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This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows' performance by applying an Machine Learning (ML) approach to evaluate a set of features -such as latency and throughputfor every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a "low-level" application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient.

Peer-to-Peer (P2P), Transactive Energy (TE) and Community Self-Consumption (CSC) are exciting energy generation and use models, offering several opportunities for prosumers, micro-grids and services to the grid; however, they require numerous components to function efficiently. Various hardware devices are required to transmit data and control the generation and consumption equipment, whereas software is needed to use the gathered information to monitor and manage the hardware and energy trading. Data can be gathered from a variety of origins from within the grid and external sources; however, these data must be well-structured and consistent to be useful. This paper sets out to gather information regarding the hardware, software and data from the several archetypes available, focusing on existing projects and trials in these areas to see what the most-common hardware, software and data components are. The result presents a concise overview of the hardware, software and data-related topics and structures within the P2P, TE and CSC energy generation and use models.

This paper describes and evaluates an Innovative Algorithm for Improved Quality Multipath Delivery of Virtual Reality Content (QM4VR) that addresses the stringent communication requirements of Virtual Reality (VR) applications. Making use of the Multipath TCP (MPTCP) built-in multipath delivery features (subflows), QM4VR explores the subflows' characteristics, evaluates their performance (e.g., delay, throughput or loss) and proposes a new management scheme to improve the Quality of Service (QOS) of the VR applications. glsqm4vr adopts a Machine Learning (ML)-based approach to evaluate the subflows' performance which is implemented in two steps: 1) a linear regression scheme to forecast the subflow's performance for a given feature; and 2) a linear classification scheme to arrange the results obtained in step 1. Based on these results QM4VR selects the most appropriate subflows for data delivery in order to achieve improvement of VR QOS levels.