LoRa is becoming an attractive low cost and low power WAN solution for many real-world IoT applications. LoRa has been designed for static end-devices to individually use the optimal configuration through an adaptive data rate mechanism (ADR), thanks to the possibility to choose a set of LoRa physical layer transmission parameters. However a large class of IoT applications (e.g. connected farm) also includes mobile nodes with specific mobility patterns. For those applications, the current ADR control algorithm may not be efficient when the radio channel attenuation rapidly changes because of the node mobility. This paper contributes to enhance the ADR mechanism by taking into account the position of the mobile devices and their trajectories in order to have a dynamic allocation. The Enhanced-ADR (E-ADR) minimizes the transmission time and energy consumption as well as packet loss for mobile devices. The testbed-based experiments show that E-ADR improves the quality of service (QoS) of the overall networks.
Long Range Wide Area Network (LoRaWAN) introduces the Adaptive Data rate (ADR) mechanism [1] aiming to maximize both battery life of the end-devices and overall network capacity. ADR performs adaptive tuning of radio configurations of the nodes by adjusting bandwidth, spreading factor, coding rate and transmission power parameters whenever the signal quality changes. The ADR algorithm was established for stable radio channel environments and is not efficient when conditions dramatically change (e.g. mobility). So, we have previously proposed an Enhanced-ADR (E-ADR) [2] that deals mobile nodes in case of predefined mobility patterns. However, several Internet Of Thing (IoT) applications, such as smart cattle ranching in smart farms [3], require sensors travelling with unknown or undefined trajectories. So, this paper extends E-ADR to unknown mobility pattern. This E-ADR extension, called VHMM-based E-ADR, is based on a Variable order Hidden Markov Model (VHMM) to predict the node trajectory. It has been implemented on Waspmote SX1272 hardware platform. Experimental results show its high efficiency in terms of the packet loss rate (PLR) and the energy consumption.
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., 36 seconds 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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