Privacy-aware cloud service composition based on QoS optimization in Internet of Things

With the advent of Internet of Things (IoT), cloud service composition problem has gotten more complex along with the composition of large number of cloud services and numerous complex requirements from the users as an NP-Hard problem. In order to compose various cloud services in the IoT context, a conceptual model is required besides an effective selection and composition model. Moreover, as an important aspect of today's human life necessity, privacy preserving during sharing the data is a crucial concern. Therefore, privacy-aware cloud service composition along with improving the other quality of service (QoS) factors is an essential research motivation especially in the cloud based-IoT environments. In this paper, a privacy-aware cloud service composition approach with respect to QoS optimization in the IoT environment is proposed through presenting an IoT-based cloud service composition conceptual model regarding the privacy level computing model and a novel hybrid evolutionary algorithm using shuffled frog leaping algorithm (SFLA) and genetic algorithm (GA) which is called SFLA-GA. The proposed algorithm is applied to optimize the suggested service composition in terms of aggregation of different QoS factors as fitness value. Also, in order to help the users to select the proper composite service, they are categorized regarding their privacy preserving level which is obtained through a computational model. Simulation results revealed that the proposed approach improves the fitness in comparison to other contemporary algorithms.