A Fuzzy based Scheduling approach for Efficient Sensing in IoT-Cloud

Smart health management and monitoring is an important IoT application. HealthCare Applications of today's age are very crucial and sensitive. Energy is an essential factor, particularly in the crowd sensing domain, where it is difficult to maintain the sensing infrastructure. The storage and processing of data is highly expensive since it is associated with computational resource requirements. Handling a vast amount of unwanted data requires more energy, and it also involves enormous processing time. Existing Scheduling techniques utilize the classification and optimization algorithm. Thus it takes more processing time to perform the scheduling. An Artificial Neural Network (ANN) based fuzzy technique is proposed for IoT data scheduling to optimize the system's resources such as memory storage, CPU processing time, and energy consumption. Further, the proposed technique is analyzed and compared with other framework.