Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 19 September 2019 / Approved: 20 September 2019 / Online: 20 September 2019 (19:20:36 CEST)
Version 2 : Received: 5 November 2019 / Approved: 6 November 2019 / Online: 6 November 2019 (03:57:34 CET)

How to cite: Sallum, E.; Pereira, N.; Alves, M.; Santos, M. Improving Quality-of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management. Preprints 2019, 2019090243 (doi: 10.20944/preprints201909.0243.v1). Sallum, E.; Pereira, N.; Alves, M.; Santos, M. Improving Quality-of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management. Preprints 2019, 2019090243 (doi: 10.20944/preprints201909.0243.v1).

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 which 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, considering realistic scenarios. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 30% in DER, and a number of collisions 17 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 10.5% and 4% of DER, and a number of collisions 13.5 and 7.5 times smaller than equal-distribution and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption 3.6 and 2.74 times lower than the equal-distribution and random dynamic allocation policies, respectively. Furthermore, we approach the practical aspects on how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Subject Areas

internet of things (IoT); low-power wide area network (LPWAN); mixed integer linear programming (milp); lorawan; lora simulator (lorasim); open-source; optimization; quality-of-service (qos); data extraction rate; packet collision rate; energy consumption; energy efficiency; network performance; protocol overhead; performance evaluation; performance improvement

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