An improved discrete flower pollination algorithm for fuzzy QoS-aware IoT services composition based on skyline operator

The Quality of Service (QoS)-aware Service Composition (QSC) for Internet of Things (IoT) consists of connecting different available atomic IoT-services to produce a Composite of IoT-Services (CS) which satisfies the requirements of end users. With the growing number of atomic IoT-services that have similar functionalities with different values in their QoS parameters, it has been a challenging issue to select the suitable ones in order to generate an optimal CS with high quality in terms of QoS values which should fulfill the end users' constraints. This problem, which is an NP-hard constrained optimization one, has been generally solved under the assumption of precise and deterministic QoS values, which is not fully advisable. Since the QoS values of an IoT-service are doomed to be altered at any point, due to changes in topological structure of IoT networks, mobility of IoT devices, IoT systems congestion, and economic policies. Hence, the ambiguity of the QoS parameters is represented using the generalized trapezoidal fuzzy number (GTrFN). Moreover, a novel efficient approach combining two modules (1) a fuzzy skyline-based module and (2) an improved discrete flower pollination algorithm is proposed to solve the QSC in Fuzzy IoT environments (QSCFIoT). The performance and the efficiency of the proposal are validated on different scales of QSCFIoT using fuzzy versions of the real QWS and a large-sized synthetic datasets; while the experimental results demonstrate that the proposed approach is superior to some recently proposed QSC optimization algorithms such as EFPA, PSO and ITL-QCA in terms of composition's quality, time, and stability

Automated summary

This article discusses the selection of suitable services to generate high-quality composite services for the Internet of Things. The proposed approach uses fuzzy numbers and an improved discrete flower pollination algorithm to solve this problem. Experimental results show that this method outperforms other algorithms in terms of composition quality, time, and stability.