arXiv subjects

Admela Jukan

Publications and source records attributed to Admela Jukan.

At least 19 recordsLinked to original sources

arXiv2026

Throughput Analysis and On-Board Buffer Sizing for Hybrid RF and Optical LEO Satellites

Low Earth Orbit (LEO) satellite networks are increasingly adopting laser communications based on Free Space Optics (FSO). Although laser inter-satellite links offer high throughput and low latency, RF up- and downlinks remain necessary to maintain connectivity during optical outages caused by adverse atmospheric conditions. The hybrid RF/FSO up- and downlinks require an efficient transmission scheduling and buffer sizing in satellite network nodes, due to traffic asymmetry and variable capacity. This paper analyzes throughput performance and buffer sizing in hybrid RF/laser satellite networks with finite buffer capacity, interference-aware scheduling, and weather-dependent laser link outage probabilities. Numerical results indicate that laser communications bring significant performance gains. Instead of increasing the transmission power of the satellite to maximize the throughput, we can select a suitable transmission scheduling priority to achieve a maximum throughput, while minimizing the buffer requirement, and lowering packet loss probability under varying weather conditions and constraints.

cs.NI↗

arXiv2026

Traffic Chunk Sizing vs. Optical Switching Speed in Future All-Optical Satellite Networks

To enable efficient resource utilization under stringent Size, Weight, and Power (SWaP) constraints through transparent and all-optical switched satellites transmission, various switching paradigms can be considered, including packet, burst, or circuit. To this end, the traffic assembly and algorithmic design for path computations at the ground stations play a key role in determining the switching fabric design. Generally, traffic can be buffered and assembled in chunks at the ground stations and forwarded over the pre-computed optical path in space, similar to terrestrial optical burst switching or fast circuit switching. Regardless of the chosen paradigm, the switching fabric must satisfy specific latency performance requirements. This paper studies the performance of all-optical satellite networks based on the maximum traffic chunk sizes that can be scheduled and the performance of optical switching fabrics in the future over all-optical constellations. We consider various optical switching technologies, including MEMS- and integrated photonic-based solutions, in the context of switching speed, power consumption, and insertion loss. Simulation results indicate that traffic chunk size critically impacts the performance required by optical switching fabrics onboard a satellite.

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arXiv2026

A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems is fundamentally challenged by the absence of reliable ground truth in open-ended environments and the risk of increasing operational drift over time. To address this challenge, we propose and experimentally evaluate an agentic AI framework, designed to enforce autonomous integrity within LLM-driven systems. We design a self-calibration mechanism that mitigates drift and dynamically approximates ground truth by incorporating an ARIMA forecaster, without requiring continuous human oversight. To demonstrate the effectiveness and reliability of our methodology, we apply it to the complex domain of profiling the resource usage of zero-knowledge workloads in edge computing networks. Experimental results show that the proposed self-calibrating agentic framework successfully profiles the zero-knowledge workloads, achieving a higher accuracy than baseline LLM agents by 91.7% for resource usage prediction and improving the prediction speed by 71.7% compared to pure profiling, establishing a robust foundation for deploying autonomous AI in decentralized infrastructures. Furthermore, the ground truth generation using the proposed ARIMA leaping algorithm is 52% faster than a standard ARIMA forecasting algorithm, while achieving the same accuracy.

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arXiv2026

Maximum Achievable Burst Size in All-Optical Satellite Networks

We analyze the maximum burst size achievable in all-optical satellite networks across different constellations. With a 100 Gbps uplink capacity, a WDM-based optical burst switching network supports burst sizes of up to 500 MB in high-altitude LEO constellations and 600 MB in low-altitude LEO constellations.

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arXiv2026

Optimizing Split Learning Latency in TinyML-Based IoT Systems

Split learning (SL) addresses the limitation of running deep learning inference directly on low-power edge/IoT nodes, in which it executes part of the inference process on the sensor and offloading the remainder to a companion device. Despite its promise, the inference latency of SL on constrained hardware under realistic low-power wireless protocols remains unexplored. This paper presents the first experimental latency benchmark of TinyML-based SL on ESP32-S3 boards, comparing four wireless communication protocol solutions (UDP, TCP, ESP-NOW, BLE). We also analyze the impact of the choice of different split points across different models (MobileNet-V2 and ResNet50) in terms of communication and computation overhead as a way to minimize the end-to-end inference latency. We propose a Beam Search-based algorithm for split point optimization that minimizes end-to-end latency, and compare it with other methods, including Greedy Search, First-Fit, Random-Fit, and Brute Force. ESP-NOW achieves the best RTT (3.6 s) and serves as the base protocol for the algorithm, which delivers near-optimal latency with processing time of 0.1 s for 5 devices.

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arXiv2026

Enhancing Secure Intent-Based Networking with an Agentic AI: The EU Project MARE Approach

In the EU project MARE, a novel plane was proposed and used in combination with intent-based networking (IBN), allowing the operator to focus on what, rather than on how. Recently, LLMs have been successfully employed to translate the high-level intents into low-level actions. The open challenge is to understand how IBN can be effectively enhanced with LLM and the emerging agentic AI for security purposes. Enhancing IBN with an agentic AI paradigm introduces significant challenges that existing solutions do not fully address. This paper proposes an enhanced IBN framework with a strong security focus toward agentic AI. We address the architectural and security requirements for a multi-agent intent-based system (IBS) architecture, including a multi-domain IBN. We propose a hierarchical multi-agent and multi-vendor architecture that can also be applied more broadly in 6G architectures and beyond, beyond the security architecture proposed in MARE. The architecture incorporates an interactive intent-processing pipeline using LLMs, and it also allows the IBS to connect to external security knowledge bases, such as MITRE ATT\&CK, MITRE FiGHT, and NIST.

cs.NI↗

arXiv2026

Efficient Self-Learning and Model Versioning for AI-native O-RAN Edge

The AI-native vision of 6G requires Radio Access Networks to train, deploy, and continuously refine thousands of machine learning (ML) models that drive real-time radio network optimization. Although the Open RAN (O-RAN) architecture provides open interfaces and an intelligent control plane, it leaves the life-cycle management of these models unspecified. Consequently, operators still rely on ad-hoc, manual update practices that can neither scale across the heterogeneous, multi-layer stack of Cell-Site, Edge-, Regional-, and Central-Cloud domains, nor across the three O-RAN control loops (real-, near-real-, and non-real-time). We present a self-learning framework that provides an efficient closed-loop version management for an AI-native O-RAN edge. In this framework, training pipelines in the Central/Regional Cloud continuously generate new models, which are cataloged along with their resource footprints, security scores, and accuracy metrics in a shared version repository. An Update Manager consults this repository and applies a self-learning policy to decide when and where each new model version should be promoted into operation. A container orchestrator then realizes these decisions across heterogeneous worker nodes, enabling multiple services (rApps, xApps, and dApps) to obtain improved inference with minimal disruption. Simulation results show that an efficient RL-driven decision-making can guarantee quality of service, bounded latencies while balancing model accuracy, system stability, and resilience.

cs.NI↗

arXiv2026

A Cross-Layer Analysis of Network Antifragility with RIS-assisted Links under Jamming Attacks

Antifragility is an economics term defined as measure of (monetary) benefits gained from the adverse events and variability of the markets. This paper integrates for the first time the antifragility into the network based on communication links with Reconfigurable Intelligent Surface (RIS) affected by a jamming attack. We analyze whether antifragility can be achieved for several jamming models. Beyond the link-level gains, the results reveal how antifragile RIS-assisted links can be integrated into multi-hop systems to improve end-to-end network resilience, connectivity, and throughput under adversarial effects.

cs.NI↗

arXiv2025

Towards a Security Plane for 6G Ecosystems

6G networks promise to be the proper technology to support a wide deployment of highly demanding services, satisfying key users-related aspects such as extremely high quality, and persistent communications. However, there is no service to support if the network is not reliable enough. In this direction, it is with no doubt that security guarantees become a must. Traditional security approaches have focused on providing specific and attack-tailored solutions that will not properly meet the uncertainties driven by a technology yet under development and showing an attack surface not completely identified either. In this positioning paper we propose a softwarized solution, defining a Security Plane built on a top of programmable and adaptable set of live Security Functions under a proactive strategy. In addition, in order to address the inaccuracies driven by the predictive models a pre-assessment scenario is also considered ensuring that no action will be deployed if not previously verified. Although more efforts are required to develop this initiative, we think that such a shift paradigm is the only way to face security provisioning challenges in 6G ecosystems.

cs.CR↗

arXiv2025

Optimum Network Slicing for Ultra-reliable Low Latency Communication (URLLC) Services in Campus Networks

Within 3GPP, the campus network architecture has evolved as a deployment option for industries and can be provisioned using network slicing over already installed 5G public network infrastructure. In campus networks, the ultra-reliable low latency communication (URLLC) service category is of major interest for applications with strict latency and high-reliability requirements. One way to achieve high reliability in a shared infrastructure is through resource isolation, whereby network slicing can be optimized to adequately reserve computation and transmission capacity. This paper proposes an approach for vertical slicing the radio access network (RAN) to enable the deployment of multiple and isolated campus networks to accommodate URLLC services. To this end, we model RAN function placement as a mixed integer linear programming problem with URLLC-related constraints. We demonstrate that our approach can find optimal solutions in real-world scenarios. Furthermore, unlike existing solutions, our model considers the user traffic flow from a known source node on the network's edge to an unknown \textit{a priori} destination node. This flexibility could be explored in industrial campus networks by allowing dynamic placement of user plane functions (UPFs) to serve the URLLC.

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arXiv2025

A Secured Intent-Based Networking (sIBN) with Data-Driven Time-Aware Intrusion Detection

While Intent-Based Networking (IBN) promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blind spot exploitable by Man-in-the-Middle (MitM) attacks, where an adversary intercepts and alters intent before it is enacted, compelling the network to orchestrate malicious configurations. This study proposes a secured IBN (sIBN) system with data driven intrusion detection method designed to secure legitimate user intent from adversarial tampering. The proposed intent intrusion detection system uses a ML model applied for network behavioral anomaly detection to reveal temporal patterns of intent tampering. This is achieved by leveraging a set of original behavioral metrics and newly engineered time-aware features, with the model's hyperparameters fine-tuned through the randomized search cross-validation (RSCV) technique. Numerical results based on real-world data sets, show the effectiveness of sIBN, achieving the best performance across standard evaluation metrics, in both binary and multi classification tasks, while maintaining low error rates.

cs.CR↗

arXiv2025

On Effectiveness of Graph Neural Network Architectures for Network Digital Twins (NDTs)

Future networks, such as 6G, will need to support a vast and diverse range of interconnected devices and applications, each with its own set of requirements. While traditional network management approaches will suffice, an automated solutions are becoming a must. However, network automation frameworks are prone to errors, and often they employ ML-based techniques that require training to learn how the network can be optimized. In this sense, network digital twins are a useful tool that allows for the simulation, testing, and training of AI models without affecting the real-world networks and users. This paper presents an AI-based Network Digital Twin (AI-NDT) that leverages a multi-layered knowledge graph architecture and graph neural networks to predict network metrics that directly affect the quality of experience of users. An evaluation of the four most prominent Graph Neural Networks (GNN) architectures was conducted to assess their effectiveness in developing network digital twins. We trained the digital twin on publicly available measurement data from RIPE Atlas, therefore obtaining results close to what is expected in real-world applications. The results show that among the four architectures evaluated, GraphTransformer presents the best performance. However, other architectures might fit better in scenarios where shorter training time is important, while also delivering acceptable results. The results of this work are indicative of what might become common practice for proactive network management, offering a scalable and accurate solution aligned with the requirements of the next-generation networks.

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arXiv2025

On Efficient Topology Management in Service-Oriented 6G Networks: An Edge Video Distribution Case Study

An efficient topology management in future 6G networks is one of the fundamental challenges for a dynamic network creation based on location services, whereby each autonomous network entity, i.e., a sub-network, can be created for a specific application scenario. In this paper, we study the performance of a novel topology changes management system in a sample 6G network being dynamically organized in autonomous sub-networks. We propose and analyze an algorithm for intelligent prediction of topology changes and provide a comparative analysis with topology monitoring based approach. To this end, we present an industrially relevant case study on edge video distribution, as it is envisioned to be implemented in line with the 3GPP and ETSI MEC (Multi-access Edge Computing) standards. For changes prediction, we implement and analyze a novel topology change prediction algorithm, which can automatically optimize, train and, finally, select the best of different machine learning models available, based on the specific scenario under study. For link change scenario, the results show that three selected ML models exhibit high accuracy in detecting changes in link delay and bandwidth using measured throughput and RTT. ANN demonstrates the best performance in identifying cases with no changes, slightly outperforming random forest and XGBoost. For user mobility scenario, XGBoost is more efficient in learning patterns for topology change prediction while delivering much faster results compared to the more computationally demanding deep learning models, such as LSTM and CNN. In terms of cost efficiency, our ML-based approach represents a significantly cost-effective alternative to traditional monitoring approaches.

cs.NI↗

arXiv2025

Evaluating the Impact of Inter-cluster Communications in Edge Computing

Distributed applications based on micro-services in edge computing are becoming increasingly popular due to the rapid evolution of mobile networks. While Kubernetes is the default framework when it comes to orchestrating and managing micro-service-based applications in mobile networks, the requirement to run applications between multiple sites at cloud and edge poses new challenges. Since Kubernetes does not natively provide tools to abstract inter-cluster communications at the application level, inter-cluster communication in edge computing is becoming increasingly critical to the application performance. In this paper, we evaluate for the first time the impact of inter-cluster communication on edge computing performance by using three prominent, open source inter-cluster communication projects and tools, i.e., Submariner, ClusterLink and Skupper. We develop a fully open-source testbed that integrates these tools in a modular fashion, and experimentally benchmark sample applications, including the ML class of applications, on their performance running in the multi-cluster edge computing system under varying networking conditions. We experimentally analyze two classes of envisioned mobile applications, i.e., a) industrial automation, b) vehicle decision drive assist. Our results show that ClusterLink performs best out of the three tools in scenarios with increased payloads, regardless of the underlying networking conditions or transmission direction between clusters. It is closely followed by Skupper, unless request and reply both transport significant amounts of data. Finally, when requesting smaller amounts of data from a service, Submariner slightly outperforms Skupper and ClusterLink regardless of the inter-node networking conditions.

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arXiv2025

Plastic computing, the cloud continuum journey beyond infinity

The ever increasing challenges introduced by the diversity of current and envisioned network technologies and IT infrastructure draw a highly distributed and heterogeneous topology where innovative services must be optimally deployed to guarantee maximum level of quality for users. Indeed, paradigms such as the cloud continuum, bringing together edge and cloud computing, along with the new opportunities coming out by considering non-terrestrial networks connecting future 6G ecosystems, all with no doubt facilitate the development of innovative services in many different areas and verticals. However, considering the intensive data and quality requirements demanded by these services, the distribution of the execution tasks must be optimally designed. On the infrastructure side, several initiatives are already active aimed at providing a Meta-OS that may seamlessly manage the different actors (services, infrastructure and users) playing under this paradigm. However, several aspects remain yet limited, particularly when referring to the mapping of resources into services, where innovative technologies based on bidirectional coordination and modeling may be pivotal for an optimal performance. In addition, the upcoming demands coming from the adoption of network technologies easing users connection with high levels of quality, such as 6G, as well the study of NTN open up the traditional cloud continuum to include also satellites that may extend the cloud paradigm further than ever considered. This paper shows a seed work toward an extendable paradigm so called as plastic computing whose main objective is to optimize service performance and users satisfaction, through considering a bidirectional strategy, easily extendable to adopt novel network and IT technologies and paradigms. Finally, two examples are briefly introduced to highlight the potential benefits of the plastic computing adoption

cs.ET↗

arXiv2024

An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks

Future smart factories are expected to deploy applications over high-performance indoor wireless channels in the millimeter-wave (mmWave) bands, which on the other hand are susceptible to high path losses and Line-of Sight (LoS) blockages. Low-cost Reconfigurable Intelligent Surfaces (RISs) can provide great opportunities in such scenarios, due to its ability to alleviate LoS link blockages. In this paper, we formulate a combinatorial optimization problem, solved with Integer Linear Programming (ILP) to optimally maintain connectivity by solving the problem of allocating RIS to robots in a wireless indoor network. Our model exploits the characteristic of nulling interference from RISs by tuning RIS reflection coefficients. We further consider Quality-of-Service (QoS) at receivers in terms of Signal-to-Interference-plus-Noise Ratio (SINR) and connection outages due to insufficient transmission quality service. Numerical results for optimal solutions and heuristics show the benefits of optimally deploying RISs by providing continuous connectivity through SINR, which significantly reduces outages due to link quality.

cs.NI↗

arXiv2024

Optimizing LoRa for Edge Computing with TinyML Pipeline for Channel Hopping

We propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge computing system, by taking advantage of its unlicensed nature and the potential for open source implementations that are common in edge computing. We propose a channel hoping optimization model and apply TinyML-based channel hoping model based for LoRa transmissions, as well as experimentally study a fast predictive algorithm to find free channels between edge and IoT devices. In the open source experimental setup that includes LoRa, TinyML and IoT-edge-cloud continuum, we integrate a novel application workflow and cloud-friendly protocol solutions in a case study of plant recommender application that combines concepts of microfarming and urban computing. In a LoRa-optimized edge computing setup, we engineer the application workflow, and apply collaborative filtering and various machine learning algorithms on application data collected to identify and recommend the planting schedule for a specific microfarm in an urban area. In the LoRa experiments, we measure the occurrence of packet loss, RSSI, and SNR, using a random channel hoping scheme to compare with our proposed TinyML method. The results show that it is feasible to use TinyML in microcontrollers for channel hopping, while proving the effectiveness of TinyML in learning to predict the best channel to select for LoRa transmission, and by improving the RSSI by up to 63 %, SNR by up to 44 % in comparison with a random hopping mechanism.

cs.NI↗

arXiv2024

Towards Smart Microfarming in an Urban Computing Continuum

Microfarming and urban computing have evolved as two distinct sustainability pillars of urban living today. In this paper, we combine these two concepts, while majorly extending them jointly towards novel concepts of smart microfarming and urban computing continuum. Smart microfarming is proposed with applications of artificial intelligence (AI) in microfarming, while an urban computing continuum is proposed as a major extension of the concept towards an efficient Internet of Things (IoT) -edge-cloud continuum. We propose and build a system architecture for a plant recommendation system that uses machine learning (ML) at the edge to find, from a pool of given plants, the most suitable ones for a given microfarm using monitored soil values obtained from IoT sensor devices. Moreover, we propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge system, due to its unlicensed nature and potential for open source implementations. Finally, we propose to integrate open source and less constrained application protocol solutions, such as Advanced Message Queuing Protocol (AMQP) and Hypertext Transport Protocol (HTTP) protocols, for storing the data in the cloud. An experimental setup is used to evaluate and analyze the performance and reliability of the data collection procedure and the quality of the recommendation solution. Furthermore, collaborative filtering is used for the completion of an incomplete information about soils and plants. Finally, various ML algorithms are applied to identify and recommend the optimal plan for a specific microfarm in an urban area.

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