A combined pattern recognition scheme with genetic algorithms for robot guidance using Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), using physically sensed data for accurate automated decision making is challenging. In response to these challenges, a combined Genetic Algorithm (GA) and pattern recognition scheme (PR) is presented in this paper. The aim of the scheme is to reduce the exponential relationship between problem size and time complexity of GA for guiding robots using WSN. The PR scheme presented in this paper is called Cellular Weighted Pattern Recogniser (CWPR) that simplifies computations and communications for energy conservation and speeds up recognition by leveraging the parallel distributed processing capabilities of WSN. Additionally, CWPR solves the problem of dilation, translation, and rotation to provide efficient pattern recognition in energy constrained WSN environments. Combining CWPR with GA allows GA to learn from experience and solve similar problems in fewer number of generations. The experimental results show that the approach efficiently supports a variety of PR applications for WSN guided robots.