Introducing Bee-Eater Hunting Strategy Algorithm for IoT-Based Green House Monitoring and Analysis

By skyrocketing of Internet of Things (IoT) applications in the recent decade, research improvement on its algorithms for more optimized manner is soared, too. Green house monitoring is one of the most in demand usages of IoT, as it decreases monitoring maintain cost and error. Automatic greenhouse monitoring helps to constantly manage environmental factors and reduce energy costs without human error. Data returned by IoT system could be transferred to regression task in order to analysis the relation between inputs and targets alongside with its correlation coefficient. At the same time these data could be clustered into similar groups, in order to make data easily understood and manipulated. To employ these two tasks, a new bio-inspired algorithm is introduced. Proposed Bee-Eater Hunting (BEH) Algorithm, not only can compete with famous evolutionary algorithms such as Genetic Algorithm (GA) but also, returns more optimized cost in comparison with other algorithms. Live data returned by ThingSpeak platform, sends to proposed Bio-inspired BEH algorithm for fuzzy regression and cluster analysis task and compares with other algorithms. Results shows considerable improvement in both tasks.