Integrating Multi-Access Edge Computing (MEC) and Network Function Virtualization (NFV) favors URLLC, but introduces challenges in resource allocation due to the reduced dimension of NFV-MEC nodes, the overhead caused by virtualization, the use of heterogeneous virtualization technologies, and potential failures during service processing. These factors require careful consideration in resource dimensioning/allocation to ensure that URLLC requirements are met without incurring excessive power consumption due to overprovisioning. This paper proposes a Coloured Petri Net (CPN)-based model for dynamic resource allocation in NFV-MEC systems supporting URLLC services. The proposed model represents a hybrid NFV-MEC system, accounting for virtualization overhead, failure costs, service buffering, and dynamic resource scaling based on demand. It also accommodates system features with diverse behaviors and provides analysis of the system's availability, latency, and power consumption. Results showed that for high arrival rates, improving the container service rate has a greater impact than adding VMs. The benefits of a higher container setup rate on response time become noticeable when VM capacity is exceeded.

This paper introduces EdgeWidgets, a versatile IoT platform designed for environmental monitoring applications. The platform integrates a Bosch BNO055 inclinometer with an ESP32 C3 SuperMini microcontroller and implements a dual-protocol communication layer (HTTP and MQTT). Our experimental evaluation shows that MQTT achieves approximately 26.1% lower latency compared to HTTP, with significantly more consistent performance. These results demonstrate the substantial performance benefits of MQTT’s persistent connection design for monitoring in connectivity-challenged environments.

The coexistence between enhanced Mobile Broad-band (eMBB) and Ultra Reliable Low Latency Communications (URLLC) is challenging in modern communication systems. To support such diversity, Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) emerge as complementary paradigms that shall offer fine-grained on-demand distributed resources closer to the User Equipment (UE). In this work, we address the combination of MEC, NFV and dynamic virtual resource allocation to overcome the challenge of resource dimensioning in the network edge. A Continuous Time Markov Chain (CTMC) based model was designed to evaluate how requests are managed by the virtualization resources of a single MEC node, with a primary focus on meeting the requirements of both eMBB and URLLC services. Practical factors such as resource failures, service prioritization, and setup (repair) times were integrated into the formulation. Some of our key findings include the idea that higher eMBB arrival rates decrease availability and increase response times, while URLLC availability remains stable, and that the container setup rates and failure rates substantially affect both availability and response times, with higher setup rates enhancing both availability and reducing response times.

The fifth generation (5G) of mobile networks has started its operation in some countries and is aimed at meeting demands beyond the current system capabilities such as the huge amount of connected devices from IoT applications (e.g., smart cities), explosive growth of high‐speed mobile data traffic (e.g., ultrahigh definition video streaming), and ultrareliable and low latency communication (e.g., autonomous vehicle). To attend to these needs, the electromagnetic spectrum must be made available, but the static spectrum allocation policy has caused a spectrum shortage and impaired the employment/expansion of the wireless systems. To overcome this issue, the dynamic spectrum access (DSA) has been promoted in 5G/6G networks, which is enabled by the cognitive radio (CR) technology. Although diverse mechanisms have been developed to tackle the challenges that emerge in different CR layers/functionalities, a standardized testing methodology and system for CR is still immature. Existing standards or methodologies and systems for CR only focus on the definition of network technologies (e.g., IEEE 802.22 and IEEE 802.11af), performance evaluation of CR algorithms/mechanisms, or definition of the device cognition level via performance results or psychometric approaches, not covering systems/methodologies to verify if the device meets the CR capabilities and regulatory policies, neglecting the conformance testing. In this respect, this paper proposes a flexible methodology and system for CR conformance testing under two perspectives, functionalities and limits. We instantiate it by using the Universal Software Radio Peripheral (USRP) software‐defined radio platform and present a proof‐of‐concept with a conformance metric. The results show the feasibility of our proposal.

The Fifth Generation (5G) of wireless communication is envisioned to comprise heterogeneous applications, different radio access technologies (RATs), and a large demand for mobile traffic. In this respect, Wireless Virtualization (WV) and Cognitive Radio (CR) are put forward as 5G enablers for providing additional spectrum resources through dynamic spectrum access (DSA) techniques, besides dealing with heterogeneity with no hardware modification. By empowering the synergy between CR and WV, we visualize an environment denoted as Cognitive Radio Virtual Networks Environment (CRVNE) that encompasses VWNs with different access priorities, called Primary Virtual Networks (PVNs) and Secondary Virtual Networks (SVNs) that may be deployed in an overlay manner. In this scenario, the SVNs users (SUs) access the resources opportunistically, which naturally raises challenges towards the SVN mapping. In this paper, we revisit our previous letter that models the interactions between PUs and SUs in a CRVNE and analyzes a proposed formulation for collision probability during the SVN mapping process. The current work is pioneer as it presents a comprehensive approach to the SVNs mapping problem; models, validates, and analyzes additional performance metrics such as SU blocking and SU dropping probabilities and joint utilization; formulates the SVNs mapping as a multiobjective problem; and proposes an evolutionary scheme based on Genetic Algorithms (GAs) to solve it. The results show that the proposed scheme outperforms the alternative method in terms of collision, SU dropping, SU blocking probabilities, and joint utilization under different primary and secondary loads.