The increasing use of mobile technologies in healthcare has driven significant advancements in patient care management while raising critical concerns about data security and privacy. This study addresses the challenges and solutions for ensuring security and privacy in mobile health applications, emphasizing the importance of integrating Privacy by Design (PbD) principles from the development phase. By implementing a framework in Flutter, the proposed approach focuses on safeguarding sensitive data through measures such as screenshot prevention and data encryption, ensuring strict compliance with regulations like the General Data Protection Law (LGPD). The results demonstrate that adopting PbD not only meets legal requirements but also strengthens user trust by effectively protecting personal data. The proposed framework establishes a new standard for developing mobile health applications, ensuring that security and privacy are integral throughout the entire design and operational process.

This paper introduces Dynamic Bayesian Optimisation for Multi-Arm Bandits (DBO-MAB), an algorithm that dynamically adapts hyperparameters of multi-arm bandit algorithms using incremental Bayesian optimisation. DBO-MAB addresses the challenge of tuning hyperparameters in uncertain and dynamic environments, particularly for applications like web server optimisation. It uses a dynamic range adjustment approach based on the interquartile mean (IQM) of observed rewards to focus the search space on promising regions. Evaluated across diverse static and dynamic environments, DBO-MAB outperforms state-of-the-art algorithms such as Bootstrapped UCB and f-Discounted-Sliding-Window Thompson Sampling, reducing average response time by ≈55%.

One key challenge in Adaptive Video Streaming is the ever-changing edge network conditions at the last mile of access networks. The edge environment is particularly dynamic, influenced by user locations, fluctuation in resource demands and resource capabilities, in contrast to traditional Content Delivery Network (CDN) setups, where content routing decisions are relatively known. To address the dynamism of edge computing environments and to enable applications to better exploit edge-cloud computing resources, this article focuses on content steering technology, a recent addition to adaptive video protocols such as HLS and DASH. We present AVENUE as an architecture for Content Steering Services to orchestrate video delivery dynamically across the Edge-Cloud Continuum. This work designs the principles of the Content Steering Service to create a mechanism that involves two modules - monitoring and selector: The monitoring module captures real-time context metrics, and the selector module chooses an edge server according to the Select Server Algorithm. Our study addresses three steering algorithms with different performance profiles. Numerical results demonstrate that different configurations may yield varying network performance in terms of Quality of Experience (QoE), cache hits, and request load. Moreover, the appropriate selection of heuristics in the Selector module can also have a significant impact, depending on the metric being evaluated.

The investigation of stateful service mobility across networking infrastructures is becoming increasingly important as applications require stateful services capable of migrating from centralized cloud data centers to edge computing infrastructures. State-of-the-art approaches propose either machine learning solutions for stateless service placement or stateful service mobility using static and inflexible state management strategies. We believe these approaches fall short of addressing the full length of the stateful service mobility problem. In this paper, we revisit an emerging concept named self-distributing systems, where a local executing application manages to detach some of its constituent (often stateful) components and place them in remote machines as a solution for stateful service mobility. In previous work, a machine learning approach to support self-distributing systems has not been thoroughly investigated. We model the distribution of stateful components across networking infrastructures as a multi-armed bandits problem and use the UCB1 algorithm to solve it as a first attempt at a flexible solution for stateful service mobility. We conclude the paper by discussing the main challenges and opportunities in this area.

Applications such as autonomous vehicles, virtual reality, augmented reality, and heavy machine learning-based applications are becoming popular and demanding more flexible deployment environments. The computing continuum, a hierarchical hybrid infrastructure comprehending user devices (smartphones, sensors, laptops, etc.), edge data centers, and cloud platforms, offers a wide range of deployment possibilities with a full range of varying computing resources. To take full advantage of such infrastructure, application development is faced with many challenges, the most important being the implementation of a transparent and generalized mechanism for code offloading and mobility throughout the continuum. To tackle such issues, this paper presents the Self-Distributing Systems (SDS) framework, a self-distribution framework that supports generalized code-offloading capabilities at the application level with a machine learning agent for deciding where to place components and a component-based model to enable seamless distribution of an application's components at runtime. We describe the framework, show its applicability in different application scenarios, and report our preliminary results. We conclude the paper with a list of challenges and invite the systems community to join the effort to further investigate them.

Contemporary deployment environments are volatile, with conditions that are often hard to predict in advance, demanding solutions that are able to learn how best to design a system at runtime from a set of available alternatives. While the self-adaptive systems community has devoted significant attention to online learning, there is less research specifically directed towards learning for open-ended architectural adaptation - where individual components represent alternatives that can be added and removed dynamically. In this paper we present the Emergent Web Server (EWS), an architecture-based adaptive web server with 42 unique compositions of alternative components that present different utility when subjected to different workload patterns. This artefact allows the exploration of online learning techniques that are specifically able to consider the composition of logic that comprises a given system, and how each piece of logic contributes to overall utility. It also allows the user to add new components at runtime (and so produce new composition options), and to remove existing components; both are likely to occur in systems where developers (or automated code generators) deploy new code on a continuous basis and identify code which has never performed well. Our exemplar bundles together a fully-functional web server, a number of pre-packaged online learning approaches, and utilities to integrate, evaluate, and compare new online learning approaches.

Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online.