Virtualization and containerization enhance the modularity and scalability of mobile network architectures, facilitating customized user services and improving management and orchestration across the network. In the context of the 5th Generation Mobile Network (5G), these advancements contribute to reduced Operational Expenditures (OPEX) and enable sliced-based networking for novel applications and services. However, as beyond fifth-generation (B5G) networks aim to address the remaining challenges regarding network slice isolation, the shared underlying hardware can lead to data plane contention among slices, resulting in the Noisy Neighbor (NN) effect, which may compromise network slicing and Service-Level Agreements (SLAs). We propose a kernel-level instrumentation of the User Plane Function (UPF) to assess the impact of noisy slices on data plane processing. Our findings reveal that even prioritized slices are susceptible to degradation induced by NN, with observable effects on latency metrics pertinent to user experience.

Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial Intelligence (AI) vision models achieve high accuracy, their adoption is hindered by the limitations of constrained devices and intermittent connectivity. This study aims to facilitate sustainable on-device diagnosis through knowledge distillation: high-capacity Convolutional Neural Networks (CNNs) trained in data centers transfer knowledge to compact CNNs through Ensemble Learning (EL). Furthermore, dense tiny pairs were integrated through simple and optimized ensembling to enhance accuracy while adhering to strict computational and energy constraints. On a curated coffee leaf dataset, distilled tiny ensembles achieved competitive with prior work with significantly reduced energy consumption and carbon footprint. This indicates that lightweight models, when properly distilled and ensembled, can provide practical diagnostic solutions for Internet of Things (IoT) applications.

Abstract Design Pattern Detection (DPD) tools are useful to support to the comprehension and maintenance of software systems. Although several DPD tools have been introduced over the years, they typically focus on a limited set of design patterns and programming languages. This paper aims to investigate (i) the reasons that motivate DPD tool designers to target specific design patterns and programming languages, and (ii) how potential users perceive the usefulness of DPD tools in practical software development scenarios. We conducted two online surveys. For the first survey, we reached out to designers of 42 DPD tools selected from a systematic literature review to obtain their perspectives on design decisions about pattern and language coverage, receiving 22% of such responses. For the second survey, we recruited 28 student and senior developers to help us understand their expectations, perceived benefits, and concerns related to the use of DPD tools. Within our sample, the findings suggest that participating tool designers often prioritize design patterns whose internal structure facilitates automated detection, while language support is frequently motivated by popularity and expected demand. From the perspective of tool users, DPD tools are expected to support development activities, such as program comprehension and software quality improvement. Unfortunately, usability difficulties, limited accuracy, and insufficient documentation often discourage them from adopting a tool. The responses suggest that some design decisions reported by participating DPD tool designers are aligned with practical and industrial considerations. However, the recurring lack of adequate documentation and usability support is a major barrier to wider usage. Taken as indicative evidence, these results suggest that future DPD tools should better balance detection capabilities and usability concerns, especially to meet the need of less experienced developers. Given the limited number of tool-designer respondents, conclusions about design rationale should be interpreted as indicative rather than representative of all DPD tool designers.

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

People and organizations around the globe are using multimedia applications to communicate. By evolving their networks, network operators are taking advantage of the convergence of voice and data. QoS metrics are usually based on parameters from the user side or network side. In this context, NFV and SDN can improve the communication experience and offers an abstraction layer to the deployment of flexible multimedia applications provided on the cloud. In this work, we propose an approach to mitigate globally manage network and computing resources for multimedia applications. To this end, we come up with a control plane entity capable of orchestrating compute and network resources for the multimedia application scenario, relying on SIP control messages. Our solution brings QoS enhancement to the user through resiliency, load balancing, packet inspection, scaling on demand and the separation of control and data planes.

Internet of Things (IoT) has increased its presence in many environments. However, this means a greater exposure of sensitive data, rising potential security threats. Thus, security becomes a key requirement for the protection and prevention of cyber attacks to IoT applications and devices. FIWARE Platform, for example, has an architecture of components responsible for interconnecting devices to IoT applications, decreasing complexity and providing a standard set of services to developers. The IDAS 5 version of the platform presents several security gaps, so this work aims to solve some of them by incorporating end-to-end security services using encryption and access control in all NGSI requests (RESTFul API). The main contribution of this paper is the implementation of the DTLS 1.2 protocol in NodeJs to support the LWM2M/CoAP protocol and its addition to the IoT Agent. This paper also allowed the IoT Agent of the FIWARE Platform support TLS communication to the MQTT protocol. Through an experimental evaluation, it was possible to validate the implementation. Our preliminary results show that the encrypted requests had a small increase regarding latency, but this cost is compensated by the increase of security in FIWARE based IoT Applications. The source code of this work is open on the GitHub and it can be used to support security services in other IoT communication protocols.