TY - JOUR
T1 - Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering
AU - Zhu, Guibing
AU - Liao, Chun Hao
AU - Sakdejayont, Theerat
AU - Lai, I. Wei
AU - Narusue, Yoshiaki
AU - Morikawa, Hiroyuki
N1 - Funding Information:
This work was supported in part by the Cross-Ministerial Strategic Innovation Promotion Program, Infrastructure Maintenance, Renewal, and Management Technology, in part by the JSPS Grant-in-Aid for Scientific Research (A) under Grant JP16H02358, and in part by the Research Laboratories, NTT Docomo, Inc.
Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - LoRa
KW - Low-power wide area network (LPWAN)
KW - multi-hop network
KW - spreading factor (SF) allocation algorithm
KW - tree-based spreading factor clustering algorithm (TSCA)
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U2 - 10.1109/ACCESS.2019.2898239
DO - 10.1109/ACCESS.2019.2898239
M3 - Article
AN - SCOPUS:85062850292
SN - 2169-3536
VL - 7
SP - 21584
EP - 21596
JO - IEEE Access
JF - IEEE Access
M1 - 8637935
ER -