Distributing microservices across the Computing Continuum reduces latency and preserves data locality but introduces management complexity on heterogeneous, resource-constrained edge nodes. Traditional reactive orchestration triggers only after saturation occurs. Under bursty or high-density workloads, this latency leads to service degradation, instability, and inefficient energy usage. To address this, the Adaptive Resource-Aware Predictive Orchestrator (ARAPO) couples per-service local forecasting with calibrated node-level aggregation. It employs a dual-threshold policy based on predicted and observed load to trigger migrations. It maps CPU forecasts to power for energy-aware placement without external instrumentation. ARAPO is evaluated in a realistic hospital reference scenario against a reactive-only baseline. Results demonstrate that the system anticipates saturation and prevents control plane congestion. It significantly improves stability in oscillating workloads. Overload time drops from 28.4% to 4.5%. Consequently, energy usage during overload falls to 14.9% of the reactive baseline. Node-level forecasting achieves R 2 up to 0.86. The power model tracks consumption with a mean absolute error as low as 0 . 40 W . This validates its suitability as a lightweight, energy-efficient controller.

The increasing complexity of IoT applications and the continuous growth in data generated by connected devices have led to significant challenges in managing resources and meeting performance requirements in computing continuum architectures. Traditional cloud solutions struggle to handle the dynamic nature of these environments, where both infrastructure demands and data analytics requirements can fluctuate rapidly. As a result, there is a need for more adaptable and intelligent resource management solutions that can respond to these changes in real-time. This paper introduces a framework based on multi-dimensional elasticity, which enables the adaptive management of both infrastructure resources and data analytics requirements. The framework leverages an orchestrator capable of dynamically adjusting architecture resources such as CPU, memory, or bandwidth and modulating data analytics requirements, including coverage, sample, and freshness. The framework has been evaluated, demonstrating the impact of varying data analytics requirements on system performance and the orchestrator's effectiveness in maintaining a balanced and optimized system, ensuring efficient operation across edge and head nodes.

Smart cities have deployed a myriad of devices to sense the status of the city and its citizens in order to reconfigure different elements to improve the quality of life. However, the analysis and reconfiguration of these elements on the fly can lead to unforeseen problems. The digital twin paradigm has risen as a promising technology to analyze and test these re-configurations before their execution. These digital twins are usually centralized in the cloud. However, the emulation of highly distributed systems can lead to scalability, response time, and security and privacy problems. In this paper, we propose a hierarchical and distributed architecture for digital twins, deployed over the Cloud-to-Thing Continuum. The proposal is illustrated by means of a case study about public transportation in smart cities.

The increasing capabilities of mobile devices have led to the emergence of new paradigms exploiting them. These paradigms foster the onload and distribution of functionalities on mobile devices, allowing the development of distributed mobile applications. This distribution reduces the latency and the data traffic overhead and improves privacy. As in any other mobile application, their success largely depends on the quality of service (QoS) they offer. Nevertheless, the evaluation of distributed mobile applications is particularly complex due to the number, heterogeneity, and interactions between the devices involved. Current techniques allow developers to assess the quality of a single device, but they are not designed for highly heterogeneous, distributed, and collaborative environments. This paper presents a framework called Perses, which allows the creation of virtual scenarios with multiple heterogeneous mobile devices to launch end-to-end tests to evaluate not only each device but also the interactions among them. The framework was evaluated against a real deployment, showing that the behavior and the quality attributes measured are similar to those of the real deployment, allowing developers to evaluate these applications before launching them. Finally, Perses was integrated into a DevOps methodology to automate its execution and further facilitate its adoption by software companies.

Summary During the last decade, the mobile application market has grown steadily thanks to the massive use of smartphones and the emergence of cloud computing for offloading computation tasks and improving the quality of experience. With the more recent deployment of Internet of Things (IoT) devices, this cloud‐based architectural design and the corresponding communication flow has been maintained. Nevertheless, the increasing amount of information exchanged, the stringent requirements of many IoT applications, and the need for these applications to adapt their behavior in real time to the user's context set these architectural assumptions a challenge. Paradigms such as mobile, mist, and edge computing have recently been proposed to exploit the computational and storage capabilities of current smartphones and IoT devices in order to onload some tasks onto them, reducing the overhead on both the cloud and the network. Currently, the application of these paradigms requires much attention from skilled developers to create ad hoc systems, as there lack standards and tools facilitating their use. This communication introduces Human Microservices as a framework facilitating the deployment of APIs on companion devices in order to provide personal and updated information that can be consumed by other entities. The framework improves the integration of humans in the IoT loop and facilitates the deployment of computation units in devices closer to end users, enhancing system response time by reducing the stress on cloud and network infrastructure. The proposed framework is based on existing standards in order to improve software quality and shorten the learning curve.

Many rural areas along Spain do not have access to the Internet. Despite the huge spread of technology that has taken place during recent years, some rural districts and isolated villages have a lack of proper communication infrastructures. Moreover, these areas and the connected regions are notably experiencing a technological gap. As a consequence of this, the implementation of technological health solutions becomes impracticable in these zones where demographic conditions are especially particular. Thus, inhabitants over 65 suppose a large portion of such population, and many elderly people live alone at their homes. These circumstances also impact on local businesses which are widely related to the agricultural and livestock industry. Taking into account this situation, this paper proposes a solution based on an opportunistic network algorithm which enables the deployment of technological communication solutions for both elderly healthcare and livestock industrial activities in rural areas. This way, two applications are proposed: a presence detection platform for elderly people who live alone and an analytic performance measurement system for livestock. The algorithm is evaluated considering several simulations under multiple conditions, comparing the delivery probability, latency, and overhead outcomes with other well‐known opportunistic routing algorithms. As a result, the proposed solution quadruples the delivery probability of Prophet, which presents the best results among the benchmark solutions and greatly reduces the overhead regarding other solutions such as Epidemic or Prophet. This way, the proposed approach provides a reliable mechanism for the data transmission in these scenarios.

This artifact is a guideline for the generation of APIs through the APIGEND (API Generator for End Devices) tool. This tool is an extension of the OpenAPI Generator [1] . It originally allows developers to create both the client and server side through an OpenAPI Specification with a ServerCentric style in different languages. The extension developed also allows one to generate APIs for end devices, specifically for Android devices and ESP32 Microcontrollers, making the application of the Edge [2] and Mobile-Centric [3] paradigms easier.