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Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing surveys or tutorials have addressed the importance of ML- or DL-based techniques for LoRaWAN. This article provides a comprehensive survey of current LoRaWAN challenges and recent solutions, particularly using DL and ML algorithms. The primary objective of this survey is to stimulate further research efforts to enhance the performance of LoRa networks and facilitate their practical deployments. We begin by emphasizing the characteristics of LoRaWAN compared to other LPWAN technologies and then examine the technical specifications of LoRaWAN that have been released so far, as well as the current research trends. Furthermore, we discuss an overview of the most utilized DL and ML algorithms for overcoming LoRaWAN challenges. We also present an interoperable reference architecture for LoRaWAN and validate its effectiveness using a wide range of applications. Additionally, we shed light on several evolving challenges of LoRa and LoRaWAN for the future digital network, along with possible solutions. Finally, we conclude our discussion by briefly summarizing our work.
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LongShoT: Long-range Synchronization of Time
Low-Power Wide Area Networks, such as LoRaWAN, are rapidly gaining popularity in the field of wireless sensing and actuation. While LoRaWan is heavily studied in applications and performance, the concept of time has rarely been characterized in such networks. Many applications will require synchronized local clocks with varying levels of precision in order to maintain consistency and coordination in the network. Traditional time synchronization protocols however do not fit LoRaWAN's delay-inherent, low duty cycle, network model and wide-area deployment topology. Meanwhile, relying on GPS for time is not an option for low-power applications. In this paper, we present LongShoT, a time synchronization scheme built on LoRaWan capable of synchronizing device clocks to within 10μs of a reference clock with a single network request. This is achieved by utilizing the deterministic properties of Lo-Ra Wan networks along with hardware- and MAC-level timestamping of packets. LongShoT was implemented on consumer off-the-shelf hardware and evaluated over physically distributed devices using GPS 1PPS as a reference. Our results show that LongShoT achieves an average synchronization error of less than 2μs and compensates oscillator drift to less than 0.1ppm with devices distributed within 4km of a gateway.
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- Award ID(s):
- 1646235
- PAR ID:
- 10107899
- Date Published:
- Journal Name:
- 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
- Page Range / eLocation ID:
- 289 to 300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3302506.3310408
