Handover management in mobile networks is challenged by high latency and reduced reliability in dense deployments and under user mobility. Here, existing schemes improve handover initiation by optimising the candidate handover at the decision time. However, these are applied after a non-negligible delay due to the control-plane signalling. Then, when applied, it may become invalid or degrade performance. To address this, we propose a Digital Twin (DT)-assisted handover scheme that performs predictive execution-time validation prior to the preparation of the Next Generation (NG)-based handover. To this end, the DT-What-If Generator (DT-WIG) is used to emulate short-horizon future network states under uncertainty. Here, the DT-WIG is a spatiotemporal graph generative model that uses variational latent sampling to generate counterfactual post-handover trajectories for the candidate handover decision. Then, the AMF estimates the failure and QoS risks associated with the candidate handover and approves/rejects it via standard-compliant signalling. With this, we form a policy-agnostic mechanism that runs on the underlying handover policy. Consequently, we evaluate performance using ns-3/5G-LENA trace generation and replay-based policy analysis, with OpenAirInterface-based signalling evaluation. The results show that the proposed method reduces the handover failure rate and handover interruption time while improving latency, jitter, throughput, and packet loss.

The Network Digital Twins (NDTs) have become a frontier thanks to their real-time monitoring, analysis, prediction, and optimisation capabilities. However, their architecture has not been designed for the scale of next-generation networks that will ensure ubiquitous connectivity. At this, different NDT applications may require distinct levels of quality of service requirements to be met. With increasing size and heterogeneity in the networks, the processing load at the NDTs escalates, and these requirements may not be met. Therefore, we propose a microservice-based architecture with application-oriented slicing. Here, we present the scaling methodology, which enables scaling at both microservice and slice levels. Then, we evaluate the throughput, delay, and quality of service requirement violation rate metrics under two scenarios. Thanks to the slicing approach with microservice-based implementation, the end-to-end delay and the QoS requirement violation rate are reduced while having higher throughput performance.

Although the emergence of 6G IoT networks has accelerated the deployment of enhanced smart city services, the resource limitations of IoT devices remain as a significant problem. Given this limitation, meeting the low-latency service requirement of 6G networks becomes even more challenging. However, existing 6G IoT management strategies lack real-time operation and mostly rely on discrete actions, which are insufficient to optimise energy consumption. To address these, in this study, we propose a Digital Twin (DT)-guided energy management framework to jointly handle the low latency and energy efficiency challenges in 6G IoT networks. In this framework, we provide the twin models through a distributed overlay network and handle the dynamic updates between the data layer and the upper layers of the DT over the Real-Time Publish Subscribe (RTPS) protocol. We also design a Reinforcement Learning (RL) engine with a novel formulated reward function to provide optimal data update times for each of the IoT devices. The RL engine receives a diverse set of environment states from the What-if engine and runs Deep Deterministic Policy Gradient (DDPG) to output continuous actions to the IoT devices. Based on our simulation results, we observe that the proposed framework achieves a 37% improvement in 95th percentile latency and a 30% reduction in energy consumption compared to the existing literature.

Digital Twins (DTs) have become indispensable in 6G for intelligent applications with real-time monitoring, modelling, and optimization. However, validating them in real-world conditions using IoT integration remained a significant challenge. Due to specialized designs, current implementations often lack reusability, which prevents integration. Here, data streams have to be formed while managing the diverse IoT data sources, formats, and volumes. However, interoperating these requires extensive manual programming. Considering these challenges, this paper proposes the Scenario Emulated IoT-DT architecture that defines the layers of Data, Ingestion, and DT. Here, the Scenario Emulator can form and integrate the data streams with the different intelligent applications. Within this, we use graph-encoding in Device and Data Schema Registry to define specifications. With this, we can embed the required information for interoperability and integration. Moreover, this approach supports scalability thanks to the fast lookup capability of graphs as the number of objects increases. Using the proposed architecture, we implement the use case of smart building management and test it in different scenarios. The results show that the proposed architecture can effectively operate with up to 74.7 % line-of-code improvement and querying latency reduction.

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data capturing in traditional approaches, resulting in inaccurate modelling and high resource and energy consumption challenges. To fill this gap, we explore spatiotemporal graphs and propose the Reinforcement Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN). By doing so, our study contributes to advancing Green Cities and showcases tangible benefits in accuracy, synchronisation, resource optimization, and energy efficiency. As a result, we note the spatiotemporal graphs are able to offer a consistent accuracy and 55% higher querying performance when implemented using graph databases. In addition, our model demonstrates right-time data capturing with 20% lower overhead and 25% lower energy consumption.