WIRELESS SENSOR NETWORK COVERAGE OPTIMIZATION FOR INTERNET OF THINGS

Wireless Sensor Network Coverage Optimization for Internet of Things

Wireless Sensor Network Coverage Optimization for Internet of Things

Blog Article

The objective of this work is to improve the existing Wireless Sensor Network coverage optimization method.The pigeon-inspired optimization algorithm was first evaluated, and its iphone xr price calgary shortcomings were noted.The pigeon-inspired optimization method was then enhanced with the good point set, Yin-Yang optimization algorithm, and opposition-based learning.

To test the improved algorithm, five representative standard functions were chosen: sphere function (f1), Rosenbrock function (f2), Levy function (f3), Schwefel function (f4), and Levy function N.13 (f5).The algorithm's speed of convergence may be determined by the first two functions, which are unimodal.

The final three functions, which are multimodal, can extract several local optimal values from the local optimum.In comparison with other known algorithms, the improved Yin-Yang PIO algorithm showed the highest optimization accuracy and stability.Three sets of experiments were performed to optimize the WSN coverage with different parameters.

The first series of experiments suggest that Yin–Yang PIO has the best optimization effect, with a coverage rate of 99.51% (10.22% higher with PIO ethical nutrients mega magnesium powder citrus 450g and 6.

41% higher compared with PSO).The second and third series of experiments show that Yin-Yang PIO significantly increased the WSN coverage ratio, up to 99.9%.

The algorithm can be applied to optimize WSN coverage in various environments.Future research can extend the research scope to include other optimization problems in IoT. .

Report this page