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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ivan Zyrianoff Leonardo Montecchiari Angelo Trotta Lorenzo Gigli Carlos Kamienski Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro”. University of Bologna Italy Technology Innovation Institute (TIl) United Arab Emirates Federal University of ABC (UFABC) Brazil

In edge-cloud IoT scenarios, proactive caching strategies constitute an effective solution to optimize the use of resources while ensuring adequate Age of Information (Aol). However, the implementation of these strategies introduces significant privacy constraints, primarily stemming from the transmission of sensitive data to the cloud. To address such issue, Federated Learning (FL) has emerged as a promising approach which processes data at the edge, transmitting only the model updates to the cloud. This paper introduces CACHUUM (Cache Architecture for Cloud and Heterogeneous edge in the ContinUUM), a proactive and privacy-aware architecture designed to facilitate the deployment of various edge caching strategies within distributed edge environments. Our architecture supports three families of strategies: local, global and federated, each tailored to meet specific privacy requirements. Furthermore, our architecture is continuum-aware, accommodating different data caching locations, whether it be at the edge node, in the cloud, or somewhere in between. We demonstrate the effectiveness of CACHUUM on simulated IoT environments, by collecting metrics on forecast accuracy, caching precision and data overhead, for different strategies. The latter anticipate the optimal cache update timings for each IoT device, ensuring that Aol aligns with application requirements upon data request.

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

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arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

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A BLE Mesh Edge Framework for QoS-Aware IoT Monitoring systems

A BLE Mesh Edge Framework for QoS-Aware IoT Monitoring Syste...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Leonardo Montecchiari Angelo Trotta Ivan D. Zyrianoff Luciano Bononi Enrico Natalizio Marco Di Felice Technology Innovation Institute (TII) United Arab Emirates Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Internet of Things (IoT) monitoring applications of-ten require a flexible network design to continuously adjust device configurations and meet Quality of Service (QoS) requirements effectively. In this paper, we provide evidence of this concept in the context of the Bluetooth Low Energy (BLE) Mesh network, where performance heavily relies on tuning various network parameters. Our contributions in this study are threefold. First, we present a software framework designed for BLE Mesh monitoring, QoS metrics computation, and QoS-aware parameter selection. This framework operates on an edge device connected to the BLE gateway, enabling efficient data gathering and decision-making. Second, we propose a Reinforcement Learning (RL) algorithm to determine the optimal BLE Mesh parameters, allowing for the simultaneous fulfillment of multiple QoS requirements. Through the RL technique, we can dynamically adapt network configurations to varying conditions and demands. Third, we conduct thorough validation of our framework on a real-world BLE Mesh test-bed, demonstrating the effectiveness of the proposed technique in network performance enhancement. The results show the ability to maximize Packet Delivery Ratio (PDR), minimize delay, or determine a suitable trade-off between these two metrics, depending on the specific user's needs.

关键词： Performance evaluation Mesh networks Software algorithms Quality of service Reinforcement learning Logic gates Software

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Distributed Service Discovery over Heterogeneous Robotic systems-of-systems

Distributed Service Discovery over Heterogeneous Robotic Sys...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Leonardo Montecchiari Angelo Trotta Lorenzo Gigli Lorenzo Bellone Marco Di Felice Enrico Natalizio Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Technology Innovation Institute (TII) United Arab Emirates

In robotic swarms, nodes must cooperate in order to accomplish complex tasks. Robot interactions may occur at run-time based on the dynamic network topology and on mission-specific goals. For this reason, service discovery mechanisms are fundamental in order to inform each robot about the functionalities offered by the other peers. Although several network discovery protocols have been proposed so far, none of them fits the unique characteristics of robotic swarms in terms of dynamic topology, communication heterogeneity, and latency requirements. In this paper, we fill such gap by designing, implementing and evaluating a novel service discovery mechanisms for generic Robotic systems-of-systems (RSoS). The study proposes algorithmic and practical contributions. Regarding the first, we describe a novel distributed service discovery algorithm for RSoS which adapts to highly mobile robotic environments while limiting the network overhead and latency. Regarding the latter, we implemented the proposed algorithm within the Uhura swarm robotics framework; as a result, our solution is able to support multi-radio scenarios where robots are provided with heterogeneous Machine-to-Machine (M2M) communication technologies. In addition, we validate our solution through large-scale simulations and a test-bed in which ground robots are able to discover a Federated Learning (FL) task and join it at run-time to improve the accuracy.

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Autonomic Faulty Node Replacement in UAV-Assisted Wireless Sensor Networks: a Test-bed

Autonomic Faulty Node Replacement in UAV-Assisted Wireless S...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Leonardo Montecchiari Angelo Trotta Luciano Bononi Marco Di Felice Enrico Natalizio Department of Computer Science and Engineering University of Bologna Italy Technology Innovation Institute (TII) United Arab Emirates Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows.

关键词： Wireless sensor networks systems operation Robot kinematics Spread spectrum communication Data collection Robot sensing systems Feature extraction

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Revealing Non-Hermitian Band Structure of Photonic Floquet Media

Revealing Non-Hermitian Band Structure of Photonic Floquet M...

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Frontiers in Optics, FiO 2022

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Center for Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally reveal the unique non-Hermitian band structures of a photonic Floquet medium emulated in the microwave regime. We observe the presence of the momentum gaps and the subharmonic parametric oscillations...

ISBN: (纸本)9781557528209

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Analysis of Shoping Intention Using E-Commerce during Pandemic 8

Analysis of Shoping Intention Using E-Commerce during Pandem...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Wijaya, Alessandro Bryan Tio, Fendy Malaysiani, Liza Azzahra Sanjaya, Sandi Martin, Yessin Rio Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665416979

This study aims to investigate people's shopping intentions using e-commerce during the pandemic in Indonesia. The method used in this research is the quantitative method in which the results of the study are used as an assumption and research discussion. Data collection was carried out by questionnaires then measure using t-test. The result can be concluded that the intention to shop using e-commerce during the COVID-19 pandemic is influenced using e-commerce features and e-commerce techniques. These results indicate that the intention to shop using e-commerce can increase if the availability of goods in e-commerce is quite complete, the quality of goods sold is very high, normal prices and technical requirement, such as speed, security, and a good e-commerce design system. © 2021 IEEE.

关键词： COVID-19

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A requirement analysis method for the design of the upgradable product-service system

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Procedia CIRP 2023年 119卷 402-407页

作者： Hanfei Wang Yuya Mitake Yusuke Tsutsui Salman Alfarisi Yoshiki Shimomura Faculty of Systems Design Tokyo Metropolitan University Asahigaoka 6-6 Hino-shi Tokyo 191-0065 Japan Human Augmentation Research Center National Institute of Advanced Industrial Science and Technology Chiba 277-0882 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan

For extending the life cycle of products and satisfying the further requirement of customers, product-service system (PSS), an integration of products and services is considered as a promising solution. However, for PSS design, customers’ expectations on the purchased product could be changed due to the development of technology and fashion. For this reason, the life of value might be exhausted before the real life of a product. Although there are some existing upgrade plans or maintenance plans for PSS design, it is found to be risky for PSS firms to provide the mentioned plans that are threatened by various uncertain events. Thus, to achieve an upgradable PSS, it is extremely essential to propose a method to identify the requirement for upgradability based on the perspective of customers. To achieve this aim, this paper has developed a method includes four steps. In step one, a set of definitions is provided to clarify the upgradability of PSS. In step 2, this study utilizes a PEST-OT matrix to analyze the driver of the future development. Step3 uses a persona-based explorative scenario building method to identify the characteristic of targeted customer and build a scenario of interaction between customer and PSS in a future scenario. Step 4 provides a requirement transformation method to transform the requirement into receiver state parameter (RSP). After that, the RSP is transformed into detailed product characteristics (PChs) and service characteristics (SChs). To verify the effectiveness, a case study of shared furniture is used. The result of the case study shows this method could work effectively in a Business-to-Customer business.

关键词： upgradable product-service system customer requirement analysis scenario building upgrade strategy

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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