Integration of ESP32-Based Wireless Sensor Network with 5G Connectivity for Real-Time Environmental Monitoring
Keywords:
Keywords: Wireless Sensor Network, ESP32, 5G Network, Environmental Monitoring, Internet of Things.Abstract
The development of the Internet of Things (IoT) has driven the use of Wireless Sensor Networks (WSN) for environmental monitoring. However, conventional WSNs still face limitations in terms of latency, reliability, and network scalability, especially for real-time applications. 5G technology offers low latency, high throughput, and massive connectivity, which has the potential to improve the performance of WSN-based environmental monitoring systems. This study aims to design and evaluate the integration of ESP32-based WSN with 5G connectivity in supporting real-time environmental monitoring systems, particularly in terms of network performance, energy efficiency, and monitoring system performance. This study uses an experimental method with a quantitative study approach. The system is designed in the form of a prototype consisting of an ESP32 sensor node, a 5G gateway, and a web-based monitoring server. Testing was conducted by measuring end-to-end latency, throughput, packet loss, sensor node energy consumption, and monitoring system response parameters. The test results showed that the system was able to transmit sensor data stably with low latency and consistent throughput through the 5G network. The packet loss rate obtained was relatively low, while the sensor node energy consumption remained efficient thanks to the application of a deep sleep mechanism. The web-based monitoring system successfully displays environmental data in real time and provides early warning notifications. The integration of ESP32-based WSN with 5G connectivity has proven effective in improving the performance and reliability of real-time environmental monitoring systems. This system fulfills the research objectives and warrants further development, especially in subsequent studies that examine network scalability, energy consumption optimization, and the integration of artificial intelligence-based smart analytics.
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