Search or ask a question

How does the Arduino platform support communication with the LoRa module?

Computer science

Enhanced Data Rates for GSM Evolution

Microcontroller

Best insight from top research papers

The Arduino platform facilitates communication with the LoRa module by enabling the design and implementation of testbeds for LoRa connectivity. Additionally, the Arduino Portenta H7 microcontroller board, equipped with embedded sensors, can be integrated with LoRa technology to create a mesh network, expanding its applications in distributed machine learning tasks. In the context of precision beekeeping, an IoT application utilizes Arduino microcontrollers with LoRa shields to establish wireless sensor networks for monitoring hive variables, showcasing successful communication between master and slave units. Moreover, for real-time remote monitoring systems measuring water depth and velocity, the Arduino Uno R3, in conjunction with LoRa SX1278 modules, processes data from sensors like the YS-F201 Hall-effect water flow sensor and HC-SR04 ultrasonic sensor.

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Proceedings Article•DOI Tributary Water Depth and Velocity Remote Monitoring System using Arduino and LoRa Ammar Azizi Jasni, Yasser Asrul Ahmad, Teddy Surya Gunawan, Mashkuri Yaacob, Nanang Ismail, Abdul Wasik - Show less +5 more 28 Jul 2022 1 Citations	The Arduino Uno R3 processes data and facilitates communication with the LoRa SX1278 module for remote monitoring of tributary water depth and velocity in real-time.
Open access•Proceedings Article•DOI Designing a Double LoRa Connectivity for the Arduino Portenta H7 Dan Pino, Felix Freitag, Mennan Selimi - Show less +2 more 11 Jul 2022	The Arduino Portenta H7 integrates a new LoRa connectivity design, enabling it to join a LoRa mesh network, expanding its applications for distributed machine learning at the edge.
Open access•Proceedings Article•DOI The Applicability of Arduino Microcontroller with a LoRa Shield as an Element in IoT Primož Podržaj, Marjan Jenko, Luka Selak, Gašper Škulj, Lidija Rihar, Rok Vrabič, Drago Bračun, Tomaž Berlec, Dominik Kozjek - Show less +8 more 27 May 2023	The Arduino microcontroller, paired with a LoRa shield, facilitates communication in IoT applications like precision beekeeping by enabling wireless sensor networks and successful data transmission between master and slave units.
Proceedings Article•DOI A Testbed for LoRa Wireless Communication between IoT devices Amar Rasheed 11 May 2023	The Arduino platform facilitates LoRa communication by creating a testbed for two Arduino systems utilizing the LoRa protocol, enabling long-range, low-power data transmission for IoT applications.
Proceedings Article•DOI A Testbed for LoRa Wireless Communication between IoT devices 11 May 2023	The Arduino platform facilitates communication with LoRa modules by enabling the design and implementation of a testbed for LoRa wireless communication between IoT devices.

My columns

Related Questions

How does the choice of LoRa transmission parameters affect the range and reliability of wireless communication?4 answersThe choice of LoRa transmission parameters significantly impacts the range and reliability of wireless communication. LoRa devices offer adaptability through parameters like gain, spreading factor, and coding rate, crucial for varying distances and environmental conditions. LoRa's transmission parameters include Spreading Factor, Transmission Power, Bandwidth, and Coding Rate, with 12480 possible parameter sets in the latest LoRa-based System on Chip. Studies show that communication quality and distance are notably affected by emission power and air velocity, with a linear relationship between node power consumption and air rate. Additionally, a proposed lightweight transmission-parameter selection scheme using reinforcement learning for LoRaWAN effectively reduces collisions between devices, enhancing communication performance and reliability.

What are the current advancements in solar-powered IoT devices using Lora technology?5 answersCurrent advancements in solar-powered IoT devices utilizing LoRa technology include the integration of photovoltaic panel parameter monitoring applications into IoT systems. These systems can record various sensor data such as PV panel current, voltage, illuminance level, temperature, atmospheric pressure, ambient temperature, and humidity over long distances using the LoRa Wide-Area Network protocol. Additionally, there are low-cost, low-power remote condition monitoring solutions for large-scale solar PV systems that utilize ESP-NOW and LoRaWAN wireless communication protocols for data exchanges. These solutions operate in self-power mode, with each IoT node capable of monitoring multiple channels of voltage, current, and temperature of the PV panels. The hardware and software configurations have been optimized to ensure accuracy, reliability, and energy efficiency in monitoring and maintaining solar PV systems.

How does the Arduino microcontroller contribute to the development and implementation of IoT projects?4 answersThe Arduino microcontroller plays a crucial role in the development and implementation of IoT projects. It provides intelligent and scalable characteristics, allowing it to control endpoint devices and facilitate interconnections in IoT systems. The microcontroller's embedded intelligence enables logical circuit control of target devices, while its scalability allows for handling complex problems. This capability is particularly important in industrial implementations, where the microcontroller serves as an intermediate device for networking communication and data logic control. Additionally, the Arduino microcontroller is used in IoT courses to teach students about designing, building, and testing IoT solutions. Its adoption, combined with extensive experimentation, encourages active participation and facilitates the retention of concepts learned. The microcontroller's low-cost and low-power features make it suitable for various applications, including groundwater monitoring and secure data transmission. Overall, the Arduino microcontroller significantly contributes to the success and advancement of IoT projects.

LoRaWAN-based Arduino: Smart Load Control for Home Energy Efficiency?5 answersLoRaWAN-based Arduino technology has been developed for smart load control in order to achieve home energy efficiency. The system measures and controls the energy consumed by electrical loads in households, office buildings, hotels, hospitals, etc. by monitoring various electrical parameters in real time and sending the information to the cloud. Additionally, an algorithm called EE-LoRa has been proposed to improve the energy efficiency of LoRaWAN networks with multiple gateways through spreading factor selection and power control. Another approach focuses on efficient allocation of wireless resources in LoRa networks, optimizing the distribution of end-devices on spreading factors to achieve load balancing and transmission power control for overall system energy efficiency. Furthermore, a LoRaWAN-based smart room monitoring solution has been developed to monitor room environmental parameters and implement automatic load control, resulting in energy cost savings and reduced greenhouse gas emissions.

Was ist LoRa adaption?5 answersLoRa adaptation refers to the process of adjusting the parameters of LoRa technology in order to optimize its performance in different scenarios. This includes varying the spreading factor, transmission power, and coding rate. The goal of LoRa adaptation is to ensure good transmission quality, improve network capacity, and reduce energy consumption. Different approaches to LoRa adaptation have been proposed, such as Enhanced Link Adaptation (ELA) which considers network load and radio environment to optimize configuration. Another approach involves increasing the number of gateways to maximize network capacity. Additionally, the joint Multi-Gateway ELA (MG j ELA) strategy exploits SF orthogonality, spatial reuse of communications, and optimized gateway capacity to improve transmission quality. Overall, LoRa adaptation aims to enhance the performance and efficiency of LoRa networks in various deployment scenarios.

What is LoRa?5 answersLoRa is a wireless communication technology that is characterized by low power consumption and long-range capabilities. It uses chirp spread spectrum modulation with different spreading factors to prioritize long-range transmission while maintaining high detecting sensitivity. LoRa operates at lower frequencies than technologies like Wi-Fi and Bluetooth, allowing for longer wireless links. It is widely used in various applications such as smart metering, factory monitoring, and providing sensor information for disaster alerts. LoRaWAN is the protocol used to create LoRa-based networks. It is a promising technology for Low Power Wide Area Networks (LPWAN) and is gaining traction in the telecom industry.

See what other people are reading

How has the integration of artificial intelligence into cybersecurity measures impacted job growth in the industry?

The integration of artificial intelligence (AI) into cybersecurity measures has significantly impacted job growth in the industry. AI has enabled organizations to systematize and enhance outdated safety procedures, providing more effective threat detection, response capabilities, and vulnerability management. Additionally, AI technologies like machine learning have led to new levels of data analysis and detecting unusual behavior across networks, rapidly increasing security response times. AI-based solutions offer robust cyber defense capabilities, identifying various cyber threats such as malware attacks, network intrusions, phishing, and data breaches. As AI continues to evolve and address complex cybersecurity challenges, the demand for skilled professionals in AI and cybersecurity fields is expected to grow, creating new job opportunities and roles within the industry.What is filter method in feature selection?

A filter method in feature selection is a technique used to identify the most relevant features in a dataset before building a prediction model. These methods aim to enhance the performance of learning algorithms by reducing the dimensionality of the problem and selecting informative subsets of features. Various filter methods have been proposed in research literature. For instance, one study introduces a filter method called Joint Mutual Information with Class relevance (JoMIC), which combines Joint Mutual Information (JMI) and Mutual Information (MI) to select features highly relevant to a given class but non-redundant with other selected features. Another study proposes a hybrid filter-based and genetic feature selection algorithm for sentiment analysis, which outperforms traditional approaches in terms of classification accuracy, particularly in Arabic sentiment analysis.What is the definition of an urban 'slum' household according to the UN Habitat?

According to the UN-Habitat, an urban 'slum' household is defined as a human settlement with inadequate access to safe water, sanitation, and infrastructure, poor housing quality, overcrowding, and insecure residential status. These settlements are typically characterized by substandard housing, squalor, and are often found in urban regions of developing and undeveloped parts of the world. Slums are a result of rapid urbanization, poverty, high unemployment, and other factors like poor planning and economic stagnation. The UN-Habitat estimates that around one billion people currently live in slums, a number expected to rise significantly in the coming years if current trends persist. These informal settlements are crucial policy concerns globally, with interventions aimed at addressing the challenges of urban poverty and inequality.What are the advantages and disadvantages of using symbolic expression in genetic programming compared to traditional methods?

Symbolic expression in genetic programming offers advantages such as interpretable machine learning, effective relationship discovery, and the ability to handle high-dimensional problems efficiently. However, traditional genetic programming methods often struggle with real-world high-dimensional problems due to their stochastic evolutionary nature and poor demonstration as the problem scale increases. To address this limitation, a differentiable approach called DGP has been proposed, which constructs GP trees in a continuous manner, allowing for gradient-based optimization and better performance in high-dimensional symbolic regression tasks. Additionally, Control Variable Genetic Programming (CVGP) introduces a customized experiment design approach to expedite symbolic expression discovery over many independent variables, outperforming several baselines in learning complex expressions involving multiple variables.Is precordial lead placement has the capability to detect coronary artery disease?

Precordial lead placement plays a significant role in detecting coronary artery disease (CAD). Studies have shown that utilizing innovative techniques like the SafOne precordial lead ECG can aid in accurate CAD detection by reducing errors in lead placement and improving efficiency. Additionally, research suggests that the use of ultrashort one-lead ECG recordings, including noisy inputs from smartwatches, in combination with advanced algorithms like autoencoder neural networks and Support Vector Machines, can effectively detect CAD. Moreover, investigations focusing on the acoustic approach for CAD screening highlight the importance of ideal microphone placement on the chest to enhance accuracy in CAD detection, with specific positions above the apex of the heart showing promising results. Overall, proper precordial lead placement techniques are crucial for enhancing the detection of CAD, especially when combined with innovative technologies and analysis methods.What are the fundamental concepts and techniques used in machine learning?

Machine learning (ML) encompasses a variety of tools and structures to extract insights from data, covering both supervised and unsupervised learning techniques. ML involves learning from datasets, understanding core concepts like features, labels, algorithms, workflow, and model evaluation metrics. Fundamental concepts in ML include empirical risk minimization, regularization, probably approximately correct learning, and the maximum likelihood principle, which are crucial for controlling errors and understanding the learning process. Additionally, ML techniques are extensively applied in various domains due to their ability to handle complex problems and make data-driven decisions, with deep learning being a vibrant subfield that has gained significant attention. This comprehensive overview equips individuals with a broad understanding of ML's foundational principles and its diverse applications.What is the relationship between Platt scaling and isotonic regression in histology?

Platt scaling and isotonic regression are both methods used in different contexts. Platt scaling, as discussed in Context_1 and Context_2, is an online post-hoc calibration technique that combines logistic regression with Platt scaling to adapt between different settings and enhance calibration. On the other hand, isotonic regression, as described in Context_3, focuses on enhancing trend tests by imposing a monotonicity constraint on dose-response functions. In the context of histology, specifically in cutaneous melanoma, the study in Context_4 found that the presence of histological regression did not play a prognostic role in nodal involvement or survival. Therefore, while Platt scaling deals with calibration in predictive modeling, isotonic regression is more focused on enhancing trend tests in dose-response analysis, showing their distinct applications in different domains.What is the difference between framework and a model?

A framework is a structured outline or support system that provides a foundation for developing models, while a model is a representation of a system or process used to understand and predict its behavior. In the context of environmental systems, modelling frameworks offer pre-built functionality for creating models efficiently. On the other hand, models are specific representations that mimic real-world processes, such as flood forecasting models used to predict high water levels. Frameworks focus on alleviating limitations in representing different types of information within a system and optimizing hardware utilization for model development. In contrast, models are the actual simulations that utilize the framework to represent and analyze specific scenarios or phenomena within a system.How much scans are involved in drone mapping of canola seed production?

Drone mapping for canola seed production involves multiple scans at different growth stages. The process includes capturing ultrahigh resolution RGB images using a digital camera mounted on a UAV platform to assess plant growth traits. Additionally, UAV multi-spectral images are acquired at various heights to detect nutrient deficiencies and optimize fertilizer application. These scans are crucial for evaluating seedling stand count and seeding performance, with regression models based on shape features extracted from segmented plant objects. The use of drones in pest management for rapeseed fields also involves scans for pest imaging and identification, as well as precision pesticide spraying. Overall, drone technology offers a non-invasive and effective approach for monitoring canola crops through multiple scanning processes at different growth stages.What is the function of architecture?

Architecture serves various functions depending on the context. In the realm of cellular metabolism, Architect, an automated pipeline, reconstructs metabolic models from protein sequences, enhancing precision and recall in enzyme annotations and model quality. Polymer architecture significantly influences the solution, rheological, and mechanical behavior of polymers, with hyperbranched polystyrenes exhibiting unique properties compared to linear polymers. Architectural design impacts the physical environment's effects on individuals, aiming to reduce stress and enhance productivity in settings like learning environments, hospitals, and operating rooms. Furthermore, the architectural organization of gap junction-connected cells, whether in tree or ring structures, influences their electrical properties, highlighting the importance of architecture in understanding cellular connectivity. Overall, architecture plays a crucial role in shaping functionality and experiences across various disciplines.How hands on experimentl skills in students?

Hands-on laboratory experiences play a crucial role in developing students' experimental skills. Engaging in hands-on experiments allows students to apply theoretical knowledge practically, fostering problem-solving and critical-thinking abilities. Through experiments involving chemical reactions, students observe changes in texture, temperature, color, and gas evolution, enhancing their understanding of scientific concepts. Furthermore, students conducting experiments on additive color mixing or electric circuits gain insights into fundamental physics laws and concepts. These experiences not only build science content knowledge but also develop students' skills in designing experiments, analyzing data, and drawing conclusions, preparing them for future educational and career opportunities requiring practical laboratory skills.