Realistic emulation of wireless communication is essential for the development and validation of protocols in lowpower networks. While tools like Wmediumd provide accurate medium emulation for IEEE 802.11, no equivalent exists for IEEE 802.15.4, an important standard underlying protocols such as ZigBee, Thread, and 6LoWPAN. This paper presents an extension of Wmediumd to support IEEE 802.15.4, enabling probabilistic modeling of wireless effects such as interference, attenuation, and packet loss. By integrating with the existing mac802154_hwsim kernel module, our approach allows for accurate, reproducible experimentation in virtual environments. We demonstrate the emulator’s effectiveness through scenarios involving range-based communication and unstable links, highlighting its potential for advancing research and testing in IoT and low-power wireless networks.

This demonstration showcases the benefits of P4 programmability to improve the efficiency and adaptability of IEEE 802.15.4-based LoWPANs using the RPL protocol. By leveraging P4, the platform enables dynamic packet processing and real-time telemetry, essential for optimizing routing decisions and enhancing network reliability in mission-critical environments. To validate these capabilities, we present an emulation platform that integrates P4-programmable BMv2 switches with IEEE 802.15.4 LoWPAN and RPL, creating a realistic and flexible environment for simulating wireless sensor networks in aviation scenarios. The platform supports dynamic sensor management, in-band telemetry, and fine-grained control over network behavior, enabling the study of routing dynamics, bottlenecks, and load-balancing strategies. Preliminary results confirm the effectiveness of this approach in replicating low-power wireless communication, highlighting its potential as a powerful and costeffective testbed for next-generation IoT and aviation systems.

The escalating complexity and critical role of Operations and Maintenance Systems (OMS) in 5G networks require robust Disaster Recovery (DR) solutions to ensure uninterrupted service and minimal downtime. Disaster Recovery Systems (DRS) are crucial for network resilience, enabling seamless failover and recovery during disruptions. The switchover function is pivotal for maintaining 5G service continuity. This study addresses the limitations of traditional rule-based decision-making, which often employs simplistic binary switchover logic inadequate for 5G’s intricate demands. We propose the predictive OMS Switchover (pOM2S), a machine learning-driven approach that leverages historical computing and networking data to select the optimal OMS backup instance from multiple candidates. Evaluated in a 5G emulation testbed, LightGBM and CatBoost outperformed other models like Gradient Boosting, XGBoost, and Elastic Net Regression, achieving superior accuracy and efficiency. Compared to a baseline method relying solely on computational KPIs, pOM2S’s holistic approach selected a backup instance with a predicted switchover time of over twice as fast as the baseline’s. This demonstrates pOM2S’s effectiveness in enhancing DR efficiency and service continuity in 5 G networks.

The rapid evolution of 5G networks has introduced unprecedented challenges in maintaining service continuity, particularly in the eHealth mission-critical vertical. This paper presents the Proactive Disaster Recovery System (PDRS), a machine learning-driven disaster recovery system for 5G Operations and Maintenance Systems (OMS) in mission-critical eHealth verticals. Unlike traditional reactive approaches using binary switchover logic, PDRS enables proactive failover by: (1) continuously monitoring OMS status through KPIs, (2) predicting disasters via real-time analytics, and (3) selecting the optimal OMS backup instances from multi-redundant candidates using migration cost estimates. Proof-of-concept evaluation demonstrates PDRS’ superiority over baseline methods in maintaining service continuity, particularly for low-latency eHealth applications requiring 99.999% availability. Results highlight the necessity of predictive strategies for 5G network resilience in critical medical services.

The Internet of Things (IoT) has transformed modern networks by interconnecting diverse devices across smart homes, industrial automation, and environmental monitoring. However, IoT networks face challenges due to constrained devices and unreliable communication links, requiring efficient routing protocols and adaptive architectures. The Routing Protocol for Low-Power and Lossy Networks (RPL), while the standard for IoT, struggles with real-time monitoring and dynamic topologies. Evaluating RPL proposals in realistic environments is essential, and network emulation offers a practical middle ground between full-scale deployment and simulations. To address these challenges, this paper introduces P4LoWPAN, an innovative IoT network emulation platform that integrates P4 with RPL, leveraging In-band Network Telemetry (INT) for real-time monitoring and a programmable data plane. Built on Mininet-WPAN and powered by Docker-based sensor nodes, P4LoWPAN provides a scalable and flexible environment for IoT network emulation. It enables dynamic routing, topology visualization, and detailed telemetry, making it a valuable tool for researchers optimizing IoT networks. Case studies demonstrate P4LoWPAN’s capabilities, showcasing its potential to support more efficient, adaptive, and programmable IoT data planes.

The next-generation mobile cellular networks are dedicated to providing a valued and unique service experience by supporting ultrareliable and low-latency communication (URLLC), high throughput, and high availability. Multiaccess edge computing (MEC) is an emerging network solution that provides services and computing functions on edge nodes to provide users with a reliable and high-quality service experience. However, achieving satisfactory Quality of Service (QoS) for diverse service requests in a mobile environment is challenging because of the densely deployed yet resource-constrained MEC servers. A solution to ensure continued service quality is to migrate the services according to the mobility of users. However, in a highly mobile environment such as vehicular communications, this may result in a repeated relocation of services, incurring high operational costs and poor utilization of network resources. Moreover, each service has its own set of communication requirements, such as delay and bandwidth. Meeting these requirements in a highly dynamic and complex vehicular environment is an exacting challenge. Software-defined networking (SDN) concepts are leveraged in MEC to provide a unified control plane interface that performs effective network and service mobility management, to manage the heterogeneity of service requests within the resource-constrained MEC servers. We conducted various Proof-of-Concept (PoC) experiments in an overlapped vehicle-to-everything (V2X) networking environment to demonstrate the feasibility of our proposed system that ensures interconnection and federation among distributed MEC servers and mobile networks.

The efficient design of SFC-enabled eHealth applications requires an accurate provision of the underlying infrastructure. This provision requires both computing and networking resources to meet stringent QoS requirements under any conditions of service demand. Cloud providers often offer automatic elasticity strategies based on monitoring specific metrics that lead to a waste of resources, time/energy consumption, and the problem of starvation with competing services. Our findings provide evidence that proactive-based elasticity overcomes these issues, when assisted by Machine Learning (ML) methods for predicting Internet traffic load. An optimal autoscaling algorithm depends on high precision and fast predictions to provide accurate results. Thus, this paper assesses ML algorithms to support SFC-enabled eHealth vertical applications. The experimental results suggest that the evaluated models achieved similar accuracy metrics, with an MLP architecture delivering the best performance in terms of time training and average prediction time.

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

Software-Defined Wireless Networking (SDWN) is an emerging approach based on decoupling radio control functions from the radio data plane through programmatic interfaces. Despite diverse ongoing efforts to realize the vision of SDWN, many questions remain open from multiple perspectives such as means to rapid prototype and experiment candidate software solutions applicable to real-world deployments. To this end, emulation of SDWN has the potential to boost research and development efforts by re-using existing protocol and application stacks while mimicking the behavior of real wireless networks. In this article, we provide an in-depth discussion on that matter focusing on the Mininet-WiFi emulator design to fill a gap in the experimental platform space. We showcase the applicability of our emulator in an SDN wireless context by illustrating the support of a number of use cases aiming to address the question on how far we can go in realistic SDWN experiments, including comparisons with the results obtained in a wireless testbed. Finally, we discuss the ability to replay packet-level and radio signal traces captured in the real testbed toward a virtual yet realistic emulation environment in support of SDWN research.

Software-Defined Wireless Networking (SDWN) is being considered an appealing paradigm to design and operate wireless networks through higher-level abstractions and programmatic interfaces such as the OpenFlow protocol. Identified benefits include cost savings, service velocity and customization, resource optimization through novel approaches to user mobility, traffic offloading, multi-layer and multi-path routing, and so on. This demonstration features Mininet-WiFi as a SDWN emulator with the ability to run realistic experiments in hybrid physical-virtual environments, where users attending the conference are able to experience first hand by connecting their devices and interacting with virtual WiFi stations in a wireless mesh network or reach the Internet through the emulated SDWN infrastructure. OpenFlow 1.3 metering and IP header re-writing actions will showcase HTTP flow redirection and rate limitation of real users' wireless traffic.

As the density of wireless networks continues to grow with more clients, more base stations, and more traffic, designing cost-effective wireless solutions with efficient resource usage and ease to manage is an increasing challenging task due to the overall system complexity. A number of vendors offer scalable and high-performance wireless networks but at a high cost and commonly as a single-vendor solution, limiting the ability to innovate after roll-out. Recent Software-Defined Networking (SDN) approaches propose new means for network virtualization and programmability advancing the way networks can be designed and operated, including user-defined features and customized behaviour even at run-time. However, means for rapid prototyping and experimental evaluation of SDN for wireless environments are not yet available. This paper introduces Mininet-WiFi as a tool to emulate wireless OpenFlow/SDN scenarios allowing high-fidelity experiments that replicate real networking environments. Mininet-WiFi augments the well-known Mininet emulator with virtual wireless stations and access points while keeping the original SDN capabilities and the lightweight virtualization software architecture. We elaborate on the potential applications of Mininet-Wifi and discuss the benefits and current limitations. Two use cases based on IEEE 802.11 demonstrate available functionality in our open source developments.