5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information.

To deal with recent increasing mobile traffic, ultra-broadband communication with millimeter-wave (mmWave) has been regarded as a key technology for 5G cellular networks. In a previous study, a mmWave heterogeneous network was composed of several mmWave small cells overlaid on the coverage of a macro cell. However, as seen from the optical fiber penetration rate worldwide, it is difficult to say that backhaul with Gbps order is available everywhere. In the case of using mmWave access under a limited backhaul capacity, it becomes a bottleneck at the backhaul; thus, mmWave access cannot fully demonstrate its potential. On the other hand, the concept of multi-access edge computing (MEC) has been proposed to decrease the response latency compared to cloud computing by deploying storage and computation resources to the user side of mobile networks. This paper introduces MEC into mmWave heterogeneous networks and proposes a content prefetching algorithm to resolve such backhaul issues. Context information, such as the destination, mobility, and traffic tendency, is shared through the macro cell to the prefetch application and data that the users request. Prefetched data is stored in the MEC and then transmitted via mmWave without a backhaul bottleneck. The effectiveness is verified through computer simulations where we implement realistic user mobility as well as traffic and backhauling models. The results show that the proposed framework achieved 95% system capacity even under the constraint of a 1 Gbps backhaul link.

Supporting Patient-Clinician Interaction in Chronic HIV Care: Design and Development of a Patient-Reported Outcomes Software Application.

BACKGROUND: The consideration of health-related quality of life (HRQL) is a hallmark of best practice in HIV care. Information technology offers an opportunity to more closely engage patients with chronic HIV infection in their long-term management and support a focus on HRQL. However, the implementation of patient-reported outcome (PRO) measures, such as HRQL in routine care, is challenged by the need to synthesize data generated by questionnaires, the complexity of collecting data between patient visits, and the integration of results into clinical decision-making processes. OBJECTIVE: Our aim is to design and pilot-test a multimedia software platform to overcome these challenges and provide a vehicle to increase focus on HRQL issues in HIV management. METHODS: A multidisciplinary team in France and Australia conducted the study with 120 patients and 16 doctors contributing to the design and development of the software. We used agile development principles, user-centered design, and qualitative research methods to develop and pilot the software platform. We developed a prototype application to determine the acceptability of the software and piloted the final version with 41 Australian and 19 French residents using 2 validated electronic questionnaires, the Depression, Anxiety and Stress Scale-21 Items, and the Patient Reported Outcomes Quality of Life-HIV. RESULTS: Testing of the prototype demonstrated that patients wanted an application that was intuitive and without excessive instruction, so it felt effortless to use, as well as secure and discreet. Clinicians wanted the PRO data synthesized, presented clearly and succinctly, and clinically actionable. Safety concerns for patients and clinicians included confidentiality, and the potential for breakdown in communication if insufficient user training was not provided. The final product, piloted with patients from both countries, showed that most respondents found the application easy to use and comprehend. The usability testing survey administered found that older Australians had reduced scores for understanding the visual interface (P=.004) and finding the buttons organized (P=.02). Three-fourths of the respondents were concerned with confidentiality (P=.007), and this result was more prevalent in participants with higher anxiety and stress scores (P=.01), as measured by the Depression, Anxiety and Stress Scale-21 Items. These statistical associations were not observed in 15 French patients who completed the same questionnaire. CONCLUSIONS: Digital applications in health care should be safe and fit for purpose. Our software was acceptable to patients and shows potential to overcome some barriers to the implementation of PROs in routine care. The design of the clinicians' interface presents a solution to the problem of voluminous data, both synthesizing and providing a snapshot of longitudinal data. The next stage is to conduct a randomized controlled trial to determine whether patients experience increased satisfaction with care and whether doctors perceive that they deliver better clinical care without compromising efficiency.

The role of user context in the design of mobile map applications.

Mobile map applications are typically used by a broad range of users. Users can be diverse in their context attributes (e.g. map use experience, activities during map use), and several previous user experience (UX) studies have focused on understanding how some contextual factors influence the UX for designing maps that satisfy users' needs. A need for research remains to evaluate the relationship between user context, UX, and variants of mobile map element design. In this article, we present our research investigating the interplay of these factors through an empirical user study with citizens in Austria. We created an online survey and generated 84 map variations, combining seven map-related tasks, three base map styles, two map detail densities, and two time-pressure variants. We tested these map variations with 107 survey participants and related their UX to user context. Map-related tasks emerged as a dominant factor modifying the map design UX. Further results showed that interactivity loaded map-related tasks were aided when paired with low detail-dense base maps, contrasting overlay features. We recommend future research to analyze an extended set of context attributes with additional participant data, to evaluate dynamic variations in context, and to find ways to dynamically monitor mobile map design UX.

ContextLabeler dataset: Physical and virtual sensors data collected from smartphone usage in-the-wild.

This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment.

Architecture and User-Context Models of CoCare: A Context-Aware Mobile Recommender System for Health Promotion.

Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user's environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobile recommender system for the promotion of healthy habits. The system is adapted to the user's needs, his/her health information, interests, time, location and lifestyles. In this paper, the CARS computational architecture and the user and context models of health promotion are presented, which were used to implement and test a prototype recommender system.