Evolutionary testing using particle swarm optimization in IOT applications

Internet of things (IOT) is coming up in a major way connecting all physical objects and managing communications and interactions. These highly informative and data intensive applications are both critical to create and manage. The research under consideration proposes an evolutionary algorithm that uses particle swarm optimization to obtain a wide search space according to the IOT data space. The testing search space has particles which are the candidate solutions to predicted errors for all encountered and un-encountered error possibilities. For each search space, particle speed and velocity moments are calculated and adjusted in perturbed iterations, depending upon the expected level of discrepancy that might appear or according to influx of data change and co-relation. This research implements the POS algorithm for optimizing IOT applications over dynamic periods of time. IOT is the future and thus needs to be both protected and tested for more comprehensive advantages coming in through IOT applications.