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 LoRaWAN 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.

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

“…In this paper, we performed a comprehensive analysis of solutions that have been developed to optimize ADR algorithms. The proposed optimization techniques address specific challenges such as scalability [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], throughput [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] and energy efficiency [ 20 , 46 , 47 , 48 , 49 , 50 , 51 ]. Our analysis distinguished the approaches used and highlighted the challenges and performance in the studies considered.…”

“…All the transmission parameter needed to be broadcast to the end nodes resulting in a memory consumption. In [ 50 ] the Approximation Algorithm maintains a linear complexity time in the worst-case. The algorithm is designed to function in the LoRaWAN Application Layer and end nodes with a time complexity that is equivalent to the ADR scheme, so that the proposed optimization algorithm does not cause any substantial computation overhead, neither in the end nodes nor in the NS.…”

“…The challenge of conserving energy in LoRaWAN networks is a QoS in the ADR algorithms in [ 20 , 46 , 47 , 48 , 49 , 50 , 51 ]. In [ 20 ] they first optimize every end node to minimise the ToA, which is determined by its distance from the gateway, searching for optimal gateway location ensuring all nodes are connected.…”

“…The model can be enhanced to improve the packet loss ratio in the network and energy consumption. In [ 50 ] they optimise the ADR scheme using SF and CF to maximise the success probability while fixing BW and CR to compute ToA, improving the DER, thus conserving energy. Out of all these schemes whose objective is energy efficiency, the most promising solution in our opinion, is [ 51 ] The technique improves the network performance by assigning the SFs to end nodes under the constraint for the average energy consumption of each end node.…”

“…In [ 50 ] the authors develop an approach that improves the QoS of the LoRaWAN network by reducing data collisions and energy consumption, and increasing the DER. They use the Mixed Integer Linear Programming (MILP) optimization method to generate optimum settings for SF and Carrier Frequecy (CF) parameters.…”