A hybrid network architecture by integrating both 2.4 GHz network and Sub-GHz network and its application in field environment surveillance system

Zhao, Honggang; Oula, Hageliezi; Zhang, Yuheng; Shi, Chuang; Chen, Xiaoyu; Jia, Yongyi; De-zhi, Niu

doi:10.1016/j.micpro.2022.104541

Cited by 1 publication

(1 citation statement)

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“…The system's inception is marked by the deployment of diverse sensor modules, each endowed with a unique measurement capability, encompassing functions such as temperature and humidity measurement via the DHT11 sensor, attitude determination through the MPU6050 sensor, infrared detection facilitated by the HC-SR501 sensor, and sound detection executed by the MAX4468 sensor. Subsequently, the system elegantly incorporates the ATK-LORA-01-V3.0 LoRa communication module, a wireless serial module distinguished by its compact dimensions and energy-e cient operation (Zhao et al, 2022). This module is forti ed with the sx1278 spread spectrum chip, characterized by its operation within the 410MHz-441MHz frequency range, replete with 32 channels.…”

Section: Scalability and Sustainabilitymentioning

confidence: 99%

The Impact of IoT and Sensor Integration on Real-Time Weather Monitoring Systems: A Systematic Review

Selvam,

Al-Humairi

2023

Preprint

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The observation of weather and its impact on human life is undeniable. Weather observations are essential for various sectors, including agriculture, transportation, aviation, and disaster management. Therefore, the collection of data on weather variations is crucial. However, the transient nature of weather variations makes it difficult to collect accurate and real-time data. To overcome this challenge, researchers have developed smart real-time weather monitoring systems integrated with mobile applications. These systems utilize various frameworks, technologies, and techniques, including the Internet of Things (IoT), Arduino, machine learning algorithms, deep learning algorithms, and sensors that measure temperature, humidity, wind speed, and wind direction. These systems provide real-time data on weather conditions, enabling individuals to make informed decisions. The primary objective of this review paper is to critically examine and elaborate on multiple research studies that focus on integrating smart real-time weather monitoring systems with mobile applications. The aim is to understand the techniques, methodologies, and technologies employed in designing and developing such systems. One of the key findings of this review is that integrating smart real-time weather monitoring systems with mobile applications has significant potential to improve weather forecasting and monitoring. For instance, machine learning algorithms can analyze sensor data to identify patterns and accurately predict future weather conditions. Additionally, integrating cloud computing technology in these systems allows for secure data storage, making it accessible to validated individuals from anywhere. In conclusion, integrating smart real-time weather monitoring systems with mobile applications has enormous potential in improving weather forecasting and monitoring. Various frameworks, technologies, and techniques have enabled researchers to develop sophisticated systems that collect accurate and real-time data on weather conditions. These systems significantly affect various sectors, including agriculture, transportation, aviation, and disaster management.

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Section: Scalability and Sustainabilitymentioning

confidence: 99%