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LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 9544

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Special Issue Editors

Dr. Yuanqing Zheng

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Guest Editor

Department of Computing, The Hong Kong Polytechnic, PQ721, Hong Kong, China
Interests: wireless networking and mobile computing; acoustic and RF sensing; Internet of Things (IoT)

Dr. Jiliang Wang

E-Mail Website
Guest Editor

School of Software, Tsinghua University, Beijing 100084, China
Interests: Internet of Things; LPWAN; LoRa; wireless sensing

Dr. Wan Du

E-Mail Website
Guest Editor

Department of Computer Science and Engineering, School of Engineering, The University of California, Merced, CA, USA
Interests: cyber physical systems; Internet of Things; networked embedded systems; wireless and mobile networking

Special Issue Information

Dear Colleagues,

LoRa and LoRaWAN are promising low-power wide-area networking technologies for inter-connecting billions of low-power Internet of Things (IoT) nodes. We envision that an increasing number of IoT nodes will be deployed and connected to the Internet via LoRa and LoRaWAN to enable various innovative applications such as smart metering, smart transportation, machine-to-machine communication, and environment monitoring. We face great practical challenges and research opportunities in the design, implementation, and evaluation of LoRa and LoRaWAN technologies and their applications and system developments. This Special Issue calls for manuscripts describing the latest advances in LoRa and LoRaWAN.

Potential topics include, but are not limited to, the following:

Novel physical layer design and optimization for LoRa;
Novel link and network layer design and implementation for LoRaWAN;
Efficient data aggregation in LoRa-based sensor networks;
Long-range backscatter and sensing systems;
Joint communication and sensing with LoRa signals;
Co-existence and co-operation with other wireless technologies in ISM bands;
Cross-technology communication with LoRa;
Machine learning and artificial intelligence application in LoRa and LoRaWAN;
Security aspects of LoRa and LoRaWAN.

Dr. Yuanqing Zheng
Dr. Jiliang Wang
Dr. Wan Du
Guest Editors

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Keywords

LoRa
LoRaWAN
LPWAN
long-range low-power communication
IoT technology

Published Papers (5 papers)

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Research

14 pages, 2673 KiB

Open AccessArticle

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

by Salaheddin Hosseinzadeh, Moses Ashawa, Nsikak Owoh, Hadi Larijani and Krystyna Curtis

Sensors 2024, 24(3), 860; https://doi.org/10.3390/s24030860 - 29 Jan 2024

Cited by 1 | Viewed by 901

Abstract

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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23 pages, 1323 KiB

Open AccessArticle

Leveraging Larger AES Keys in LoRaWAN: A Practical Evaluation of Energy and Time Costs

by Phithak Thaenkaew, Bruno Quoitin and Ahmed Meddahi

Sensors 2023, 23(22), 9172; https://doi.org/10.3390/s23229172 - 14 Nov 2023

Cited by 1 | Viewed by 1252

Abstract

Internet of Things (IoT) devices increasingly contribute to critical infrastructures, necessitating robust security measures. LoRaWAN, a low-power IoT network, employs the Advanced Encryption Standard (AES) with a 128-bit key for encryption and integrity, balancing efficiency and security. As computational capabilities of devices advance and recommendations for stronger encryption, such as AES-256, emerge, the implications of using longer AES keys (192 and 256 bits) on LoRaWAN devices’ energy consumption and processing time become crucial. Despite the significance of the topic, there is a lack of research on the implications of using larger AES keys in real-world LoRaWAN settings. To address this gap, we perform extensive tests in a real-world LoRaWAN environment, modifying the source code of both a LoRaWAN end device and open-source server stack to incorporate larger AES keys. Our results show that, while larger AES keys increase both energy consumption and processing time, these increments are minimal compared to the time on air. Specifically, for the maximum payload size we used, when comparing AES-256 to AES-128, the additional computational time and energy are, respectively, 750

m

s

and 236 μJ. However, in terms of time on air costs, these increases represent just 0.2% and 0.13%, respectively. Our observations confirm our intuition that the increased costs correlate to the number of rounds of AES computation. Moreover, we formulate a mathematical model to predict the impact of longer AES keys on processing time, which further supports our empirical findings. These results suggest that implementing longer AES keys in LoRaWAN is a practical solution enhancing its security strength while not significantly impacting energy consumption or processing time. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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21 pages, 717 KiB

Open AccessArticle

Modeling and Optimization of LoRa Networks under Multiple Constraints

by Hui Zhang, Yuxin Song, Maoheng Yang and Qiming Jia

Sensors 2023, 23(18), 7783; https://doi.org/10.3390/s23187783 - 10 Sep 2023

Cited by 2 | Viewed by 1489

Abstract

With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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24 pages, 25948 KiB

Open AccessArticle

Energy-Efficient LoRa Routing for Smart Grids

by Raja Kishore Repuri and John Pradeep Darsy

Sensors 2023, 23(6), 3072; https://doi.org/10.3390/s23063072 - 13 Mar 2023

Cited by 7 | Viewed by 2228

Abstract

Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network’s longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol–cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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17 pages, 802 KiB

Open AccessArticle

Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN

by Matheus Araujo Gava, Helder Roberto Oliveira Rocha, Menno Jan Faber, Marcelo Eduardo Vieira Segatto, Heinrich Wörtche and Jair Adriano Lima Silva

Sensors 2023, 23(3), 1239; https://doi.org/10.3390/s23031239 - 21 Jan 2023

Cited by 12 | Viewed by 2386

Abstract

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (

S F

), bandwidth and transmission power were adjusted to minimize the network’s total energy per useful bit (

E_{b i t}

) and the total data collection time. The importance of the

S F

in the trade-off between (

E_{b i t}

) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of

S F

aiming at reduction of

E_{b i t}

. The usage of large

S F

’s becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per

S F

to avoid discrepancies in the time data collection. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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