Abstract:
he rapid growth of the Internet of Things (IoT) has led to the proliferation of services, making Quality of Service (QoS)-aware service
composition a critical challenge. This thesis addresses this issue through three complementary contributions. The first contribution
presents a systematic literature review of QoS-aware service composition approaches, introducing a two-layered taxonomy that distinguishes between plan-based and autonomous approaches. This review identifies key limitations in the state-of-the-art, such as the lack
of semantic matching consideration, the assumption of a prior existence of an abstract composition plan, limited scalability, and the use
of fixed population sizes. These findings highlight the need for more effcient and adaptive approaches. To overcome these issues, the
second contribution proposes the parallel differential evolution-based approach with population size reduction for QoS-aware services
composition (PDE-QSC). By evolving two parallel sub-populations with distinct strategies and adaptively reducing population size,
the PDE-QSC approach improves the composition quality and computation time compared to five baseline approaches. However, this
approach still relies on the existence of an abstract plan and does not account for the semantic matching aspect. The third contribution
addresses these remaining limitations by introducing two database concepts-driven approaches for autonomous QoS-aware semantic
service composition (HCFDSSC and ESFDSSC). The proposed approaches leverage functional dependency theory to ensure semantic
feasibility, reduce the search space, achieve fault tolerance in the case of service failures, and generate high-quality compositions. This
thesis advances QoS-aware service composition in IoT by linking plan-based optimization and autonomous semantic approaches, opening
new perspectives for scalable, adaptive, and resilient service systems.
