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Optimal Data Collection Time in LoRa Networks—A Time-Slotted Approach

1
Tyndall National Institute, University College Cork, T12R5CP Cork, Ireland
2
School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
3
Université Côte d’Azur, CNRS, I3S, Inria, 06902 Sophia Antipolis, France
4
School of Computer Science and IT, University College Cork, T12R5CP Cork, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(4), 1193; https://doi.org/10.3390/s21041193
Received: 28 December 2020 / Revised: 27 January 2021 / Accepted: 28 January 2021 / Published: 8 February 2021
(This article belongs to the Section Sensor Networks)
LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. These devices are often deployed in remote areas where the end-to-end connectivity provided through one or more gateways may be limited. In this paper, we examine the case where the gateway is not available at all times. As a consequence, the sensing data need to be buffered locally and transmitted as soon as a gateway becomes available. However, due to the Aloha-style transmission policy of current LoRa-based standards, such as the LoRaWAN, delivering a large number of packets in a short period of time by a large number of nodes becomes impossible. To avoid bursts of collisions and expedite data collection, we propose a time-slotted transmission scheduling mechanism. We formulate the data scheduling optimisation problem, taking into account LoRa characteristics, and compare its performance to low complexity heuristics. Moreover, we conduct a set of simulations to show the benefits of synchronous communications on the data collection time and the network performance. The results show that the data collection can reliably be achieved at least 10 times faster compared to an Aloha-based approach for networks with 100 or more nodes. We also develop a proof-of-concept to assess the overhead cost of communicating the schedule to the nodes and we present experimental results. View Full-Text
Keywords: LoRa; scheduling; resource allocation LoRa; scheduling; resource allocation
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MDPI and ACS Style

Zorbas, D.; Caillouet, C.; Abdelfadeel Hassan, K.; Pesch, D. Optimal Data Collection Time in LoRa Networks—A Time-Slotted Approach. Sensors 2021, 21, 1193. https://doi.org/10.3390/s21041193

AMA Style

Zorbas D, Caillouet C, Abdelfadeel Hassan K, Pesch D. Optimal Data Collection Time in LoRa Networks—A Time-Slotted Approach. Sensors. 2021; 21(4):1193. https://doi.org/10.3390/s21041193

Chicago/Turabian Style

Zorbas, Dimitrios; Caillouet, Christelle; Abdelfadeel Hassan, Khaled; Pesch, Dirk. 2021. "Optimal Data Collection Time in LoRa Networks—A Time-Slotted Approach" Sensors 21, no. 4: 1193. https://doi.org/10.3390/s21041193

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