Adaptive QoS-Aware Service Composition in the Internet of Things Using a Hybrid Bayesian Network-Based Optimization Algorithm

As smart cities nowadays use many Internet of Things (IoT) devices, the need for service composition methods that are both efficient and flexible has become unavoidable. Methodologies should be designed to provide flexibility and sustain high quality of service (QoS) under dynamic urban conditions. Deterministic techniques often have poor scalability and require complete, accurate QoS information. In contrast, nondeterministic approaches can handle uncertainty but tend to be computationally expensive and less reliable under dynamic conditions. Service composition is considered an NP-hard problem due to the exponential complexity involved in selecting and combining optimal services under multiple QoS constraints. This article proposes a novel hybrid framework called Bayesian network (BN)-Gray Wolf optimizer (BN-GWO), which integrates BNs with the Gray Wolf optimizer (GWO) algorithm. The BN captures conditional dependencies among QoS attributes and accurately estimates missing values, while the GWO algorithm efficiently explores the compositional space to optimize QoS metrics. Experimental evaluations conducted using QoS traces synthetically generated via the iFogSim2 simulation platform demonstrate that the proposed BN-GWO framework significantly outperforms state-of-the-art methods across multiple performance metrics.