Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Abstract

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.