Multiple approaches aim to enhance user experience in the delivery of immersive video content. The popularisation of VR, combined with recent advances in mulsemedia technology has improved access to immersive visual and olfactory stimuli. Synchronising multiple scent dispensers positioned around the user when watching 360° videos can more accurately indicate the location of scent sources, guiding users to move their heads accordingly to the indicated directions. However, the manual annotation process required to add mulsemedia effects is labour-intensive, limiting the availability of content with sensory enhancements, particularly when using multiple scent dispensers from various directions. Addressing this issue, this paper introduces OmniScent-CNN, an innovative solution to automate the diffusion of scents from different directions in a VR environment using Convolutional Neural Networks (CNNs) for scene recognition. Multiple instances of the solution were tested, employing a number of CNN architectures. The results demonstrated that olfaction accuracy can reach up to 71.28% with the ResNet-18 model. Furthermore, user perceptual tests revealed excellent results, with 87.5% of participants agreeing or strongly agreeing that the scents enhanced their enjoyment of the experience. This indicates the feasibility of automating the process of synchronising omnidirectional scents based on 360° scene recognition.

Skin detection technology serves multiple purposes across various sectors, including surveillance, criminal justice, and healthcare. This study focuses on skin detection by extracting RGB values of skin tones from facial images of diverse ethnic backgrounds. Utilizing a three-tier OpenCV-based architecture, the approach encompasses skin detection, skin tone identification, and is tested in an application for recommendation of the most fitting makeup foundation shade, brand, and product. The research evaluates three color-space models for their effectiveness in skin and skin tone detection: HSV (Hue, Saturation, Value) with Gaussian blur, HSV alone, and a combination of HSV and YCrCb enhanced with gamma correction and image segmentation. The precision of each color space method was evaluated by measuring the difference between the predicted RGB skin tone values and the actual RGB skin tone values, utilizing the Delta-E metric for comparison. The hybrid model combining HSV and YCrCb color spaces emerged as the most accurate, achieving the lowest Delta-E average value of 16.68, thereby surpassing the other methodologies.

This paper introduces the Utility-Based Multipath Transmission Control Protocol (uMPTCP), an innovative extension of MPTCP designed for prioritized extended reality (XR) content delivery, based on monitored Quality of Service (QoS) network metrics. The proposed approach includes an algorithm that assesses subflows’ delivery performance and dynamically selects the most efficient one to deliver prioritized content with reduced latency. A comparison is made against a stateof-the-art solution, the virtual private channel (VPC), and the default MPTCP algorithm. The evaluation considers both singlehomed and multi-homed configurations in scenarios with varied bandwidth requirements, including XR. The study is conducted within the framework of the FRADIS project, aimed at providing a comprehensive solution for 5G and beyond heterogeneous network environments. FRADIS integrates machine learning to optimize service-specific approaches, allowing a choice between traffic engineering with network slicing and protocol-based solutions, including the proposed uMPTCP solution. The framework supports a diverse range of services for smart city monitoring, XR applications, e-health solutions and entertainment.

Bluetooth Low Energy (BLE) is one of the technologies that can be used for short-range communications. BLE beacons are small wireless devices that can store URLs and real-world data, including GPS locations and identifiers. This article proposes BeTwin, an efficient solution that integrates BLE beacons with a digital twin environment in order to enhance and customise a virtual 3D platform, bridging the real and virtual worlds. The beacons are used to bring information from real objects into the virtual platform. We extend the Eddystone messaging protocol and investigate the impact of beacon distance, energy levels, and number of beacons on the system performance. We also describe a testbed consisting of a virtual environment, a beacon reader along with beacons, and assess its performance in terms of communications latency, Received Signal Strength Indicator (RSSI), and number of beacons.

Improving user experience during the delivery of immersive content is crucial for its success for both the content creators and audience. Creators can express themselves better with multisensory stimulation, while the audience can experience a higher level of involvement. The rapid development of mulsemedia devices provides better access for stimuli such as olfaction and haptics. Nevertheless, due to the required manual annotation process of adding mulsemedia effects, the amount of content available with sensorial effects is still limited. This work introduces an innovative mulsemedia-enhancement solution capable of automatically generating olfactory and haptic content based on 360 video content, with the use of neural networks. Two parallel neural networks are responsible for automatically adding scents to 360 videos: a scene detection network (responsible for static, global content) and an action detection network (responsible for dynamic, local content). A 360 video dataset with scent labels is also created and used for evaluating the robustness of the proposed solution. The solution achieves a 69.19% olfactory accuracy and 72.26% haptics accuracy during evaluation using two different datasets.

Virtual Reality (VR) popularity is increasing as it is becoming more affordable for end users. Available VR hardware includes low-end inexpensive devices like Google Cardboard and high-end ones like HTC Vive or Oculus Rift, which are more expensive headsets. Using VR as a platform for content delivery allows better user engagement than other traditional methods, as VR headsets remove external distractions. Multiuser VR applications provide shared experiences where users can communicate and interact in the same virtual space. This shared environment, however, introduces challenges regarding network performance, quality of service (QoS) and sessions privacy. This paper presents a multi-user VR application and aims to evaluate network behaviour in a number of scenarios, including real VR headsets (i.e. Oculus Rift), as well as simulated ones. This QoS analysis is important for the understanding of how many VR users can be simultaneously connected with high image quality.

The Internet of Things (IoT) is a growing network of physical objects where the devices are connected to the Internet through unique addressing schemes and multiple protocols. The increase of IoT devices in the recent years presents significant challenges in terms of security, authentication and usability. The recently introduced Social Internet of Things (SIoT) tries to address these challenges with the virtualisation of IoT devices and the use of an infrastructure where people and IoT devices can communicate with each other, both in the real-world and virtual-world, through a common platform. In the proposed SIoT architecture, IoT devices can be operated by virtual reality (VR) headsets and Twitter, a social media platform. The aim of the platform is to allow users to seamlessly operate IoT devices, using their preferred interface: remotely with text messages (i.e.tweets) and VR headsets or operate the IoT devices directly. This paper also describes the implementation of a testbed and presents the performance analysis of the solution, demonstrating its feasibility and low latency.

Internet of Things (IoT) networks handle multiple data types generated by numerous types of devices, with the additional challenge of delivering this data at high QoS levels. A great number of IoT applications require real-time updates and fresh data, in areas such as health, vehicular, UAVs and sensor monitoring (temperature, acceleration, motion, etc therefore, a metric such as the Age of Information (AoI) is useful to measure how recent the information is, considering the difference between the time it was generated and the time it is successfully delivered. In this paper, we propose an architecture consisting of IoT objects, which provide diverse services including video applications and CCTV monitoring, smart gateways, which support network connectivity, and a cloud-deployed platform utilised for resource management. The performance, quality and mobility challenges of IoT services are improved through an algorithm which uses Quality of Service (QoS), AoI, objects location and service relevance metrics in order to cluster the networked IoT objects, attach them to the most suitable smart gateway or relay device and improve the performance of their services.

The extremely large number of devices available to the modern day user, with the increase in device intercommunication, is fuelling the latest Internet of Things (IoT) development. IoT needs to enable exchange of various types of data, from sensor data to multimedia, between numerous diverse devices differing in power, connectivity, mobility and energy, while also maintaining high levels of Quality of Service (QoS). This paper performs statistical analysis of the innovative NETworking Scheme for sMart IoT gatewayS (NETSMITS) in terms of several QoS network-related metrics with most significant impact on devices' performance. NETSMITS introduces an innovative algorithm which uses QoS and service relevance metrics in order to efficiently cluster intercommunicating IoT objects. Statistical analysis is performed on the QoS data collected in a highly relevant multi-device scenario in order to understand NETSMITS' behaviour. Interesting results were obtained, describing the relationship between the QoS metrics and different types of IoT devices.