Scheduling algorithm for real-time embedded control systems using arduino board

Abstract

Automation has become a key component in the advancement of technologies in the recent past, these advancements are centered towards ensuring the increase in productivity with minimal man power by the usage of smart sensor systems and intelligent actuators that contributes to the increased efficiency of the system. Some applications that are used in day to day life such as elevator control and train traffic management require automation that has the ability to schedule processes effectively so that the safety in terms of collision avoidance and process deadline satisfaction is guaranteed. The use of multicore systems using algorithms that can schedule a process is become a common practice. The system divides the task into different sub-modules and handles deadline requirements by sharing the tasks with other modules within the system. There exists few aspects of this system that has to be considered while evaluating the efficiency of the system they are as follows: a) execution time b) worst case scenario and the deadlines of the system [1]. Using an embedded system for scheduling helps in allocating the available resources for the tasks and sequencing the process through understanding the use of scheduling tasks over a given time period [2]. In this paper, we propose an idea of validating the efficiency of the scheduling algorithm used in microcontroller platforms such as the VHDL-based Altera FPGA board and Arduino board to implement two practical examples of elevator control system and train traffic management. As an initial proof of concept the fabrication of the prototype model and implication of scheduling algorithm using Altera FPGA board using the logical algorithm to verify its efficiency, speed of operation and critical execution time is carried out successfully. Based on our observation from the initial phase of our proposed project, the difficulties faced while using Altera board involving the parallel processing issues is compared with the same experiment implemented using an Arduino board in phase 2 of our project. Finally, we are planning to work on optimization of the scheduling algorithms by integrating the scheduled algorithms using ant colony optimization technique