A planning framework for QoS based composition services in IoT environment

Recently, Internet has developed into a new technology known as Internet of Things (IoT), enabling the interconnection of billions sensors, actuators, devices, as well as users, which are applied for huge data generating. Moreover, due to the numerous and heterogeneous included devices, and services delivered, the IoT service discovery, selection, and composition is a challenging task. Given that several services have the same functionality but different non-functional criteria (QoS). Thus it is interesting to create an automatic, dynamic, and optimal IoT service composition system to respond on real time to large scale of services, including QoS. Accordingly, we propose, in this paper, an approach for IoT service composition based automatic planning (AP) enhanced by genetic algorithm (GA). The objective is to produce a plan composition satisfying the optimal QoS. In addition, we employ the cloud technology to establish an optimal IoT service composition with less memory consumption, and expanded scalability of our suggested framework. The simulation results have illustrated the efficiency of our approach to resolve the composition problem in a distributed and dynamic IoT system.