<p>The amount of data generated has recently increased significantly as IoT devices and sensors have grown. This vast volume of data collected necessitates sophisticated processing. We need novel computing strategies like fog to manage huge data since IoT sensors have limited resources and energy. Task scheduling with available resources is one of the most essential difficulties in the fog-cloud environment. This paper proposes an Integer Linear Programming (ILPM) model for scheduling tasks in the fog-cloud environment to minimize cost and energy consumption. Requests from IoT sensors are first turned into tasks in the ILPM, and a data structure is created for them. Then, these tasks are prioritized based on their deadline and type of resources. Finally, the integer linear programming model is used to select the most appropriate placement to perform the tasks based on the task requirements and the capacity of the service providers. The simulation results show that the proposed algorithm outperforms the four comparable algorithms in terms of cost, energy consumption, scheduling time, and response time.</p>