Energy-efficient adaptive data rate optimisation scheme for LoRaWAN

Abstract

The Internet of Things (IoT) is a paradigm that has revolutionised wireless network technologies worldwide owing to its low power consumption, low cost of deployment, long-range communication, and its ability to accommodate a substantial number of end devices (EDs). Many industries have adopted the use of IoT to improve decision-making, to function more efficiently, to automate various operations, and to minimise energy usage, thus helping in the reduction of greenhouse gas emissions. Among the IoT technologies, Low Power Wide Area Networks (LPWANs) perform a pivotal function in delivering efficient and scalable connectivity for this ever-expanding wireless communication system. One prominent LPWAN technology, Long Range Wide Area Network (LoRaWAN), has achieved widespread adoption attributable to its operation in the license-free sub-gigahertz frequency band, scalability, low bit rate, and energy efficiency.

LoRaWAN networks consist of low-power end devices connected in a star-of-star topology to a network server (NS) through a gateway (GW), enabling seamless exchange of data connected through an IP-based back-haul connection. The EDs are energy-constrained as they are battery operated and can be static or mobile and can sometimes be deployed in difficult-to-reach places, hostile or hazardous environments, necessitating a long battery life lasting several years. Wireless communication is the major source of energy consumption due to interference and packet collisions during packet transmission. It is essential to limit energy utilisation while maintaining the communication between the end devices and the gateway. This study focuses on enhancing energy efficiency in LoRaWAN networks to extend their network lifetime. An essential aspect of LoRaWAN is the Adaptive Data Rate (ADR) scheme, which optimises Quality of Service (QoS) requirements, battery life of EDs, and overall network performance. ADR manages transmission settings such as transmission power (TP), spreading factor (SF), bandwidth (BW), and coding rate (CR) based on the link budget. By optimising ADR, we can reduce airtime, increase network capacity, and improve energy efficiency.

Following the introduction of the LoRaWAN protocol in 2015 by the LoRa Alliance, there has been an absence of a standardised ADR implementation in the LoRa specification. It does not define the way the network server controls EDs regarding data rate adaptation. This has led to various ADR schemes being proposed by different vendors to accommodate diverse IoT scenarios and QoS requirements, posing challenges to reliability and suitability. A comprehensive literature review of existing ADR schemes highlighted their focus on scalability, throughput, and energy efficiency. The existing ADR schemes use different algorithms with different computational complexities to optimise the data rate, depending on the different goals such as coverage, received signal strength, congestion, capture effect and channel contention. The issue of energy consumption emerged as a major challenge.

To address this challenge, this research study proposed and implemented a novel fuzzy-logic-based adaptive data rate (FL-ADR) scheme for energy-efficient LoRaWAN communication. The impact of multiple GWs on a LoRaWAN network was investigated and the results were incorporated in implementing the LoRaWAN network using a ns-3 based LoRaWAN simulator, a widely used open source simulation platform. The proposed FL-ADR scheme performance was contrasted with other state-of-the-art algorithms. Network performance was monitored by analysing the energy consumption, interference or collision rate, the packets that were lost because the GWs were busy, confirmed packet success rate (CPSR), the uplink packet delivery ratio (UL-PDR) and the energy efficiency of the algorithms. These metrics were evaluated for different data intervals and different network sizes. The simulation results showed that the proposed FL-ADR scheme achieved substantial energy savings of 43% and 14% compared to the standard Semtech-ADR algorithm and the ns-3-ADR algorithm respectively. Although the CPSR and UL-PDR dropped slightly, the FL-ADR algorithm exhibited lower interference/collision rates, confirming its energy efficiency. The FL-ADR managed to efficiently adjust SF and TP despite a trade-off with CPSR and UL-PDR.

Additionally, we developed an SNR-based Spreading Factor Interference Rate controlled Adaptive Data Rate Algorithm, SSFIR-ADR, an ADR algorithm that improves packet delivery ratio by reducing packet collision and managing interference. We implemented multiple static end devices connected to a single gateway to resolve the packet delivery ratio challenge without compromising energy consumption. SSFIR-ADR decreases signal interference by managing the SF allocation to reduce the probability of simultaneous transmissions with the same spreading factor in a particular annulus region in the network using a stochastic approach. The performance was evaluated using three state-of-the-art algorithms. The simulation results demonstrated that our proposed approach exhibits an improved packet delivery ratio and interference rate compared to existing solutions. These significant results contribute to the novelty of the proposed adaptive data rate algorithms.

In conclusion, this research contributes valuable energy-efficient ADR algorithms for LoRaWAN communication, offering a practical and reliable solution to extend network lifetime and enhance overall network performance in IoT deployments.