Experimental vs. simulation analysis of LoRa for vehicular communications

Ortiz, Fernando Molano; Almeida, Thales Teixeira de; Ferreira, Adésio; Costa, Luís Henrique M. K.

doi:10.1016/j.comcom.2020.06.006

Cited by 27 publications

(17 citation statements)

References 14 publications

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“…Many articles focus on experimental and simulation comparison [16][17][18], proving that experiment and simulation are consistent. The sameness is the primary requirement in the simulation, where any difference may cause fatal results.…”

Section: Introductionmentioning

confidence: 96%

Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips

Jacko

Beres

Kovacova

et al. 2022

Sensors

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The article describes the implementation of IoT technology in the teaching of microprocessor technology. The method presented in the article combines the reality and virtualization of the microprocessor technology laboratory. A created IoT monitoring device monitors the students’ microcontroller pins and sends the data to the server to which the teacher is connected via the control application. The teacher has the opportunity to monitor the development of tasks and student code of the program, where the functionality of these tasks can be verified. Thanks to the IoT remote laboratory implementation, students’ tasks during the lesson were improved. As many as 53% (n = 8) of those students who could improve their results achieved an improvement of one or up to two tasks during class. Before the IoT remote laboratory application, up to 30% (n = 6) of students could not solve any task and only 25% (n = 5) solved two tasks (full number of tasks) during the class. Before implementation, 45% (n = 9) solved one problem. After applying the IoT remote laboratory, these numbers increased significantly and up to 50% (n = 10) of students solved the full number of tasks. In contrast, only 10% (n = 2) of students did not solve any task.

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Section: Introductionmentioning

confidence: 96%

Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips

Jacko

Beres

Kovacova

et al. 2022

Sensors

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“…Though this debate still rages, several researchers have shown that simulation results are often at par with real-world tests. For instance, using LoRa, the authors in [20] compared simulation results with real world test for intervehicle communication. They used NS3 as a simulation platform and an Arduino UNO + Dragino LoRa module for the real-world tests, while Propagation loss, coverage Packet Inter-reception (PIR), Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) level were used as benchmark metrics.…”

Section: ) Wireless Communication Network For Water Monitoringmentioning

confidence: 99%

“…In a similar work, Hassan [21] also compared the efficacy of simulation results (from Radio Mobile simulator) with real-world tests (using micro controllers + LoRa modules) when using LoRa as a bridge for Wi-Fi. Unlike [20], [21] did not give a side-byside comparison of simulated vs. real-world results for each metric considered but concluded that the simulator performed well. [22] set up seven pairs of XBee modules and compared communication performance using both the 800/900MHz and 2.4GHz frequencies.…”

Section: ) Wireless Communication Network For Water Monitoringmentioning

confidence: 99%

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

et al. 2022

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Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting. To this end this study proposes a network architecture to collect data on water parameters in real-time and use machine learning (ML) tools to automatically determine suitability of water samples for drinking and irrigation purposes. The developed monitoring network is based on LoRa and takes the land topology into consideration. Results of simulations done in Radio Mobile revealed a partial mesh network topology as the most adequate. Due to the absence of large and open datasets on drinking and irrigation water, datasets usable for training ML models were developed. Three ML models -Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) were considered for the water classification and results obtained showed that LR performed best for drinking water, while SVM was better suited for irrigation water. Recursive feature elimination was then combined with the three ML models to reveal which of the water parameters had the greatest influence on the classification accuracies of the respective model.

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“…Numerous studies have been provided to understand the technical limits of LoRa technology. These works have focused on different layers of LoRa including the application layer [ 19 ], transport layer [ 20 ], network layer [ 21 ], data link layer [ 22 ] as well as the physical layer [ 23 ]. The first attempts started by investigating the secret of the LoRa modulation technique based on the Chirp Spreading Spectrum (CSS) scheme as reviewed in [ 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

Yazid

Guerrero-González

Ez‐zazi

et al. 2022

Sensors

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Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions.

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Experimental vs. simulation analysis of LoRa for vehicular communications

Cited by 27 publications

References 14 publications

Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips

Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

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