LED lights are a good component for managing energy consumption to provide illumination, but an important issue will be to keep their surface clean from dust and pollution. Accordingly, the method of creating an automatic washing approach for LED lights was examined in this study. The study's main purpose was to provide a novel model for AWS of LED street lighting via IoT based on upper and lower bounds simulation and using metaheuristic algorithms such as PSO. In the next step, the calculations of the weights of the normalized matrices with fuzzy preferences were provided to MATLAB software. Finally, the detection and convergence rates can be estimated. The study's findings showed that the simulation connection modules provided by PSO proved that the optimization related to the automatic washing of LED lights has fitness values to provide illumination. The simulation of the PSO algorithm for the dryness sensor showed the presence of fitness in the illumination supply, and its fitness value is equal to 2.03. The dryness sensor is expected to monitor the environment 30 times at any given time to detect pollution. The simulation of the PSO algorithm for the light sensor shows fitness values. The light sensor monitors the environment 101 times at a time interval to detect pollutants. In addition, the simulation of the PSO algorithm for cloud servers demonstrates secure communication with the processing center. Cloud servers exchange environmental pollution information 63 times in any given period. The PSO simulation algorithm for the processing center also showed its fitness level of pollution detection so that the processing center can process pollution information 25 times for LED lights at any given time interval.
