Energy efficient workflow scheduling for fog-enhanced IOT based healthcare application

Abstract

The Internet of Things has generated a tremendous amount of data. These files must be handled quickly and are typically kept in a cloud data center. Cloud computing can provide abundant resources to process such information IoT devices relating to data security, energy consumption, and data processing are driving the growth and interplay of fog computing and cloud computing. The problem of resource scheduling in a system designed to reduce task execution time and overall energy consumption. To partition the graph and determine task processing permutations and processor assignment, the tri EDA and division operator are employed. The comparison findings of a single application simulation reveal that the Pareto set produced by the proposed methodology can dominate a considerable number of those solutions produced by the heuristic technique and straightforward EDA. When it comes to multi-objective simulation, IoT devices can last much longer and perform similarly to other algorithms in terms of nodes' energy consumption and job completion time.