Advances in technology are not only changing the world around us but also driving the wireless industry to develop the next generation of network technology. There is a lot of buzz building over the advent of 5G that will facilitate the entire planet through continuous and ubiquitous communication connecting anybody to anything, anywhere, anytime, and anyhow regardless of the device, service, network, or geographical existence. 5G will also prove to be a paradigm shift including high carrier frequencies with massive bandwidths, having a large number of antennas, and with an extreme base station and device densities. In this paper, we investigate the potential beneficiaries of 5G and identify the use-cases, where 5G can make an impact. In particular, we consider three main use-cases: vehicle-to-everything (V2X) communication, drones, and healthcare. We explore and highlight the problems and deficiencies of current cellular technologies with respect to these use-cases and identify how 5G will overcome those deficiencies. We also identified the open research problems and provide possible future directions to cope with those issues. 5G, V2X communication, drones, healthcare, ultra-low-latency, ultra-high-reliability. INDEX TERMS

In this paper we present a new gamified learning system called Reflex which builds on our previous research, placing greater emphasis on variation in learner motivation and associated behaviour; having a particular focus on gamification typologies. Reflex comprises a browser based 3D virtual world that embeds both learning content and learner feedback. In this way the topography of the virtual world plays an important part in the presentation and access to learning material and learner feedback. Reflex presents information to learners based on their curriculum learning objectives and tracks their movement and interactions within the world. A core aspect of Reflex is its gamification design, with our engagement elements and processes based on Marczewski's eight gamification types [1]. We describe his model and its relationship to Bartle's player types [2] as well as the RAMP intrinsic motivation model [3]. We go on to present an analysis of experiments using Reflex with students on two 2nd year Computing modules. Our data mining and cluster analysis on the results of a gamification typology questionnaire expose variation in learner motivation. The results from a comprehensive tracking of the interactions of learners within Reflex are discussed and the acquired tracking data is discussed in context of gamification typologies and metacognitive tendencies of the learners. We discuss correlations in actual learner behaviour to that predicted by gamified learner profile. Our results illustrate the importance of taking variation in learner motivation into account when designing gamified learning systems.

With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.

This paper presents an approach to improve protocol stack aims to use such information in advance to transmission success in delay-tolerant networks. The Contextremove the need for human understanding of contextual Aware Broker (CAB) grants networking autonomy when information, and to automate the process when communicating communicating in challenging environments, which suffer from in extreme environments. conditions which are variable and exceed the limits for which Context-aware configuration of the protocol stack is terrestrial protocols were designed. Such environments currently important where real-time decisions must be made. Our require human intervention and the manual configuration of research involves an anticipated deep space scenario; here, we each communicationa seemingly simple decision of when to envisage network congestion, more than one communication transmit becomes an issue in deep space due to planet movement. .' However, manual configuration is becoming unrealistic, given the route,d a human presenc aineeper sac M da flows ,ust scale on which communications occur. CAB automates the couple with l itedntwor arcitu diadesireeto JUSt process by making intelligent decisions before transmission push out a signal without prior planning dictates a need for begins, and reconfigures as it progresses. It recognises the real-time decision-making. dynamic environments through which a transmission may pass II. CURRENT USE OF CONTEXT-AWARENESS IN and matches protocol capabilities with environmental DTN constraints. There are several autonomic missions currently under Index Terms-Context-awareness, Autonomy, Delay-Tolerant development [ 3 ], and NASA's Autonomic Computing Networking (DTN), Interplanetary backbone, Quality of Service initiative will officially be launched between 2020 and 2030 (QoS) [4]. These missions typically use a number of individual I. INTRODUCTION components which communicate with each other, and reconfigure using real-time information. Due to the number of Autonomy empowers the network to be fully responsible components and decisions, their location, and the need for for communication decisions, thus removing the need for immediate reaction to unexpected events and real-time human intelligence and intervention. This istbecoming configuration, human control of the mission is not an option. increasingly important to allow boundaries restricting Context-awareness which enables autonomic decision-making networking efforts in the 21St Century to be overcome. The is therefore empowering a new type of independent mission. most extreme environment is deep space, where long and In addition to the development of autonomic missions at

Policy-based management allows the deployment of networks offering quality services in environments beyond the reach of real-time human control. A policy-based protocol stack middleware, the context-aware broker, has been developed by the authors to autonomically manage the remote deep space network. In this article example policy rules demonstrate the concept, and prototype results from ns-2.30 show the overall positive costbenefit impact in an example scenario.