Formulation of Control Strategies for IoT Task Scheduling

The various Internets of Things (IoT) application tasks are difficult to schedule due to heterogeneity properties of IoT. So an efficient algorithm is required that forms pair appropriately. This paper presents a more sensible model for varying execution times of tasks and deviation in task parameters for building a schedule is allowed. The system provides an adaptive learning mechanism called Expected Time Matrix ETM (i, j). When the environment of the system changes dynamically, the system learns and adapts itself to the new changes automatically, since the learning mechanism has been incorporated in the system. ETM (i, j) concepts allows the system to learn from past instances as well. The work is supported by simulations that highlight the viability of concepts proposed. The key objective of this paper is to present the developed scheduling algorithm that is self-configurable and dynamicThe various Internets of Things (IoT) application tasks are difficult to schedule due to heterogeneity properties of IoT. So an efficient algorithm is required that forms pair appropriately. This paper presents a more sensible model for varying execution times of tasks and deviation in task parameters for building a schedule is allowed. The system provides an adaptive learning mechanism called Expected Time Matrix ETM (i, j). When the environment of the system changes dynamically, the system learns and adapts itself to the new changes automatically, since the learning mechanism has been incorporated in the system. ETM (i, j) concepts allows the system to learn from past instances as well. The work is supported by simulations that highlight the viability of concepts proposed. The key objective of this paper is to present the developed scheduling algorithm that is self-configurable and dynamic