LLM-enabled adaptive scheduling in IoT sensing for optimized network performance.

The use of numerous sensors on edge devices, combined with the emergence of AI techniques, makes the IoT environment more intelligent and interactive. The resulting paradigm encompasses device-centric systems that operate instantly and remotely with zero clicks. However, with these advantages, many functional challenges affect remote sensing, including incomplete data, communication delay, lack of context awareness, and dynamically switching topology. To address these challenges, we have proposed a novel scheme, "LLM-Enabled Adaptive Scheduling in IoT Sensing for Optimized Network Performance (LLM-AS)." This scheme uses LLM to adjust the system's sensing to avoid redundant and useless data sending and enhance decision-making for optimized network resources. First, LLM-AS is trained with a defined data set for different parameters, such as packet loss trends, time-based fluctuations, event triggers, network failure patterns, and congestion signals with contextual decisions. Then, this scheme is deployed in a dynamic remote monitoring system for learning and updating task descriptions to utilize the feedback for future decisions and enhance the system performance. Evaluation of LLM-AS on various parameters using the CASAS dataset shows that the optimization functions of LLM are useful and make the IoT more usable. The LLM-AS optimization function confirms an improvement of 57.8% to 60% in MTP, a decrease of 26% to 60% in median delay, and an optimized energy solution with a confidence interval of 95% and a very small error margin. It also indicates that the precision score is about 0.86, the recall score is about 0.82, and the RMSE is about 0.21; all these values suggest high separability for varying conditions of IoT systems in dynamically changing situations.