Multicast video streaming over multirate wireless LANs imposes strong demands on video codecs and the underlying network. It is not sufficient that only the video codec or only the underlying protocols adapt to changes in the wireless link quality. Research efforts should be applied in both and in a synchronized way. Cross layer design is a new paradigm that addresses this challenge by optimizing communication network architectures across traditional layer boundaries. This paper presents cross-layer architecture for adaptive video multicast streaming over multirate wireless LANs where layer-specific information is passed in both directions, top-down and bottom-up. The authors jointly consider three layers of the protocol stack: the application, data link and physical layers. The authors analyze the performance of the proposed architecture and extensively evaluate it via simulations. The results show that the real-time video quality of the overall system can be greatly improved by cross-layer signaling.

In the last few years, in an attempt to further motivate students to learn a foreign language, there has been an increasing interest in task-based teaching techniques, which emphasize communication and the practical use of language, thus moving away from the repetitive grammar-translation methods. Within this approach, the significance of situating foreign language learners in scenarios where they can meaningfully learn has become a major priority for many educators. This approach is particularly relevant in the context of teaching foreign languages to young children, who need to be introduced to a new language by means of very concrete vocabulary, which is facilitated by the use of objects that they can handle and see. In this study, we investigate the benefits of using wearable and Internet-of-Things (IoT) technologies in streamlining the creation of such realistic task-based language learning scenarios. We show that the use of these technologies will prove beneficial by freeing the instructors of having to keep records of the tasks performed by each student during the class session. Instead, instructors can focus their efforts on creating a friendly environment and encouraging students to participate. Our study sets up a basis for showing the great benefits of using wearable and IoT technologies in streamlining 1) the creation of realistic scenarios in which young foreign language learners can feel comfortable engaging in chat and becoming better prepared for social interaction in a foreign language, and 2) the acquisition and processing of performance metrics.

Increasing school dropout rates are a problem in many educational systems, with student disengagement being one significant factor. Learning analytics (LA) is a new field with a key role in educational institutions in the coming years. It may help make strategic decisions to reduce student disengagement. The use of technology in educational environments has grown significantly and, with it, awareness of the importance of student engagement. We exploit tracking and wearable technologies to increase user engagement in learning processes, exploring also the area of multi modal learning analytics (MMLA). We use WIoTED (wearables and Internet of Things for education), an interactive and collaborative system designed to improve motivation and learning. This paper presents the results obtained in different experiments conducted in a secondary school in a long-term participatory learning context. The captured data were analyzed and used to identify different students' behavior patterns, showing their progress and motivation. Subsequently, from the captured data, and aiming at a decision-making phase, we used machine learning techniques and MMLA methodologies to construct models able to 'explain' when student engagement is present, so this knowledge can later be exploited. In particular, we chose decision trees and rule systems based on a set of variables with proven relevance to the problem. The evaluation of this novel engagement classification system confirms the high performance of these variables. The rules obtained, which can be easily interpreted by a non-expert, help the teacher to observe, analyze and make decisions with the purpose of fostering engagement.

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.