Sensor networks (SN) could be defined as networks of autonomous devices that can sense and/or act on physical or environmental conditions cooperatively. In sensor networks, data is typically sensed and sent to an aggr...
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ISBN:
(纸本)9781665451093
Sensor networks (SN) could be defined as networks of autonomous devices that can sense and/or act on physical or environmental conditions cooperatively. In sensor networks, data is typically sensed and sent to an aggregator that will process it with local AI or send it to a cloud with larger AI. This centralized architecture has drawbacks such as the aggregator having to receive and process a potentially huge amount of data, which results in power consumption that can be significant. In addition, the transmission of all the sensor data results in extra consumption of energy due to communication. To reduce the impact of this last point, on-edge computing allows data to be preprocessed at the sensor level. It is often used in the architecture of distributed sensor networks in which each node receives data from other nodes in the network and processes it with its local data. In this work, a distributed sensor network model aims to solve this problem while reducing the impact of data transmission on energy consumption. The proposed model is able to reduce the number of transmitted bits per node by 90% in a nodeto-node directed communication scenario. It is also capable of working with different AI paradigms depending on the required balance between energy consumption, application configurability, and accuracy. Finally, this model is capable of converging even in a network without complete interconnections between nodes.
Conditional Generative Adversarial networks (cGANs) are increasingly popular web-based synthesis services accessed through a query API, e.g., cGANs generate a cat image based on a "cat" query. However, cGAN-...
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ISBN:
(纸本)9798331530044;9798331530037
Conditional Generative Adversarial networks (cGANs) are increasingly popular web-based synthesis services accessed through a query API, e.g., cGANs generate a cat image based on a "cat" query. However, cGAN-based synthesizers can be stolen via adversaries' queries, i.e., model thieves. The prevailing adversarial assumption is that thieves act independently: they query the deployed cGAN (i.e., the victim), and train a stolen cGAN using the images obtained from the victim. A popular anti-theft defense consists in throttling down the number of queries from any given user. We consider a more realistic adversarial scenario: model thieves collude to query the victim, and then train the stolen cGAN. CLUES is a new collusive model stealing framework, enabling thieves to bypass throttle-based defenses and steal cGANs more efficiently than through individual efforts. Thieves collect queried images and train a stolen cGAN in a federated manner. We evaluate CLUES on three image datasets, e.g., MNIST, FashionMNIST and CelebA. We experimentally show the scalability of the proposed attack strategies against the number of thieves and the queried images, the impact of a classical noise-based defense, a passive watermarking defense and a JPEG-based countermeasure. Our evaluation shows that such a collusive stealing strategy gets close to 4 units of Frechet Inception Distance from a victim model. Our code is readily available to the research community: https://***/records/10224340.
In the study of signal estimation, the performance of distributed blind equalization over wireless sensor network (WSN) is significantly affected by channel conditions. However, there has been few studies investigatin...
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Cellular networks, renowned for their robustness and high availability, must consistently meet very stringent standards to ensure service provision and attract deployment in manufacturing industries that cannot tolera...
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ISBN:
(纸本)9798350362527;9798350362510
Cellular networks, renowned for their robustness and high availability, must consistently meet very stringent standards to ensure service provision and attract deployment in manufacturing industries that cannot tolerate any downtime. Despite the progressive introduction of numerous algorithms over the years across different layers of the 5G communication model to ensure packet delivery and decoding, jamming attacks have consistently disrupted connectivity. Detection and analysis of jamming attack characteristics, including their type and duration, provide comprehensive insights for security analysts to develop educated countermeasures. This paper demonstrates how computer vision can be employed on spectrograms to detect, classify, and log information on three types of jamming attacks in real-time within a 5G campus network. We evaluate the efficiency of our real-time jamming detector by analyzing detection latency, classification accuracy, and attack duration capture accuracy. Our results indicate that our Deep Learning classification model detects all tested jammers with remarkably high accuracy.
As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To ...
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ISBN:
(纸本)9798350369205;9798350369199
As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To maintain fast execution times while accommodating more complex simulations, it is essential to utilize the parallel computing capabilities of modern hardware. This paper discusses the need for an updated architectural design in the MATSim traffic simulation framework to take advantage of parallel computing infrastructures. We introduce a prototype that adapts the existing traffic simulation logic to a distributed parallel algorithm. Extensive benchmarks have been conducted to evaluate the prototype's performance and identify its limitations. The results demonstrate that the prototype performs up to 100 times faster than the current implementation. Based on these findings, we advocate for the integration of a distributed traffic simulation within the MATSim framework and outline necessary steps to enhance the prototype.
Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For inst...
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ISBN:
(纸本)9798331531317;9798331531300
Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (Multi-MDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.
This paper delves into the potential of leveraging federated learning (FL) techniques to revolutionize vehicle-to-everything (V2X) wireless communications. We examine the significant opportunities that FL offers in th...
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ISBN:
(纸本)9798350364279;9798350364262
This paper delves into the potential of leveraging federated learning (FL) techniques to revolutionize vehicle-to-everything (V2X) wireless communications. We examine the significant opportunities that FL offers in the V2X domain, including distributed model training, privacy preservation and enhanced communication efficiency. Concurrently, we spotlight the formidable challenges associated with adapting FL to the unique characteristics of highly dynamic and heterogeneous vehicular networks. These challenges encompass Non Independent Identically Distribution (Non-IID) data distribution, ensuring model reliability and addressing scoring disparities among participating vehicles. To navigate this intricate landscape, we propose novel research directions, including hierarchical FL, incentive mechanisms and the development of robust training algorithms, aimed at surmounting these obstacles. This paper draws upon valuable insights gleaned from an initial analysis, along with simulation results that underscore the efficacy of tailored FL designs in unlocking the transformative potential of collective learning within vehicular networks, while simultaneously mitigating systemic limitations.
The global state of a distributed system is most commonly stored in databases. Databases are responsible for ensuring that the data is available, durable, and correct at some level of consistency. However, this approa...
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ISBN:
(纸本)9783031809453;9783031809460
The global state of a distributed system is most commonly stored in databases. Databases are responsible for ensuring that the data is available, durable, and correct at some level of consistency. However, this approach has its limitations. The computing nodes responsible for the application tier need to constantly communicate over the network to be able to interact with the global state and this comes with a performance limitation. Our Traquest model concept allows us to manage the data and even provide full ACID properties on the application tier level instead of the database level. In most cases, the data can be stored right next to the computation even in the same runtime environment. Our prototype measurements imply, that in some cases a Traquest model-based distributed system can provide even magnitudes larger throughput than the fastest in-memory databases today. The Traquest model also introduces a workaround for the famous CAP theorem by introducing temporary availability.
The integration of distributed generators (DGs) poses great challenges to the topology, operation planning, control methods, and protection configuration of traditional distribution networks (DN). To solve the optimiz...
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This study investigates the impact of role fixation (RF) and role rotation (RR) scripts on learners' socially shared regulation (SSR) in computer-supported collaborative learning (CSCL) environments. The research ...
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ISBN:
(纸本)9798350361414
This study investigates the impact of role fixation (RF) and role rotation (RR) scripts on learners' socially shared regulation (SSR) in computer-supported collaborative learning (CSCL) environments. The research involved 25 undergraduate students from a university in central China, divided into RF and RR groups for an eight-week collaborative learning activity focused on the computernetworks course. Data were collected through self-report questionnaires and online discussions platform. Findings indicate that RF groups exhibited significantly higher levels of SSR and more balanced discussions compared to RR groups. RF groups fostered a more evenly distributed interaction network, enhancing cooperation and sharing. These results underscore the effectiveness of role fixation scripts in promoting SSR and improving collaborative learning outcomes.
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