A study of LoRa low power and wide area network technology

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.

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“…With CSS, each LoRa symbol is coded with a spreading code of 2 SF chips. Then, it takes 2 SF chips (SF = SFbits × 2 SF ) to spread a symbol [27].…”

Section: Lora Physical Layermentioning

confidence: 99%

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

Sallum

Pereira

Alves

et al. 2020

JSAN

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“…These parameters influence the effective bitrate of the modulation, its resistance to interference noise, and its ease of decoding. Noreen et al [42] provided in depth analysis of the impact of these three parameters on the data rate and time on air, and Phung et al [43] provided an in-depth analysis and assessment of LoRaWAN functional components: its capabilities (total traffic load, packet delivery quality) versus its efficiency (collision and frequency usage).…”

Section: Loramentioning

confidence: 99%

Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

Caño

Sánchez-Sutil

2020

Energies

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This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffic, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.

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“…One of the most common short‐range wireless technologies with less power consumption compared to others including the basic Bluetooth is Bluetooth Low Energy (BLE). Bluetooth and BLE are used for different purposes (Noreen, Bounceur, & Clavier, ). Conventional Bluetooth can handle almost all the variety of data, but it consumes more power and cost.…”

Section: Challenges/issues With Current Solutionsmentioning

confidence: 99%

“…), but the most common ones are LoRa and SigFox. They successfully propose wide area connectivity from a few to tens of kilometers for low data rate, low power and low throughput applications (Noreen et al, ). They facilitate a large scale of connectivity, especially in large scale developments such as smart city applications.…”

Section: Challenges/issues With Current Solutionsmentioning

confidence: 99%

Internet of Things and data mining: From applications to techniques and systems

Gaber

Aneiba

Basurra

et al. 2018

WIREs Data Min & Knowl

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The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing, or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Health Care Application Areas > Industry Specific Applications

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A study of LoRa low power and wide area network technology

Cited by 200 publications

References 10 publications

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

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

Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

Internet of Things and data mining: From applications to techniques and systems

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