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Sustainable Spectrum Crowdsensing
Get PDFSpectrum crowdsensing is a paradigm where participants upload their collected spectrum data to the cloud for extracting analytics. First movers like Microsoft Spectrum Observatory and Electrosense, though with support from leading industry, research, and government, still suffer from sustainability challenges. In this paper, we present Fiesta, a sustainable framework for spectrum crowdsensing. On the technology side, we use federated learning and blockchain to decentralize the data analysis computations. For individual participants, minimal invasion of privacy suppresses concerns regarding large-scale adoption. From organizations’ perspectives, using blockchain avoids single point of failure and enhances the robustness of the entire system against malicious attacks. On the policy side, we propose a reward quantification mechanism to motivate engagement. Potential funding sources to ensure ongoing sustainability are also discussed. We have demonstrated Fiesta through simulation testbeds and real-world deployments with two demo tasks. Results show that Fiesta, as a decentralized framework, can
preserve user privacy, enhance system robustness, maintain data fidelity compared with traditional methods, and fairly reward participants. We believe Fiesta is a stepping stone for the future
spectrum crowdsensing paradigm.Ministerio de Asuntos Económicos y Transformación DigitalTRUEpu
Network Intelligence in Action: the DAEMON Perspective
Get PDFThe native integration of AI and ML algorithms in the next-generation mobile network architecture will allow for meeting the expectations of 6G. This aspect is targeted by the DAEMON project, which proposed a solution to natively manage Network Intelligence (NI) through novel architectural elements and procedures. In this paper, we discuss how NI solutions based on AI and ML can leverage NI native procedures implemented by the NI Orchestrator to improve their lifecycle management. We also discuss how the architectural procedures can be implemented in practice, using state-of-the-art software components.This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101017109 “DAEMON”. L.E. Chatzieleftheriou is a Juan de la Cierva awardee (JDC2022-050266-I), funded by MCIU/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.TRUEpu
CloudRIC demo: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing
Get PDFOpen and virtualized Radio Access Networks (vRANs) are breeding a new market with unprecedented opportunities. However, carrier-grade vRANs today are expensive and energyhungry, as they rely on hardware accelerators (HAs) that are dedicated to individual distributed units (DUs). We demonstrate CloudRIC, a system that, powered by lightweight data-driven models, meets specific reliability targets while (��) coordinating access between DUs and heterogeneous computing infrastructure; and (����) assisting DUs with compute-aware radio scheduling procedures. Using a user-friendly dashboard to control an experimental testbed remotely, we demonstrate that CloudRIC achieves comparable reliability performance to a DU-dedicated platform while offering up to 40x higher cost-efficiency and up to 6x higher energy efficiency when pooling resources for up to 70 DUs.Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D project no.TSI-063000-2021 "OPEN6G"Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D project no. 022/0005395 "CLARION"TRUEpu
Age-Of-Information in Tandem Queues With Delayed Feedback: Zero-Wait vs. Pipelining
Get PDFAn established policy for updating systems is zerowait: a source immediately sends a new sample as soon as the sink acknowledges the receipt of the previous one. The rationale of zero-wait is that with instantaneous feedback, the transmission of samples can fully utilize the forward link without ever causing a queue. However, this ideal behavior does not extend to multihop networks and two-way delay. One approach to generalize zero-wait for use in larger networks is message pipelining, where there is a fixed number of samples and acknowledgments k ≥ 1 in the network at any time. We analyze the peak age-of-information of updating systems with pipelining in multi-hop networks with arbitrarily many queues in the forward and feedback paths. While pipelining improves network utilization, it also increases queuing delays, and the optimal degree k must strike a balance between the two. We show how this depends on the diameter and topology of the network, the presence of bottlenecks, and the statistical distribution of service times. In an a priori unknown and changing network, it is beneficial to adjust the pipelining adaptively. We demonstrate how basic delay-based congestion control can be effectively used to achieve this goal.TRUEpu
An Autonomous System for the Self-supervision of Animal Fattening in the context of Precision Livestock Farming
Get PDFBeef production needs certain levels of autonomy to ensure that animal fattening processes achieve certain sustainability objectives (e.g., financial and environmental). For example, it is required oversight in the animal fattening process, so that stakeholders can make better decisions about what is happening in the fattening process. For monitoring the animal fattening process, this paper proposes an autonomous system. In this paper, this autonomous system is designed and implemented using the methodology for the development of data Mining applications called MIDANO, and is tested in a cattle farm simulator that has been developed to reproduce the events of the animal fattening production process. This autonomous system for the self-supervision of the animal fattening process is composed of two data analysis tasks, one to detect anomalies in the fattening of cattle, and another to diagnose this anomaly. The results with real data demonstrate the ability of the proposed supervision system to detect and diagnose anomalies in various conditions (normal, animal health problems, and forage problems in the paddock), and the possible causes of abnormal values in the weight variable. The anomaly detection models have a MAE of the order of 5.5 kg, and the diagnostic model has 95% of Accuracy and 1 of AUC. The results of the experiments are encouraging, as they show that the autonomous system is capable of detecting anomalies and diagnosing them in different operating scenarios. Our system allows giving self-supervision characteristics to a production process.TRUEpu
CNN based Metrics for Performance Evaluation of Generative Adversarial Networks
Get PDFIn this work, we propose two Convolutional Neural Network (CNN) based metrics, Classification Score (CS) and Distribution Score (DS), for performance evaluation of Generative Adversarial Networks (GANs). Though GAN-generated images can be evaluated through manual assessment of visual fidelity, it is prolonged, subjective, challenging, tiresome, and can be misleading. Existing quantitative methods are biased towards memory GAN and fail to detect over-fitting. CS and DS allow us to experimentally prove that training of GANs is actually guided by the data set, that it improves with every epoch and gets closer to following the distribution of the data set. Both methods are based on GAN-generated image classification by CNN. CS is the root mean square (RMS) value of three different classification techniques, Direct Classification (DC), Indirect Classification (IC), and Blind Classification (BC). It exhibits the degree to which GAN can learn the features and generate fake images similar to real data sets. DS shows the contrast between the mean distribution of GAN-generated data and the real data. It indicates the extent to which GANs can create synthetic images with similar distribution to real data sets. We evaluated CS and DS metrics for different variants of GANs and compared their performances with existing metrics. Results show that CS and DS can evaluate the different variants of GANs quantitatively and qualitatively while detecting over-fitting and mode collapse.TRUEpu
Twinning Commercial Network Traces on Experimental Open RAN Platforms
Get PDFWhile the availability of large datasets has been instrumental to advance fields like computer vision and natural language processing, this has not been the case in mobile networking. Indeed, mobile traffic data is often unavailable due to privacy or regulatory concerns. This problem becomes especially relevant in Open Radio Access Network (RAN), where artificial intelligence can potentially drive optimization and control of the RAN, but still lags behind due to the lack of training datasets. While substantial work has focused on developing testbeds that can accurately reflect production environments, the same level of effort has not been put into twinning the traffic that traverse such networks. To fill this gap, in this paper, we design a methodology to twin real-world cellular traffic traces in experimental Open RAN testbeds. We demonstrate our approach on the Colosseum Open RAN digital twin, and publicly release a large
dataset (more than 500 hours and 450 GB) with PHY-, MAC-, and App-layer Key Performance Measurements (KPMs), and protocol stack logs. Our analysis shows that our dataset can be used to develop and evaluate a number of Open RAN use cases, including those with strict latency requirements.Ministry of Sciences and InnovationRyC (RYC2022-036375-I)TRUEinpres
Using fuzzy cognitive maps to evaluate the innovation in micro, small and medium-sized enterprises
No full textInnovation covers the development of new products, internal processes, and market positioning strategies, among other aspects. Organizations must evaluate their innovation performance to continually improve their processes, methods, and capabilities. This article presents a fuzzy cognitive map for the evaluation of innovation in organizations. A review of previous literature indicates a lack of tools to evaluate innovation in organizations using intelligent systems. Our approach, based on fuzzy cognitive maps, yielded strong results in three case studies, effectively determining the level of innovation in organizations. Furthermore, the study revealed the most influential and essential innovative activities/variables within organizations, significantly contributing to the improvement of their operations and competitiveness. The fuzzy cognitive maps demonstrated a high level of accuracy, with an accuracy of 82% in the Colombian case studies, and an accuracy of 92% in the global case studies. These results highlight the effectiveness of the model for quantitatively assessing levels of innovation within organizations. Furthermore, the study revealed the most influential and essential innovative activities/variables within organizations, contributing significantly to the improvement of their operations and competitiveness.TRUEpu
On the Impact of Age of Channel Information on Secure RIS-assisted mmWave Networks
Get PDFReconfigurable Intelligent Surfaces (RISs) have shown great prospects in securing mmWave communication from potential eavesdropping by configuring reflecting elements to strengthen the signal strength at the desired location and creating nulls at potential eavesdropping locations. Acquiring perfect
channel information is crucial for optimizing RIS configuration; however, obtaining such information is costly and, as a result, should be performed sparingly. This work studies the impact of the age of channel information on the secrecy performance of a RIS-assisted mmWave network. In particular, we investigate
how outdated channel information affects the joint optimization of transmit beamforming and RIS configuration. In our MonteCarlo simulations, we first identify the factors influencing the aging process of a RIS-assisted mmWave channel in both the near and far fields of the RIS. Subsequently, we examine the
impact of channel aging on secrecy capacity and demonstrate that adequate secrecy capacity can still be achieved even when channel information is slightly outdated, thus reducing the need for frequent RIS configuration.Ministerio de Asuntos Económicos y Transformación DigitalEuropean UnionTRUEpu
HiSAC: high-resolution sensing with multiband communication signals
Get PDFIntegrated Sensing And Communication ( ISAC ) systems are ex-
pected to perform accurate radar sensing while having minimal
impact on communication. Ideally, sensing should only reuse com-
munication resources, especially for spectrum which is contended
by many applications. However, this poses a great challenge in that
communication systems often operate on narrow subbands with low
sensing resolution. Combining contiguous subbands has shown sig-
nificant resolution gain in active localization. However, multiband
ISAC remains unexplored due to communication subbands being
highly sparse (non-contiguous) and affected by phase offsets that
prevent their aggregation (incoherent). To tackle these problems,
we design HiSAC, the first multiband ISAC system that combines
diverse subbands across a wide frequency range to achieve super-
resolved passive ranging. To solve the non-contiguity and incoher-
ence of subbands, HiSAC combines them progressively, exploiting
an anchor propagation path between transmitter and receiver in
an optimization problem to achieve phase coherence. HiSAC fully
reuses pilot signals in communication systems, applies to different
frequencies, and can combine diverse technologies, e.g., 5G-NR and
WiGig. We implement HiSAC on an experimental platform in the
millimeter-wave unlicensed band and test it on objects and humans.
Our results show it enhances the sensing resolution by up to 20
times compared to single-band processing while occupying the
same spectrum.TRUEpu