEE (2020)
Wang, S., Yin, Z., Li, Z., He, T.: Networking support for physical-layer cross-technology communication. In: 2018 IEEE 26th International Conference on Network Protocols (ICNP), pp. 259–269. IEEE (2018)
Wang, S., Kim, S.M., Yin, Z., He, T.: Correlated coding: efficient network coding under correlated unreliable wireless links. In: 2014 IEEE 22nd International Conference on Network Protocols, pp. 433–444. IEEE (2014)
Jiang, W., Yin, Z., Liu, R., Li, Z., Kim, S.M., He, T.: BlueBee: a 10,000x faster cross-technology communication via PHY emulation. In: 15th ACM Conference on Embedded Network Sensor Systems, pp. 1–13 (2017)
Kim, S.M., He, T.: FreeBee: cross-technology communication via free side-channel. In: 21st Annual International Conference on Mobile Computing and Networking, pp. 317–330 (2015)
Jiang, W., Yin, Z., Kim, S.M., He, T.: Transparent cross-technology communication over data traffic. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9 (2017)
Eletreby, R., Zhang, D., Kumar, Yaǧan, S.: Empowering low power wide area networks in urban settings. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 309–321. ACM (2017)
Xia, X., Zheng, Y., Gu, T.: FTrack: parallel decoding for LoRa transmissions. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems, pp. 192–204. ACM (2019)
Wang, X., Kong, L., He, L., Chen, G.: mLoRa: a multi-packet reception protocol in LoRa networks. In: IEEE 27th International Conference on Network Protocols, pp. 1–11. IEEE (2019)
Xu, Z., Luo, J., Yin, Z., He, T., Dong, F.: S-MAC: achieving high scalability via adaptive scheduling in LPWAN. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 506–515. IEEE (2020)
Gamage, A., Liando, J.C., Gu, C., Tan, R., Li, M.: LMAC: efficient carrier-sense multiple access for LoRa. In: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, pp. 1–13. IEEE (2020)
Shahid, A., et al.: A convolutional neural network approach for classification of LPWAN technologies: Sigfox, LoRa and IEEE 802.15. 4g. In: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–8. IEEE (2019)
SEMTECH. https://www.semtech.com/. Accessed 5 Apr 2021
Acknowledgement
This work was supported in part by National Natural Science Foundation of China under Grant No. 61902066, Natural Science Foundation of Jiangsu Province under Grant No. BK20190336, China National Key R&D Program 2018YFB2100302 and Fundamental Research Funds for the Central Universities under Grant No. 2242021R41068.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mei, L., Yin, Z., Zhou, X., Wang, S., Sun, K. (2021). ECCR: Edge-Cloud Collaborative Recovery for Low-Power Wide-Area Networks Interference Mitigation. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-85928-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85927-5
Online ISBN: 978-3-030-85928-2
eBook Packages: Computer ScienceComputer Science (R0)