Improving Quality-of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

Abstract

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

The Art of Designing Remote IoT Devices—Technologies and Strategies for a Long Battery Life

“…This can considerably lower a major obstacle to prolong the battery life of remote IoT nodes, as explained in this paper. Furthermore, researchers propose different mechanisms to optimize conventional LPWAN technologies [ 18 , 41 , 103 ]. Multiple Radio Access Technology (Multi-RAT), adopted from cellular standards, is introduced to intelligently switch between LPWAN technologies in order to dynamically adapt to the current application requirements and operating environments [ 104 , 105 ].…”

A Survey on Long-Range Wide-Area Network Technology Optimizations

“…To be more precise, a given IoT device can use a particular BW to transmit each packet, which indicates the width of the frequencies in the transmission band [30]. LoRaWAN makes it possible to set the BW at 125 kHz, 250 kHz, and 500 kHz according to the regional parameters [32].…”

“…The CF is spread out along different frequency channels through leveraging pseudo-random channel hopping implementation [69]. The CF values depend on local frequency regulations, whereas LoRaWAN gateways usually support eight channels, while the IoT devices generally support at least 16 channels [30,69]. Specifically, CF can be configured within the range of 137 to 1020 MHz, following the legislation in force in the geographic region [70].…”

“…To cope with this issue, LoRaWAN considers a set of radio parameters that can be adjusted on the fly by a resource allocation mechanism [29]. In specific terms, resource allocation means adjusting on the fly a set of radio parameters, such as BW, CF, CR, DC, SF, and TP [30,31,32]. These parameters can be adjusted by the network server or device's application layer, providing a trade-off among transmission range, data rate, air time, interference, and energy consumption [32].…”

Experimental Evaluation of the Availability of LoRaWAN Frequency Channels in the Czech Republic

“…However, most studies focus on a targeted verification of technological possibilities, or on improving Quality-Of-Service in LoRaWAN through Optimized Radio Resource Management as settings parameters (ADR, SF, CSS, channels, etc.) [ 10 , 11 , 12 ]. The consequences of such optimization have an impact on energy consumption, which is also addressed by several authors, as exemplified: “Comparison of LoRaWAN Classes and their Power Consumption” [ 13 ].…”