Accuracy Assessment and Cross-Validation of LPWAN Propagation Models in Urban Scenarios

Štůsek, Martin; Moltchanov, Dmitri; Mašek, Pavel; Mikhaylov, Konstantin; Zeman, Otto; Roubicek, Martin; Koucheryavy, Yevgeni; Hošek, Jiří

doi:10.1109/access.2020.3016042

Cited by 22 publications

(21 citation statements)

References 17 publications

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“…where By operating with high SF values, LoRa transmitters increase their communication ranges [20]; robustness against channel impairments and interference from third systems, along with frequency selectivity and the Doppler effect, are also enhanced by increasing SFs. On the other hand, this results in a low data rate and long ToA, which are the main drawbacks of operating with high SFs.…”

Section: Technology Background: Lora and Lorawanmentioning

confidence: 99%

LoRaWANSim: A Flexible Simulator for LoRaWAN Networks

Marini

Mikhaylov²,

Pasolini³

et al. 2021

Sensors

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Among the low power wide area network communication protocols for large scale Internet of Things, LoRaWAN is considered one of the most promising, owing to its flexibility and energy-saving capabilities. For these reasons, during recent years, the scientific community has invested efforts into assessing the fundamental performance limits and understanding the trade-offs between the parameters and performance of LoRaWAN communication for different application scenarios. However, this task cannot be effectively accomplished utilizing only analytical methods, and precise network simulators are needed. To that end, this paper presents LoRaWANSim, a LoRaWAN simulator implemented in MATLAB, developed to characterize the behavior of LoRaWAN networks, accounting for physical, medium access control and network aspects. In particular, since many simulators described in the literature are deployed for specific research purposes, they are usually oversimplified and hold a number of assumptions affecting the accuracy of their results. In contrast, our simulator has been developed for the sake of completeness and it is oriented towards an accurate representation of the LoRaWAN at the different layers. After a detailed description of the simulator, we report a validation of the simulator itself and we then conclude by presenting some results of its use revealing notable and non-intuitive trade-offs present in LoRaWAN. Such simulator will be made available via open access to the research community.

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Section: Technology Background: Lora and Lorawanmentioning

confidence: 99%

LoRaWANSim: A Flexible Simulator for LoRaWAN Networks

Marini

Mikhaylov²,

Pasolini³

et al. 2021

Sensors

Self Cite

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“…Stusek et al [112] performed enhancements to current propagation models for LPWAN technologies, such as Ericsson Urban for NB-IoT and LoRAWAN, and 3GPP for SigFox. To achieve this, the authors adopted a fine-tuning strategy for the current models to improve the urban environment's planning policies.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Long-Range Wide-Area Network Technology Optimizations

et al. 2021

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Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN's performance. We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future.

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“…In the first phase of this study, our joint effort with Vodafone Czech Republic 1 delivers (i) an extensive measurement campaign for three leading LPWAN technologies publicly available today (wherein the research activities with Vodafone allowed us to adjust the network configuration as well as obtain detailed information while performing the measurement campaign), across the city of Brno located in the Czech Republic [16]- [19]. Using these measurements and utilizing the knowledge of the BS locations for all the technologies in question, (ii) we construct the baseline coverage map and derive a reference model used further on to characterize the prediction accuracy of the propagation models as an alternative to the interpolation methods.…”

Section: B Key Contributionsmentioning

confidence: 99%

“…Therefore, at the second step, we select a validated PL model representing the closest match to our empirical model. During our analysis [35] of the propagation models available for LPWAN technologies, e.g., Okumura-Hata, Ericsson 9999, Stanford University Interim, and COST (European Cooperation in Science and Technology) 231 Walfisch-Ikegami, we found out that none of them provide sufficient accuracy. Notably, the selected models in their basic form significantly overestimate the path loss values for all the considered LPWAN technologies.…”

Section: B Reference Modelmentioning

confidence: 99%

LPWAN Coverage Assessment Planning Without Explicit Knowledge of Base Station Locations

Štůsek

Moltchanov

Mašek

et al. 2022

IEEE Internet Things J.

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An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power widearea network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, NB-IoT, Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. By contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as Natural-neighbor, Linear interpolation, or Inverse-Distance Weighting, can achieve comparable (and occasionally even better) accuracy levels.

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Accuracy Assessment and Cross-Validation of LPWAN Propagation Models in Urban Scenarios

Abstract: For the research, the infrastructure of the SIX Center was used. This article is based upon support of international mobility project MeMoV, No. CZ.

Cited by 22 publications

References 17 publications

LoRaWANSim: A Flexible Simulator for LoRaWAN Networks

LoRaWANSim: A Flexible Simulator for LoRaWAN Networks

A Survey on Long-Range Wide-Area Network Technology Optimizations

LPWAN Coverage Assessment Planning Without Explicit Knowledge of Base Station Locations

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