A load balancing scheme based on deep-learning in IoT

Extending the current Internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. The Internet of Things (IoT) contribution is in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge for the benefit of society. Benefit due to the service controlled by communication between objects is now being increased by people who use these services in real life. The sensors are deployed to monitor one or more events in an unattended environment. A large number of the event data will be generated over a period of time in IoT. Hence, the load balancing protocol is critical considerations in the design of IoT. Therefore, we propose an agent Loadbot that measures network load and process structural configuration by analyzing a large amount of user data and network load, and applying Deep Learning's Deep Belief Network method in order to achieve efficient load balancing in IoT. Also, we propose an agent Balancebot that processes a neural load prediction algorithm based on Deep Learning's Q-learning method and neural prior ensemble. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analysis.