Background: Most adults do not engage in sufficient physical activity to maintain good health. Smartphone apps are increasingly used to support physical activity but typically focus on tracking behaviors with no support for the complex process of behavior change. Tracking features do not engage all users, and apps could better reach their targets by engaging users in reflecting their reasons, capabilities, and opportunities to change. Motivational interviewing supports this active engagement in self-reflection and self-regulation by fostering psychological needs proposed by the self-determination theory (ie, autonomy, competence, and relatedness). However, it is unknown whether digitalized motivational interviewing in a smartphone app engages users in this process. Objective: This study aimed to describe the theory- and evidence-based development of the Precious app and to examine how digitalized motivational interviewing using a smartphone app engages users in the behavior change process. Specifically, we aimed to determine if use of the Precious app elicits change talk in participants and how they perceive autonomy support in the app. Methods: A multidisciplinary team built the Precious app to support engagement in the behavior change process. The Precious app targets reflective processes with motivational interviewing and spontaneous processes with gamified tools, and builds on the principles of self-determination theory and control theory by using 7 relational techniques and 12 behavior change techniques. The feasibility of the app was tested among 12 adults, who were asked to interact with the prototype and think aloud. Semistructured interviews allowed participants to extend their statements. Participants’ interactions with the app were video recorded, transcribed, and analyzed with deductive thematic analysis to identify the theoretical themes related to autonomy support and change talk. Results: Participants valued the autonomy supportive features in the Precious app (eg, freedom to pursue personally relevant goals and receive tailored feedback). We identified the following five themes based on the theory-based theme autonomy support: valuing the chance to choose, concern about lack of autonomy, expecting controlling features, autonomous goals, and autonomy supportive feedback. The motivational interviewing features actively engaged participants in reflecting their outcome goals and reasons for activity, producing several types of change talk and very little sustain talk. The types of change talk identified were desire, need, reasons, ability, commitment, and taking steps toward change. Conclusions: The Precious app takes a unique approach to engage users in the behavior change process by targeting both reflective and spontaneous processes. It allows motivational interviewing in a mobile form, supports psychological needs with relational techniques, and targets intrinsic motivation with gamified elements. The motivational interviewing approach shows promise, but the impact of its interactive features and tailored feedback needs to be studied over time. The Precious app is undergoing testing in a series of n-of-1 randomized controlled trials.

Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability.

This paper proposes a solution that allows home hosted content, located on home PCs or UPnP media servers, to be offered to remote clients, for consumption. The system is based on a proxy residing on the home network that exports the multimedia content to a Web syndication feed following the Atom protocol, which is then accessed from compatible remote clients. A prototype of the system was built, with the proxy functionality implemented on a home PC and the remote clients running on mobile phones. Special attention has been paid to the social aspects of content sharing, for allowing the home owner to also invite other external users to remotely access specific, home-based media containers, with very simple and secure device configuration.

This paper presents the Italian pilot on Licensed Shared Access (LSA) in the IMT Band 40 to explore the potential and logistics of spectrum sharing from a regulatory perspective. Organised on a small cell / micro cell scale, it verified both the technical feasibility and regulatory compliance of LSA in a real, LTE-TDD network, constrained by the national spectrum use in Band 40. The LSA system was implemented in compliance with the CEPT, ETSI and 3GPP specifications. We present an innovative distributed architecture implemented across three countries, enabling certain features of 5G networks. We describe the sharing framework and sharing conditions in the Italian scenario for the use cases representing actual incumbents. These were verified through in-field measurements, key results of which are presented. Measurement results show that sharing based on LSA is feasible to provide additional capacity for mobile broadband services in the 2300-2400 MHz band without detriment to incumbent services. Use of commercial off-the-shelf equipment to create new mobile broadband service in an urban indoor / outdoor environment, pilot enabled us to assess the spectrum sharing opportunities through LSA technology for general deployment today and towards future policy for 5G spectrum in Italy.

Robotic teleoperation has seen widespread adoption across industries. Advances in technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Extended Reality (XR) are making teleoperation more flexible. The integration of visual, audio, tactile, and immersive interfaces enhances situational awareness, enabling teleoperators to interact effectively with complex remote environments and make informed decisions. However, challenges persist, particularly in environments characterized by constrained network resources. The limited bandwidth, delays, and intermittent communication can disrupt the teleoperator’s interaction with the robot. This study aims to comprehensively understand the scenarios of industrial ground robotic teleoperation and the intricacies of its network to effectively enhance teleoperation performance. Initially, we introduce the multimodal perception-enhanced robotic teleoperation, accompanied by an analysis of the Quality of Service (QoS) of all involved streams. Subsequently, we introduce a Deterministic Robotic Teleoperation (Det-RT) system, along with a deterministic traffic flow scheduling framework designed for real-time remote environment perception. Finally, we evaluate the proposed scheduling solution in a simulated environment to assess its performance within the Det-RT system. The results obtained demonstrate the capability of our solution to deliver high-quality teleoperation performance.

Missing sensor data in human activity recognition is an active field of research that is being targeted with generative models for synthetic data generation. In contrast to most previous approaches, we aim to generate data of a sensor exclusively from data available at sensors in different body locations. Particularly, we evaluate existing approaches proposed in the literature for their suitability in this scenario. In this paper, we focus on the prediction of acceleration data and generate machine learning models based on generative adversarial networks and trained using correlated data from sensors in different body positions to generate synthetic sensor data that can replace the missing data from a sensor in a specific body position. The accuracy of the generated synthetic data is evaluated using a classification model based on a convolutional neural network for human activity recognition.