For highly dynamic vehicular environments, this paper presents a fully decentralized, context-aware network slicing framework for next-generation (6 G) networks. Using Distributed Deep Reinforcement Learning (DDRL), slice participation of edge nodes is made autonomously based on local observations of latency, available energy levels, trust scores, and QoS requirements. Unlike centralized or federated systems, the presented framework eliminates the need for global coordination, thus reducing bottlenecks while enhancing adaptability and fairness. Simulations performed confirm high levels of performance in reducing delay and increasing QoS satisfaction and resilience in comparison to centralized DRL, federated learning, and DSRC/5G-V2X baselines.

Content delivery through cloud networks has gained popularity due to its effectiveness and reliability. Much progress has been made to allow for the easy integration of heterogeneous systems to compose services on the fly. Despite all efforts, the task of fast service composition has made the assumption of the presence of static searchable and updatable central repositories within cloud data centers. With today's dynamic and mobile environments, service providers' movements are unpredictable, thus a rejuvenated service composition mechanism for cloud environments is needed. This paper presents a cloudlet-supported service composition solution in cloud networks that can find a suitable mechanism to discover mobile media processing functions and seamlessly integrate them into media delivery sessions. Furthermore, the established sessions are monitored by a novel client-side QoS measurement collection mechanism and adapted dynamically to network, user, and service providers' changing conditions. Simulation results showcase the effectiveness of the presented solution in terms of composed service stability and QoS measurement collection accuracy.

Optimization of service compositions represents a challenging area of research in mobile networks. The use of formal semantic descriptions of service interfaces and functionalities enable automated reasoning over service compositions. The growing challenge of finding effective methods to satisfy non-functional QoS requirements of customers exacerbates the composition problem. Fuzzy reasoning provides an effective means to represent QoS concepts that are vague or approximate thus providing a flexible best-match service query scheme. This paper presents a new fuzzy-induced, semantic-similarity-based service-specific overlay network composition over a hybrid service overlay network. The presented composition method allows for efficient, accurate, and QoS-aware component service discovery, composition, and execution. We demonstrate that our approach provides better performance and reduced composition delay when compared to other service-composition solutions.

The inefficiency of one-to-one matching of user requirements to a single service has given rise to service composition. Service composition allows simple services to be dynamically combined into more complex services. In mobile environments, service discovery and integration involves cooperation between various service nodes where reliance on a centralized service registry introduces efficiency and single point of failure problems. Plan- and workflow-based composition solutions restrict service discovery to defined abstract models. Semantic similarity measurements play an important role in service integration. In this paper we introduce a semantic similarity and semantic nearness service composition method. The objective of our work is to free service nodes from adherence to restrictive composition plans. Our work also enables compositions to succeed in dynamic environments through a decentralized solution even when nodes are unaware of nearby service nodes. This paper presents a service composition solution that semantically advances composition paths towards users’ needs with each service hop while guaranteeing a user-acceptable QoS level. Simulation results showcase the effectiveness of the presented solution and its compatibility with underlying service discovery methods.

Context based dynamic adaptation of autonomous services and applications require the acquisition of an array of contextual information. Context dissemination mechanisms may result in network flooding, unrestricted access to private context information, and the inability of consumers to limit or personalize received context. Adapting context information disseminated to users is linked to direct contextual requests made by users. This paper describes policy- and ontology-based, context-aware system architecture. A Context Level Agreements is proposed to help deliver contextual information to users according to their needs. The paper illustrates use of the negotiation protocol through design and implementation of context-aware system architecture capable of acquiring, modeling, reasoning and disseminating context through ontologies.