Optimizing Wireless Sensor Networks in Environmental Monitoring Applications with IoT Technology in Indonesia
Keywords:
Wireless Sensor Networks, Internet of Things, Particle Swarm Optimization, Energy Efficiency, Environmental Monitoring.Abstract
Advances in Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies have brought significant changes to global environmental monitoring systems. However, the application of these technologies in Indonesia still faces obstacles in terms of energy efficiency, communication reliability, and network infrastructure limitations, especially in areas with complex geographical conditions. This research aims to optimize WSN performance in IoT-based environmental monitoring applications with a focus on improving energy efficiency, data transmission stability, and network lifetime to suit the characteristics of Indonesia's tropical environment. The approach used is a quantitative experimental study based on simulation and field testing. The system is designed using a three-layer architecture (sensor, network, and application) integrated with the IoT ThingsBoard platform through the MQTT protocol. Network optimization is performed using the Particle Swarm Optimization (PSO) algorithm to determine communication paths and adaptive cluster head selection. The results of the study show an increase in energy efficiency of up to 28%, an increase in Packet Delivery Ratio to 96.1%, and an extension of network lifetime to 142 hours. The average latency decreased to 1.12 seconds, and the throughput increased to 27.3 kbps. The application of the PSO algorithm has proven to be effective in improving the performance of IoT-based wireless sensor networks for environmental monitoring. The resulting model can serve as a basis for the development of a Smart Environmental Monitoring system in Indonesia and open up opportunities for further research into the application of artificial intelligence for more efficient and adaptive network optimization.
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