Background: With the further development of the electric Internet of Things (eIoT), IoT
devices in the distributed network generate data with different frequencies and types.
Objective: Fog platform is located between the smart collected terminal and cloud platform, and the
resources of fog computing are limited, which affects the delay of service processing time and response
Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed.
First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes
cluster the fog nodes through the K-mean+ algorithm and implement the earliest deadline first
dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm.
Results: We use tools to simulate the environment, and the results show that this method has strong
advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate, and energy
consumption, which provides a better guarantee for eIoT.
Conclusion: Resource scheduling is an important factor affecting system performance. This article
mainly addresses the needs of eIoT in terminal network communication delay, connection failure,
and resource shortage. A new method of resource scheduling and load balancing is proposed. The
evaluation was performed, and it proved that our proposed algorithm has better performance than
the previous method, which brings new opportunities for the realization of eIoT.