This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load.

This study presents a novel investigation into immersive teleoperation systems using collaborative, device-agnostic interfaces for advancing smart haptics in industrial assistive applications. The research focuses on evaluating the quality of experience (QoE) of users interacting with a teleoperation system comprising a local robotic arm, a robot gripper, and heterogeneous remote tracking and haptic feedback devices. By employing a modular device-agnostic framework, the system supports flexible configurations, including one-user-one-equipment (1U-1E), one-user-multiple-equipment (1U-ME), and multiple-users-multiple-equipment (MU-ME) scenarios. The experimental set-up involves participants manipulating predefined objects and placing them into designated baskets by following specified 3D trajectories. Performance is measured using objective QoE metrics, including temporal efficiency (time required to complete the task) and spatial accuracy (trajectory similarity to the predefined path). In addition, subjective QoE metrics are assessed through detailed surveys, capturing user perceptions of presence, engagement, control, sensory integration, and cognitive load. To ensure flexibility and scalability, the system integrates various haptic configurations, including (1) a Touch kinaesthetic device for precision tracking and grounded haptic feedback, (2) a DualSense tactile joystick as both a tracker and mobile haptic device, (3) a bHaptics DK2 vibrotactile glove with a camera tracker, and (4) a SenseGlove Nova force-feedback glove with VIVE trackers. The modular approach enables comparative analysis of how different device configurations influence user performance and experience. The results indicate that the objective QoE metrics varied significantly across device configurations, with the Touch and SenseGlove Nova set-ups providing the highest trajectory similarity and temporal efficiency. Subjective assessments revealed a strong correlation between presence and sensory integration, with users reporting higher engagement and control in scenarios utilizing force feedback mechanisms. Cognitive load varied across the set-ups, with more complex configurations (e.g., 1U-ME) requiring longer adaptation periods. This study contributes to the field by demonstrating the feasibility of a device-agnostic teleoperation framework for immersive industrial applications. It underscores the critical interplay between objective task performance and subjective user experience, providing actionable insights into the design of next-generation teleoperation systems.

In the last few years, the use of automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) has experienced a sustainable increase in different verticals such as factories and logistics. However, they still have some technical limitations that hamper their autonomous operation in unpredictable or dynamic environments, requiring them to be supervised and/or controlled by human operators. In such situations, current tele-operated driving (ToD) systems lack the required stimulation and spatial perception to precisely manipulate the AGVs/AMRs, besides suffering from real-time challenges that limit the accuracy of movement. This chapter describes a proposal to solve these problems, by combining low-latency 5G-IoT networks and immersive cockpits equipped with haptic and mixed-reality devices. It also explains how such devices provide intuitive feedback for ToD and facilitate context-aware decision-making. The results are validated in the context of two innovative demonstrations deployed in the environment of a 302seaport, where ToD of multiple AGVs/AMRs is supported by a 5G mm Wave network infrastructure. <!----> <!----> <!----> <!---->

Reaction times are critical for assessing user response efficacy in applications like virtual environments, assistive technologies, and real-time monitoring. This study examines reaction times across visual, audio, and haptic stimuli, categorized into signal path (direct vs. encoded) and task context (static vs. dynamic), among N = 44 participants. Participants were grouped by demographic factors, including age, gender, and education; and lifestyle factors, including videogame and computer use, and physical exercise. We first analyzed observed reaction times and then derived effective reaction times by subtracting measured processing latencies for each delivery condition. Haptic stimuli yielded the fastest observed responses in the direct and dynamic conditions and comparatively low variability across the full sample. After latency correction, encoded haptic produced the shortest effective reaction times, followed by encoded audio, whereas visual stimuli remained the slowest, particularly in the dynamic condition. Exploratory summaries by demographic and lifestyle subgroup suggested some descriptive differences, but these patterns should be interpreted cautiously because several subgroups were small and imbalanced. Overall, the results support haptic cues as a promising channel for rapid-response applications within the specific end-to-end systems evaluated here, offering valuable insights for designing future technologies.