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Lately, gamification (i.e., employing game-design elements and game principles in non-game contexts) has gained massive popularity and widespread usage in various areas, including education. However, gamification deployment in education in general and remote education in particular still faces many challenges that mainly influence user quality of experience. These challenges include lack of dedicated communication-based systems, potential additional load on teachers, absence of customization and personalization for users, and no support for advanced technology-enhanced learning (TEL). This paper investigates the use of gamification for networked delivery of science, technology, engineering and mathematics (STEM) subjects. It proposes an innovative gamification framework, the NEWTON-enhanced gamification model (N-EGM), which was designed as part of the European Horizon 2020 project NEWTON. The paper also describes the proposed N-EGM model deployment in the gamification engine of a real learning management system and its associated communication and networking solution. The communication support provides easy-to-use gamification configuration functionality and efficient data collection and processing in a heterogeneous technology context. Finally, the paper evaluates the proposed N-EGM model as part of a NEWTON project pilot deployed in a Romanian school. The results demonstrate the effectiveness of the proposed gamification solution in improving both students’ learning experience and their engagement, while also increasing student knowledge gain.

Interactive 360° remote video applications have seen booming advancements due to the proliferation of smart display devices that enable a truly immersive experience. Compared to regular monoscopic videos, 360° videos have different requirements related to content preparation, packaging, transmission, specialized viewing equipment, and display factors (e.g., brightness, contrast, delay, frame rate, resolution, image quality, etc. In addition, 360° video requires substantial network and computational resources, which are challenging to achieve with conventional transmission and rendering infrastructure. Viewport-adaptive streaming is a common way to ensure visual quality under limited bandwidth resources. However, identifying, extracting, and rendering the true viewport in response to drastic head rotations can adversely affect user experience. This paper proposes two dynamic viewport selection approaches, which adapt the streamed regions based on content complexity variations and positional information to ensure viewport availability and smooth visual angles for VR users. They incorporate content information as well as user head movement patterns to support tile-based prioritized 360° video streaming. Moreover, a practical, prioritized bitrate adaptation approach, which requests selected tiles at appropriate quality levels, is also proposed to reduce the impact of inefficient bandwidth utilization in the VR scene. Experimental evaluations under real 4G bandwidth logs demonstrate that the proposed solutions outperform the closest state-of-the-art algorithms across multiple performance metrics, i.e., viewport overlap, perceived quality levels, quality fluctuations, and viewport bandwidth utilization.

The Internet of Things (IoT) can avail from device-to-device (D2D) communication techniques to increase object data exchange performance. IoT networks aim to offer a massive number of services at high quality levels, and many of the devices providing these services are mobile. Devices such as wearables, sensors, drones and smart vehicles need constant connectivity despite their moving patterns and therefore, an IoT architecture should consider both Quality of Service (QoS) and mobility. D2D allows devices to communicate directly to share content and functionality, such as access to the Internet. This paper proposes REMOS-IoT - A RElay and MObility Scheme for improved IoT communication performance in support of increased QoS for the data exchange services between mobile IoT devices. Simulation-based testing showed how performance of devices increased in several scenarios, demonstrating the efficiency of the proposed architecture and algorithms.

High-resolution audio-visual virtual reality (VR) technologies currently offer satisfying experiences for both sight and hearing senses in the world of multimedia. However, the delivery of truly immersive experiences requires the incorporation of other senses such as touch and smell. Multisensorial effects are usually manually synchronized with videos and data is stored in companion files, which contain timestamps for these effects. This manual task becomes very complex for 360° videos, as the scenes triggering effects can occur in different viewpoints. The solution proposed in this paper aims to automatically add extra sensory information to immersive 360° videos. A novel scent prediction scheme using Convolutional Neural Networks (CNN) is proposed to perform scene predictions on 360° videos represented in the Equi-Angular Cubemap format in order to add scents relevant to the detected content. Digital signal processing is used to detect loud sounds in the video with a Root Mean Square (RMS) function, which are then associated with haptic feedback. A prototype was developed, which outputs multisensorial stimuli by using an olfaction dispenser and a haptic mouse. The proposed solution has been tested and it achieved excellent results in terms of accuracy of scene detection, olfaction latency and correct execution of the relevant effects. Different CNN architectures, including AlexNet, ResNet18 and ResNet50, were also assessed comparatively, achieving a labeling accuracy of up to 72.67% for olfaction-enhanced media.

Teachers face many difficulties in the motivation, engagement, and improvement of learning outcomes for students in Science, Technology, Engineering, and Mathematics (STEM). In this paper, we present a research study on the learning experience of a new interactive educational 3D video game called Final Frontier, designed for primary school students. This game supports student knowledge acquisition on two Solar System planets Mercury and Venus, and a satellite - Moon through direct experience, interactive challenges and fun. This article compares the learning impact of the computer game-based learning approach with a classic teacher-based learning approach. User experience with the game and game usability are also evaluated. A case study that involved 53 children was conducted for the evaluation. Among the most important findings, include the facts that the experimental group that used the game in their learning activity have performed much better than the control group and the students had a great learning experience when using the Final Frontier game.

Multimedia delivery support has recently been added to Wireless Sensor Networks (WSN) and has led to increased interest in Wireless Multimedia Sensor Networks (WMSN). WMSNs are expected to be crucial to the success of applications related to the Internet of Things (IoT), such as smart health, smart surveillance, smart homes, etc. Alongside their improved multimedia capabilities, WMSNs inherit WSN limitations such as energy and processing constraints. Additionally, WMSNs have significant Quality of Service (QoS) requirements, since multimedia delivery requires increased network performance in terms of bandwidth, latency, etc. Balancing energy efficiency and QoS is a fundamental challenge for WMSN users and operators alike. This paper proposes Reinforcement Learning based Duty Cycle (rlDC), an innovative learning-based scheme to adjust the duty cycle and contention window of WMSN nodes in order to meet energy efficiency and QoS targets. By employing rlDC, WMSN sensor nodes intelligently adapt their operation according to network delivery performance and application requirements. The proposed rlDC scheme was evaluated under different use cases in a simulation environment, and testing results show it outperforms other state-of-the-art duty-cycle-based protocols for WMSNs.

Contributions: This article presents the results of a study carried out as part of two large-scale pilots and analyzes the impact of the use of modern technologies in education in term of knowledge acquisition in case of students with hearing disabilities. Background: Teaching topics from STEM area is a challenging task for all teachers who have students with hearing disabilities in their class. On the one hand, because of the high degree of difficulty of the information in this field and on the other hand, because of the difficulties that students with hearing disabilities face in understanding this information. In this context, the use of modern technologies in education, but especially the way they are used together with traditional ones, can provide great support for teachers of students with special needs. Intended Outcomes: A set of modern technologies, i.e., virtual lab, virtual reality (VR), in the game-based learning context of the EU-funded NEWTON project, were employed in education as part of the Earth Course. The goal was to increase the accessibility of STEM information in the area of natural sciences for the benefit of students, especially those with hearing disabilities. Application Design: The study targeted secondary school students with hearing disabilities. Content related to natural sciences (i.e., wildlife and sealife) was presented as part of the Earth Course via innovative applications that included virtual laboratories and VR. Avatars were used to support the students with special needs (sign-language translation). Findings: The results of this study demonstrate that the use of modern technologies, combined with game-based learning in science lessons for students with hearing disabilities, contribute to increasing their knowledge level. The best results were obtained when these technologies were used in mixed lessons, alongside the traditional teaching methods.

Abstract Cloud and IoT technologies have the potential to support applica- tions that are not strictly limited to technical fields. This paper shows how digital fabrication laboratories (Fab Labs) can leverage cloud technologies to enable resource sharing and provide remote access to distributed expensive fabrication resources over the internet. We call this new concept Fabrication as a Service (FaaS), since each resource is exposed to the internet as a web ser- vice through REST APIs. The cloud platform presented in this paper is part of the NEWTON Horizon 2020 technology-enhanced learning project. The NEWTON Fab Labs architecture is described in detail, from system concep- tion and simulation to system cloud deployment and testing in NEWTON project small and large-scale pilots for teaching and learning STEM subjects.

The next-generation network environment is expected to include the networks of diverse types, associated with heterogeneous performance. At the same time, this heterogeneous network environment will be used to deliver various services with different requirements. In order to support high quality of experience for the users' availing from these services, there is a need for a solution to the complex problem of selecting the appropriate network support for each user service type. This paper introduces the network Traffic tYpe-based DifferEntiated Reputation (TYDER) solution, which differentiates the data delivery process according to its type. TYDER considers network reputation in the context of traffic type requirements in order to increase the delivery performance for the data exchanged. The Comparative testing involving four traffic categories, including video, gaming, browsing, and the Internet of Things, showed how TYDER outperforms a classic solution in terms of major performance metrics.

Increasing number of researchers are focusing on the emerging communication technologies which enrich user perceived quality of experience by involving vision, auditory, tactile, olfaction, gustatory, and other senses. However, there are multiple challenges related to using multiple sensorial media (i.e., mulsemedia), including synchronization with the traditional multimedia content and delivery over diverse network environments. This paper proposes MulseDASH, a novel multiple sensorial media content delivery solutions based on the Dynamic Adaptive Streaming over HTTP standard (DASH). MulseDASH is described and evaluated in a real test-bed in terms of the effectiveness of its adaptive streaming and synchronization mechanisms. The extensive testing involving both network emulation and subjective assessment experiments shows how MulseDASH performs an excellent real-time streaming adjustment to match network conditions and improves user quality of experience.

Virtual reality (VR) is currently being used in many different areas such as car prototyping, gaming, medical training, teaching, and so on. The Internet of Things (IoT) devices such as systems-on-a-chip (e.g. Raspberry Pi), smart appliances, and sensors support a wide range of services, including machine automation, and remote monitoring and control. This paper introduces a novel social VR-IoT environment, which allows users to share and control local or remote IoT devices in a virtual platform. Two approaches using the VR-IoT solution are presented: one local network-based and one cloud-based. The proposed VR-IoT environment contains VRITESS, the novel VR-IoT Environment Synchronization Scheme, which facilitates a consistent and integrated experience for users by enabling control of real IoT objects with VR headsets. The control of some IoT objects in extreme environments or devices which are complex to operate can be simplified in a virtual environment. The VRITESS synchronization scheme maintains the real objects updated following instructions given in the virtual world and vice-versa. Testing involved local network-based and cloud-based testbeds created with a VR headset and IoT devices with the Performance Engineering Laboratory, Dublin City University's, Ireland. Test results demonstrated that lower latency is experienced in the local-network testbed in comparison with the cloud testbed. Furthermore, tests regarding the communications protocols implemented in the cloud testbed indicated that MQTT generates less delay and data traffic than REST.

Fabrication as a Service (FaaS) is a new concept developed within the framework of the NEWTON Horizon 2020 project. It is aimed at empowering digital fabrication laboratories (Fab Labs) by providing hardware and software wrappers to expose numerically-controlled expensive fabrication equipment as web services. More specifically, FaaS leverages cloud and IoT technologies to enable a wide learning community to have remote access to these labs’ computer-controlled tools and equipment over the Internet. In such context, the fabrication machines can be seen as networked resources distributed over a wide geographical area. These resources can communicate through machine-to-machine protocols and a centralized cloud infrastructure and can be digitally monitored and controlled through programmatic interfaces relying on REST APIs. This paper introduces FaaS in the context of Fab Lab challenges and describes FaaS deployment within NEWTON Fab Labs, part of the NEWTON European Horizon 2020 project on technology enhanced learning. The NEWTON Fab Labs architecture is described in detail targeting software, hardware and network architecture. The system has been extensively load-tested simulating real use-case scenarios and it is presently in production. In particular, this paper shows how the measured data has been used to build a simulation model to estimate system performance and identify possible bottlenecks. The measurements performed show that the platform delays exhibit a tail distribution with Pareto-like behaviour; this finding has been used to build a simple mathematical model and a simulator on top of CloudSim to estimate the latencies of the critical paths of the NEWTON Fab Lab platform under several load conditions.

Recently, vehicular sensor networks (VSNs) have emerged as a new intelligent transport networking paradigm in the Internet of Things. By sensing, collecting, and delivering traffic-related information, VSNs can significantly improve both driving experience and traffic flow control, especially in constrained urban environments. Latest technological advances enable vehicular devices to be equipped with multiple wireless interfaces, which can support cooperative communications for concurrent multipath transfer (CMT) in VSNs. However, path heterogeneity and vehicle mobilitycauseCMT not to achieve the same high transport efficiency recorded in wired nonmobile network environments. This paper proposes a novel vehicular network-based CMT solution (VN-CMT) to address the above issues and improve data delivery efficiency. VN-CMT is based on a CMT disorder analytic model which can effectively and accurately evaluate the degree of out-of-order data. Based on this proposed model, a series of mechanisms are introduced as follows: (1) a packet disorder-reducing retransmission policy to reduce retransmission delay; (2) a path group selection algorithm to find the best path set for data multipath concurrent transfer; and (3) a data scheduling mechanism to distribute data according to each path's capacity. Simulation results show how VN-CMT improves data delivery efficiency in comparison with an existing state-of-the-art solution.