Research on Self-Organization and Adaptive Strategy of the Internet of Things Sensor Networks

The aim of this research is to devise and assess a holistic approach for optimizing the performance of the Internet of Things Sensor Network (IoTSN), thereby enabling it to better adapt to diverse and unpredictable environments. Initially, this study delves into the self-organizing network, achieving intelligent nodal cooperation and allowing the network to autonomously reshape its topology. Subsequently, an adaptive approach is incorporated to intelligently modulate the operational modes and task allocations of nodes by continuously monitoring network load, energy usage, and data transfer efficiency. To validate the efficacy of our method, we utilized the SensorScope Dataset, composed of real-time data from various IoT devices, encompassing diverse environmental metrics such as temperature, humidity, and lighting. Experimental findings reveal that, in contrast to conventional strategies, our integrated self-organization and adaptive approach offer substantial benefits in terms of load balancing, energy consumption, and data transfer latency. More specifically, we recorded a 30% drop in average load, a 25% decrease in energy use, and a 20% reduction in average transmission delay. These outcomes strongly support the effectiveness of our comprehensive strategy in boosting IoTSN performance when juxtaposed with alternative methods. Additionally, we offer an in-depth exploration of the experimental data and identify potential areas for refinement and future research avenues, opening the door to fresh perspectives and advancements in IoTSN intelligence and efficiency. This investigation is pivotal in advancing IoT technology and bolstering the adaptability and performance of IoTSN in a wide range of practical scenarios.