Cluttered traffic distribution in LoRa LPWAN

Low Power WAN (LPWAN) is a wireless broad area network technology. It is interconnects using only low bandwidth, less power consumption at long range. This technology is operating in unauthorized spectrum [1] which designed for wireless data communication. To have an insight of such long-range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities clutter grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of these sensing nodes follow the built infrastructure around the city of smart buildings. The system is evaluated in terms of connectivity and packet loss ratio

Cluttered traffic distribution in LoRa LPWAN

Low Power WAN (LPWAN) is a wireless broad area network technology. It is interconnects using only low bandwidth, less power consumption at long range. This technology is operating in unauthorized spectrum [1] which designed for wireless data communication. To have an insight of such long-range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities clutter grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of these sensing nodes follow the built infrastructure around the city of smart buildings. The system is evaluated in terms of connectivity and packet loss ratio

Sensor node clutter distribution in LoRa LPWAN

Low Power WAN (LPWAN) is a wireless broad area network technology. It is interconnects using only low bandwidth, less power consumption but at a long range. This new technology is operate in unauthorized spectrum [1] which designed for wireless data communication. In order to have an insight of such long range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities clutter grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of these sensing nodes follow the built infrastructure around the city of smart buildings. The system is evaluated in terms of connectivity and packet loss ratio

Effects of shadowing on LoRa LPWAN Radio links

LoRaWAN is a long-range, low-power, wireless telecommunications method; expected to play a big role for the Internet of Things. End appliances use LoRaWAN through a single wireless hop to communicate with gateways linked to the Internet that function as transparent bridges relaying messages amongst these end-devices and a central network server. This technology youtes a combination of extended range, low power utilization and protected data communication and is gaining significant traction in IoT networks being deployed by wireless network operators. However, no comprehensive evaluation of the technology exists in the open literature. The main intention of this paper is to investigate the effects of shadowing on LoRaWAN links and analyze the performance in terms of packet loss ratio for different physical layer settings. Results indicate large differences in performance when shadowing is taken into consideration upsetting the expected performance tremendously

Gateway sink placement for sensor node grid distribution in LoRa smart city networks

Low Power Wide Area Network (LPWAN) is a type of wireless communication network designed to allow long range communications at a low bit rate among things (connected objects), such as sensors operated on a battery. It is a new technology that operate in unauthorized spectrum [1] which designed for wireless data communication. It is used in Internet of Thing (IoT) applications and M2M communications. It provides multi-year battery lifetime and is intended for sensors and applications that need to transmit only a few information over long distances a few times per hour from different environments. In order to have an insight of such long range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities node grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of the these sensing nodes follow the utilities infrastructure around the city (e.g., street light posts, water and gas delivery pipes,…etc). The system is evaluated in terms of connectivity and packet loss ratio

Gateway sink placement for sensor node grid distribution in lora smart city networks

&lt;span&gt;Low Power Wide Area Network (LPWAN) is a type of wireless communication network designed to allow long range communications at a low bit rate among things (connected objects), such as sensors operated on a battery. It is a new technology that operates in unauthorized spectrum which designed for wireless data communication [1]. It is used in Internet of Thing (IoT) applications and M2M communications. It provides multi-year battery lifetime and is intended for sensors and applications that need to transmit only a few information over long distances a few times per hour from different environments. In order to have an insight of such long range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities node grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of the these sensing nodes follow the utilities infrastructure around the city (e.g., street light posts, water and gas delivery pipes,…etc). The system is evaluated in terms of connectivity and packet loss ratio.&lt;/span&gt;</jats:p

Load forecasting for air conditioning systems using linear regression and artificial neural networks

The increasing demand for energy efficiency in industrial sectors necessitates innovative approaches to optimize energy consumption. This research addresses the challenge of accurately forecasting energy loads in air conditioning systems within the metal printing industry. Traditional forecasting methods often fail to capture industrial settings' complex, dynamic energy demands. This study aims to develop a precise load forecasting model by integrating Linear Regression (LR) and Artificial Neural Networks (ANN). Using real-world data from Kian Joo Can Factory Berhad, the ANN model demonstrated superior performance with a Mean Absolute Percentage Error (MAPE) of 11.44% and a Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 4.214%. These findings suggest significant potential for reducing energy consumption, lowering operational costs, and improving equipment maintenance. Implementing machine learning algorithms in this context underscores their value in enhancing the efficiency, reliability, and cost-effectiveness of Air Handling Units (AHU) in industrial air conditioning systems

A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction

Solar power prediction is crucial for integrating renewable energy into the grid, but current methods often struggle with accuracy due to the limitations of machine learning algorithms. This study aims to enhance prediction accuracy by comparing the performance of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models using datasets from Hebei, China. The main objective is identifying the most effective algorithm for precise solar power forecasting. The methodology involves training both models on historical solar power data and evaluating their performance against the Graph Spatial-Temporal Attention Neural Network (GSTANN) benchmark. The SVM model was selected for its superior metrics, achieving an MAE (Mean Absolute Error) of 0.5587, RMSE of 0.9741, and a training time of 0.0157 seconds. Results show that SVM outperforms GSTANN in 45 and 60- minute intervals, with MAE, MAPE, and RMSE improvements of up to 68.62%, 42.65%, and 69.44%, respectively. These findings suggest that SVM offers a more reliable solution for solar power prediction, providing valuable insights for further model enhancements

Addressing Nurses as Sister or Brother: What Should You Say?

Nurses are the backbone of the health care service. Nurses are a key part of the hospital's ability to ensure the quality of medical care [1]. Nursing is the most prosperous profession in the world. In this endemic situation, the value of the nursing profession can ultimately be understood. Nurses are people who are very close in taking care of the patient. Nurses go through very complicated situations when caring for a patient. The article prompts discussion about addressing nurses as sisters or brothers. What Should we have to Say?</jats:p