Viktoriya Degeler
- Driving Towards Efficiency: Adaptive Resource-aware Clustered Federated Learning in Vehicular Networks (), In The 22nd Mediterranean Communication and Computer Networking Conference (MedComNet’24)., IEEE, 2024.
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 - APP-CEP: Adaptive Pattern-level Privacy Protection in Complex Event Processing Systems (), In The 10th International Conference on Information Systems Security and Privacy (ICISSP 2024)., SCITEPRESS, 2024.
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 - Towards Pattern-Level Privacy Protection in Distributed Complex Event Processing (), In The 17th ACM International Conference on Distributed and Event-Based Systems (DEBS 2023), ACM press, 2023.
Abstract
In event processing systems, detected event patterns can reveal privacy-sensitive information. In this paper, we propose and discuss how to integrate pattern-level privacy protection in event-based systems. Compared to state-of-the-art approaches, we aim to enforce privacy independent of the particularities of specific operators. We accomplish this by supporting the flexible integration of multiple obfuscation techniques and studying deployment strategies for privacy-enforcing mechanisms. Moreover, we share ideas on how to model the adversary’s knowledge to better select appropriate obfuscation techniques for the discussed deployment strategies. Initial results indicate that flexibly choosing obfuscation techniques and deployment strategies is essential to conceal privacy-sensitive event patterns accurately.
BibTeX
url - AQuA-CEP: Adaptive Quality-Aware Complex Event Processing in the Internet of Things (), In Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems (DEBS 2023), ACM press, 2023.
Abstract
Sensory data profoundly influences the quality of detected events in a distributed complex event processing system (DCEP). Since each sensor’s status is unstable at runtime, a single sensing assignment is often insufficient to fulfill the consumer’s quality requirements. In this paper, we study in the context of AQuA-CEP the problem of dynamic quality monitoring and adaptation of complex event processing by active integration of suitable data sources. To support this, in AQuA-CEP, queries to detect complex events are supplemented with consumer-definable quality policies that are evaluated and used to autonomously select (or even configure) suitable data sources of the sensing infrastructure. In addition, we studied different forms of expressing quality policies and analyzed how it affects the quality monitoring process. Various modes of evaluating and applying quality-related adaptations and their impacts on correlation efficiency are addressed, too. We assessed the performance of AQuA-CEP in IoT scenarios by utilizing the notion of the quality policy alongside the query processing adaptation using knowledge derived from quality monitoring. The results show that AQuA-CEP can improve the performance of DCEP systems in terms of the quality of results while fulfilling the consumer’s quality requirements. Quality-based adaptation can also increase the network’s lifetime by optimizing the sensor’s energy consumption due to efficient data source selection.
BibTeX
url - Towards adaptive quality-aware Complex Event Processing in the Internet of Things (), In Proceedings of the 18th International Conference on Mobility, Sensing and Networking (MSN 2022), IEEE, 2022.
Abstract
This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers' quality requirements. We show that the quality-based selection of sensor sources also extends the network's lifetime by optimizing the data sources' energy consumption.
BibTeX
urldoi - Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks (), In Processes, volume 9, 2021.
Abstract
By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.
BibTeX
urldoi - Digital Twins: an enabler for digital transformation (), Chapter in The Digital Transformation handbook, 2021.
- Unsupervised approach towards analysing the public transport bunching swings formation phenomenon (), In Public Transport, 2020.
- Dynamic Rule-Based Reasoning in Smart Environments (), Rijksuniversiteit Groningen, 2014.
- Dynamic Constraint Satisfaction with Space Reduction in Smart Environments (), In International Journal on Artificial Intelligence Tools, volume 23, 2014.
- Itemset-based Mining of Constraints for Enacting Smart Environments (), In Symposium on Activity and Context Modeling and Recognition, 2014.
- Architecture pattern for context-aware smart environments (), Chapter in Creating Personal, Social and Urban Awareness through Pervasive Computing (D. Riboni, B. Guo, P. Hu, eds.), IGI Global, 2013.
- Towards Context Consistency in a Rule-Based Activity Recognition Architecture (), In International Symposium on Ubiquitous Intelligence and Autonomic Systems, 2013.
- Dynamic Constraint Reasoning in Smart Environments (), In IEEE International Conference on Tools with Artificial Intelligence, 2013.
- Service-Oriented Architecture for Smart Environments (), In IEEE International Conference on Service Oriented Computing and Applications, 2013.
- Policy-Based Scheduling of Cloud Services (), In Scalable Computing: Practice and Experience, volume 13, 2012.
- Optimizing Energy Costs for Offices Connected to the Smart Grid (), In IEEE Transactions on Smart Grid, volume 3, 2012.
- Reduced Context Consistency Diagrams for Resolving Inconsistent Data (), In ICST Transactions on Ubiquitous Environments, volume 12, 2012.
- Cost-efficient Context-aware Rule Maintenance (), In Workshop on Context Modeling and Reasoning, 2012.
- Interpretation of Inconsistencies via Context Consistency Diagrams (), In Annual IEEE International Conference on Pervasive Computing and Communications, 2011.
- Optimizing Offices for the Smart Grid (), Technical report JBI 2011-12-01, University of Groningen, 2011.
- Concept mapping for faster QoS-Aware Web Service Composition (), In IEEE Conference on Service Oriented Computing and Applications, 2010.