An Autonomous Planning Model for Deploying IoT Services in Fog Computing

IoT-based devices are constantly sending data to the cloud. However, the centralization of cloud data centers and the long distance to the location of data sources has reduced the efficiency of this paradigm in real-time applications. Fog computing can provide the resources needed by Internet of Things devices in a distributed manner at the edge of the network without involving the cloud. Therefore, processing, analysis and storage are closer to the source of data and end users cause the delay is reduced. Every Internet of Things program includes a set of Internet of Things services with different quality of service requirements, whose required resources can be provided by deploying on cloud nodes. This study deals with the challenge of locating Internet of Things services as an autonomous planning model in fog computing. We develop the colonial competition algorithm as a meta-heuristic approach to solve this problem. Since fog nodes with enough resources can host several IoT services, we consider resource distribution in the localization process. The proposed algorithm prioritizes Internet of Things services to reduce delay and solves the multi-objective positioning problem. The results of the experiments show that our algorithm can effectively improve the performance of the system and have 15% to 31% better effectiveness than the best results of the advanced algorithms in the literature.