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For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (UK) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72\% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

Introduction Moving away from face-to-face (F2F) to virtual laboratory (VL) has impacted our teaching practices and students experience. With increasing numbers of students on stay home notices due to health risk alerts and warnings, online blended learning has been implemented for physical laboratory sessions, whenever the circumstances permit. In these situations, achieving the learning outcomes has become more challenging. It has been reported (Philips and Goryll, 2020) that the remote and VL teaching has often resulted in the loss of mentorship, community, peer motivation, and overall student engagement has been diminished. These challenges lead us to investigate how we can motivate our students and increase their engagement in VLs to achieve the learning outcomes. This study intends to provide practical answers to this question by identifying what aspects of the VL session increase students��� engagement levels. A survey on 150 students��� engagement is conducted on the virtual electronics laboratories, designed for students in their first and second year of studies, respectively. The VLs have been designed to enable students to: 1. work in teams and have peer discussions to develop critical thinking. 2. use computer-based simulations to facilitate the application of theoretical concepts. 3. use equipment to conduct experimental design activities and provide hands-on experience. MethodsTo evaluate student engagement, a survey was designed using concepts of emotional and cognitive engagement (Fredricks et al., 2004) and Self-Determination Theory (SDT) (Ryan and Deci, 2017). A growing body of evidence indicates that emotional engagement positively influences cognitive engagement. Therefore, an essential aspect of developing VLs sessions will be incorporating elements of emotional engagement to ensure that cognitive engagement is achieved. SDT offers a framework for understanding the conditions under which students are likely to become emotionally engaged in their work. These are three psychological needs: Autonomy, the need to feel ownership of one���s behaviour (choice), Competence, the need to produce desired outcomes and to experience mastery (confidence), and Relatedness, the need to feel connected to others (social presence). The survey will be carried out in two parts: a pre-session in Week 7 and a post-sessional questionnaire concluded in Week 10. The objectives are: (1) to measure the students��� perception on whether the learning outcomes have been achieved. (2) to obtain insights regarding cognitive and emotional engagement, preferred working formats in laboratories, and advantages of collaborating in groups. The pre-sessional questionnaire, based on F2F laboratory experience, intends to obtain a baseline measure of engagement levels and achievement of learning outcomes that the student perceives. In the post-sessional questionnaire, the objective is to re-evaluate students��� engagement levels to determine any change in the engagement levels of the VLs and what elements of the new setting impacted the achievement of learning outcomes. ResultsWe have analysed the response of the pre-sessional survey using multivariate analysis. The results show that 88% students with a high level of cognitive engagement agree or strongly agree that the following two learning outcomes are important: (1) the laboratory improves their critical thinking and (2) the ability to identify the strengths and limitations of theoretical models as predictors of natural physical behaviours. Furthermore, the analysis shows that working in groups facilitates achieving these objectives, and the 89% students also agree or strongly agree that relatedness (social presence) is the most important of the three SDT needs. Finally, we plan to conduct the post-sessional survey to measure the impact of the VLs and present the complete analysis in the final version of this work. Preliminary conclusion The study will measure the impact of VL on student engagement and identify which learning activities of the VLs best influence emotional and cognitive engagement.

For an engineering course to be effective and acceptable to industry it must be continually revised to incorporate new technology and the latest thinking. Traditionally, the method used was to approach companies in the immediate locale of the teaching institution and elicit membership to form an industrial advisory board (IAB). The IAB had limited power and influence over activities within the school and was most prominent in ensuring the course obtained accreditation from an appropriate and relevant body. However, as industry transitioned first into international and then global companies, the geographically centric advisory board became a limitation; the original strengths and benefits of engaging with local companies were outweighed by the need to embrace cultural differences. Furthermore, as system and product integration extended beyond the single engineering disciplines, the membership of the advisory board had to be similarly extended. The most recent illustration of this is the blurring of the boundaries between Computer Science and Electronic Engineering in the emergent market for the Internet of Things. With the advent of trans-national joint degree programmes between Universities in separate continents, the problem is exacerbated. This paper looks at the challenges and approaches taken to address some of these issues when establishing an Industrial Advisory Board that is not only international, but is also multi-cultural and multi-discipline in support of a joint undergraduate degree programme in Electronic and Electrical Engineering in China. It proposes a balance between domestic versus international organisations to ensure that the educational content delivers the widest possible opportunities to the students. Finally, it examines how an effective advisory board can assist the academic staff in delivering the most appropriate, up to date training possible through the utilisation of members of the Advisory Board. Using this approach, the School can deliver best-in-class, up-to-date knowledge and experience from an industrial perspective, combined with traditional teaching of the fundamental disciplines. By involving the Industrial Advisory Board, both students and industrialists get a richer, more relevant experience and interaction.

The Internet of Things (IoT) has come to mean all\ud things to all people. Combined with the huge amount of interest\ud and investment into this emerging opportunity, there is a real\ud possibility that the arising confusion will hamper adoption by\ud the mass market.\ud This paper proposes a phased model of the IoT ecosystem\ud development, starting with infrastructure establishment, and\ud culminating in exploitation through the creation of new\ud companies and business models. It does not attempt to quantify\ud the emerging opportunities, relying instead on the many\ud publications dedicated to detailed market analysis. The focus is\ud to place the opportunities in context, demonstrate the\ud importance of sensor system technology underpinning the\ud emerging IoT revolution, and suggests areas where Europe can\ud establish a leadership position. Throughout the paper, examples\ud of the likely protagonists have been used by way of illustration.

The Internet of Things (IoT) has come to mean all things to all people. Combined with the huge amount of interest and investment into this emerging opportunity, there is a real possibility that the arising confusion will hamper adoption by the mass market. The SILC team have used their extensive Sensor Systems market and technical knowledge in an attempt to clarify the situation for individuals interested in understanding IoT, and the underpinning role of Sensor Systems.\ud This paper proposes a phased model of the IoT ecosystem, starting with infrastructure establishment, and culminating in exploitation through the creation of new companies and business models. It does not attempt to quantify the emerging opportunities, relying instead on the many publications dedicated to detailed market analysis. The focus is to place the opportunities in context, demonstrate the importance of sensor system technology underpinning the emerging IoT revolution, and suggests areas where the UK could establish leadership positions. Throughout the paper, examples of the likely protagonists have been used by way of illustration.

The concept of a reference model has been well known in the communications\ud industry for many years. This has provided clearly defined layers and interface standards\ud which allow different organisations and across the industry to develop products independent\ud of each other in the knowledge that their products will function correctly within the greater\ud system. With the move towards massively parallel sensor systems networks, a similar\ud approach needs to be adopted in order to permit concurrent developments in sensor\ud networks. This paper summarises the need for such a reference model, and proposes a\ud reference model applicable to sensor system.

The excitement and hype surrounding the emerging technology market known as the Internet of Things\ud (IoT) has attracted a great deal of attention from industry, business leaders, and governments alike.\ud According to Cisco estimates1, the IoT will create a $14 Trillion global opportunity, and Business Insider\ud predict it will be the largest market for technology hardware ever 2. If these are correct, the impact on\ud traditional business models through the adoption of new technology within industry, the service sector\ud and governments will be transformational. At an individual and societal level, important areas such as\ud health care and aging population will particularly benefit from IoT. The changes on tomorrow’s society\ud can only be imagined but there is little doubt, whether individually, societally, economically, or\ud industrially, these changes will be far reaching.\ud However, the promise of future riches can fool the unwary into overlooking the jewels that already exist\ud which are delivering benefit to the UK economy today; whose presence will be pivotal to the UK’s position\ud in the IoT market of tomorrow. In the SILC white paper ‘The IoT Tree of Life’3, the 4 foundation roots from\ud which the IoT will develop are: Low Power Processing, Miniature Sensors, Ubiquitous Wireless, and\ud Power Efficiency. This briefing paper explores Sensor Systems, the ‘eyes and ears’ of the IoT, and\ud elucidates how the existing UK strengths in this area can be leveraged to deliver competitive advantage to\ud the UK economy in the emerging IoT market.