Distributed Systems

2024

  1. Graph Neural Networks for Pressure Estimation in Water Distribution Systems (, , and ), In Water Resources Research, .

    Abstract

    Abstract Pressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics-based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data-driven approaches have gained traction to overcome such limitations. In this work, we combine physics-based modeling and graph neural networks (GNN), a data-driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN-based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real-world scenarios. As a result, a new state-of-the-art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average.

    BibTeX


    urldoi
  2. Supporting business process variability through declarative process families ( and ), In Computers in Industry, volume 159-160, .

    Abstract

    Organizations use business process management systems to automate processes that they use to perform tasks or interact with customers. However, several variants of the same business process may exist due to, e.g., mergers, customer-tailored services, diverse market segments, or distinct legislation across borders. As a result, reliable support for process variability has been identified as a necessity. In this article, we introduce the concept of declarative process families to support process variability and present a procedure to formally verify whether a business process model is part of a specified process family. The procedure allows to identify potential parts in the process that violate the process family. By introducing the concept of process families, we allow organizations to deviate from their prescribed processes using normal process model notation and automatically verify if such a deviation is allowed. To demonstrate the applicability of the approach, a simple example process is used that describes several variants of a car rental process which is required to adhere to several process families. Moreover, to support the proposed procedure, we present a tool that allows business processes, specified as Petri nets, to be verified against their declarative process families using the NuSMV2 model checker.


    Keywords: Business processes, Variability, Declarative, Process families, Temporal logic, Verification

    BibTeX


    doi
  3. Large-scale multipurpose benchmark datasets for assessing data-driven deep learning approaches for water distribution networks (, , and ), In Engineering Proceedings, MDPI, volume 69, .

    BibTeX
  4. Graph neural networks for pressure estimation in water distribution systems (, , and ), In Water Resources Research, Wiley Online Library, volume 60, .

    BibTeX
  5. DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks (, , , , , and ), In , .

    BibTeX
  6. In-Network Total Order Guarantees supporting State Machine Replication with P4 Programmable Switches ( and ), In Proceedings of The 32nd IEEE International Conference on Network Protocols (IEEE ICNP 2024), IEEE, .

    BibTeX


    url
  7. DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks (, , , , , and ), In Leveraging Applications of Formal Methods, Verification and Validation. Application Areas, Springer Nature Switzerland, .

    Abstract

    Conventional digital twins (DT) for critical infrastructures are widely used to model and simulate the system's state. But fundamental environment changes bring challenges for DT adaptation to new conditions, leading to a progressively decreasing correspondence of the DT to its physical counterpart. This paper introduces the DiTEC system, a Digital Twin for Evolutionary Changes in Water Distribution Networks (WDN). This framework combines novel techniques, including semantic rule learning, graph neural network-based state estimation, and adaptive model selection, to ensure that changes are adequately detected, processed and the DT is updated to the new state. The DiTEC system is tested on the Dutch Oosterbeek region WDN, with results showing the superiority of the approach compared to traditional methods.

    BibTeX


    urldoi
  8. Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks (, , and ), In 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI), .

    Abstract

    Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. In this work, we provide a collection of datasets that includes several small- and medium-sized publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky10. In total, 1,394,400 h of WDN data operating under normal conditions are made available to the community.

    BibTeX


    urldoi
  9. Driving Towards Efficiency: Adaptive Resource-aware Clustered Federated Learning in Vehicular Networks (, , , and ), In The 22nd Mediterranean Communication and Computer Networking Conference (MedComNet’24)., IEEE, .

    Abstract

    Guaranteeing precise perception for fully autonomous driving in diverse driving conditions requires continuous improvement and training. In vehicular networks, federated learning (FL) facilitates this by enabling model training without sharing raw sensory data. As an extension, clustered FL reduces communication overhead and aligns well with the dynamic nature of these networks. However, current literature on this topic does not consider critical dimensions of FL, including (1) the correlation between perception performance and the networking overhead, (2) the limited vehicle storage, (3) the need for training with freshly captured data, and (4) the impact of non-IID data and varying traffic densities. To fill these research gaps, we introduce AR-CFL, an Adaptive Resource-aware Clustered Federated Learning framework. AR-CFL utilizes clustered FL to collectively model the environment of connected vehicles, integrating models from all vehicles and ensuring universal accessibility to the refined model. AR-CFL dynamically enhances system efficiency by adaptively adjusting the number of clusters and specific in-cluster participant selection strategies. Using AR-CFL, we systematically study the scenario of online car detection model training on non-IID data across varied conditions. The evaluation results highlight the robust detection performance exhibited by the trained model employing the clustered FL approach, despite the constraints posed by limited vehicle storage capacity. Furthermore, our investigation unveils superior training performance with clustered FL in comparison to specific classical FL scenarios, increasing the training efficiency in terms of participating nodes by up to 25% and reducing cellular communication by 33%.

    BibTeX


    url
  10. In-Network Management of Parallel Data Streams over Programmable Data Planes ( and ), In Proceedings of the 23rd International Federation for Information Processing Networking Conference (IFIP NETWORKING 2024), IEEE, .

    BibTeX


    urldoi
  11. Analog In-Network Computing through Memristor-based Match-Compute Processing (, , , and ), In Proceedings of the 43rd International Conference on Computer Communications (INFOCOM 2024), IEEE, .

    BibTeX


    url
  12. dAQM: Derivative-based Active Queue Management (, and ), In Proceedings of the 23rd IFIP Networking Conference (NETWORKING 2024), IFIP, .

    BibTeX


    url
  13. Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size (, and ), In Proceedings of the 37th Network Operations and Management Symposium (NOMS 2024), IEEE, .

    BibTeX


    url
  14. APP-CEP: Adaptive Pattern-level Privacy Protection in Complex Event Processing Systems (, , and ), In The 10th International Conference on Information Systems Security and Privacy (ICISSP 2024)., SCITEPRESS, .

    Abstract

    Although privacy-preserving mechanisms endeavor to safeguard sensitive information at the attribute level, detected event patterns can still disclose privacy-sensitive knowledge in distributed complex event processing systems (DCEP). Events might not be inherently sensitive, but their aggregation into a pattern could still breach privacy. In this paper, we study in the context of APP-CEP the problem of integrating pattern-level privacy in event-based systems by selective assignment of obfuscation techniques to conceal private information. Compared to state-of-the-art techniques, we seek to enforce privacy independent of the actual events in streams. To support this, we acquire queries and privacy requirements using CEP-like patterns. The protection of privacy is accomplished through generating pattern dependency graphs, leading to dynamically appointing those techniques that have no consequences on detecting other sensitive patterns, as well as non-sensitive patterns required to provide acceptable Quality of Service. Besides, we model the knowledge that might be possessed by potential adversaries to violate privacy and its impacts on the obfuscation procedure. We assessed the performance of APP-CEP in a real-world scenario involving an online retailer’s transactions. Our evaluation results demonstrate that APP-CEP successfully provides a privacy-utility trade-off. Modeling the background knowledge also effectively prevents adversaries from realizing the modifications in the input streams.

    BibTeX


    url
  15. Too Good To Be True: accuracy overestimation in (re) current practices for Human Activity Recognition (, and ), In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), .

    BibTeX
  16. Science-Twins: Digital Twins for Interactive Lecture Demonstrations ( and ), In , .

    BibTeX


    url
  17. Self-adaptive service selection for machine learning continuous delivery (, and ), In 2024 IEEE International Conference on Web Services (ICWS), .

    BibTeX
  18. Beyond Digital! Memristor-based Energy Efficient Analog Network Functions ( and ), In ICT.OPEN - CompSys Research for a Responsibly Digitalised Society, NWO ICT.OPEN, .

    BibTeX


    url
  19. Towards Analog In-Network Computing for Supporting Cognitive and Energy-Efficient Network Functions ( and ), In 6th International Conference on Applications of Intelligent Systems (APPIS 2024), University of Las Palmas de Gran Canaria, Spain, .

    BibTeX


    url