Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

Guibing Zhu, Chun Hao Liao, Theerat Sakdejayont, I. Wei Lai, Yoshiaki Narusue, Hiroyuki Morikawa

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice.

Original languageEnglish
Article number8637935
Pages (from-to)21584-21596
Number of pages13
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • LoRa
  • Low-power wide area network (LPWAN)
  • multi-hop network
  • spreading factor (SF) allocation algorithm
  • tree-based spreading factor clustering algorithm (TSCA)

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

Zhu, G., Liao, C. H., Sakdejayont, T., Lai, I. W., Narusue, Y., & Morikawa, H. (2019). Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering. IEEE Access, 7, 21584-21596. [8637935]. https://doi.org/10.1109/ACCESS.2019.2898239