Enhanced dynamic duty cycle in LoRaWAN network

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Abstract

LoRa’s long-range and low-power features have made it an attractive candidate for IoT devices in various fields. In this work, we present an enhanced LoRaWAN protocol. LoRaWAN MAC protocol is characterized by the restrictive use of the channel, limited by the regulatory authorities to a 1% duty cycle per cycle (i.e., 36s per hour) per node. This regulation penalizes the nodes which require a channel access time greater than the limited duty cycle to occasionally transmit a large amount of data such as video surveillance or access control information in applications like smart school surveillance. However, some other nodes like environment sensors sharing a same LoRaWAN server may send very small amounts of information (e.g. temperature, humidity,..) and under-use the authorized activity time of 1% duty cycle. Hence the idea of implementing an activity time sharing mechanism among nodes that allows devices to borrow additional activity time from a device or set of devices that have completed the transmission of their packets and do not need the remaining time of the corresponding duty cycle. Our work extends and improves the activity time sharing mechanism initially proposed in [1]. Instead of FIFO sharing-time allocation based on a global activity time, which may lead to the starvation of the nodes that are others than that in the head of FIFO line, we propose a new time allocation algorithm based on the classification of the different requests according to their needs in terms of their QoS requirements. It allows to satisfy a larger number of nodes requiring extra time, with less control overheads while ensuring fairness. Our time-sharing algorithm has been implemented and tested on the wasp-mote chip of libelium, showing the performance improvement and its practical usability.

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APA

Benkahla, N., Tounsi, H., Song, Y. Q., & Frikha, M. (2018). Enhanced dynamic duty cycle in LoRaWAN network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11104 LNCS, pp. 147–162). Springer Verlag. https://doi.org/10.1007/978-3-030-00247-3_15

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