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Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection

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Transformer-based Compound Correlation Miner for Smart Home ...

IEEE International Conference on Cloud networking (CLOUDNET)

作者： Andrew D’Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Information Warfare Division Air Force Research Laboratory Rome NY USA

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

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arXiv 2022年

作者： Yuan, Zhenyi Cavraro, Guido Singh, Manish K. Cortés, Jorge The Department of Mechanical and Aerospace Engineering UC San Diego United States The Power Systems Engineering Center National Renewable Energy Laboratory United States The Department of Electrical and Computer Engineering University of Minnesota United States

This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load flow

Constant step-size stochastic approximation with delayed updates

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Constant step-size stochastic approximation with delayed upd...

IEEE Conference on Decision and Control

作者： Aditya Mahajan Silviu-Iulian Niculescu Mathukumalli Vidyasagar department of Electrical and Computer Engineering McGill University Montreal QC Canada Laboratory of Signals and Systems (L2S) University of Paris-Saclay CNRS CentraleSupelec Inria Saclay Gif-sur-Yvette France department of Artificial Intelligence Indian Institute of Technology Hyderabad Kandi Telangana India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider constant step-size stochastic approximation with delayed updates. For the non-delayed case, it is well known that under appropriate conditions, the discrete-time iterates of stochastic approximation track the trajectory of a continuous-time ordinary differential equation (ODE). For the delayed case, we show in this paper that, under appropriate conditions, the discrete-time iterates track the trajectory of a delay-differential equation (DDE) rather than an ODE. Thus, delayed updates lead to a qualitative change in the behavior of constant step-size stochastic approximation. We present multiple examples to illustrate the qualitative affect of delay and show that increasing the delay is generally destabilizing but, for some systems, it can be stabilizing as well.

关键词： Sufficient conditions Asymptotic stability Ordinary differential equations Approximation algorithms Trajectory Delays

A vector almost-supermartingale convergence theorem and its applications

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A vector almost-supermartingale convergence theorem and its ...

IEEE Conference on Decision and Control

作者： Aditya Mahajan Silviu-Iulian Niculescu Mathukumalli Vidyasagar department of Electrical and Computer Engineering at McGill University Montreal QC Canada CNRS CentraleSupelec Inria Saclay Laboratory of Signals and Systems (L2S) University of Paris-Saclay Gif-sur-Yvette France department of Artificial Intelligence at the Indian Institute of Technology Hyderabad Kandi Telangana India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The almost-supermartingale convergence theorem of Robbins and Siegmund (1971) is a fundamental tool for establishing the convergence of various stochastic iterative algorithms including system identification, adaptive control, and reinforcement learning. The theorem is stated for non-negative scalar valued stochastic processes. In this paper, we generalize the theorem to non-negative vector valued stochastic processes and provide two set of sufficient conditions for such processes to converge almost surely. We present several applications of vector almost-supermartingale convergence theorem, including convergence of autoregressive supermartingales, delayed supermartingales, and stochastic approximation with delayed updates.

关键词： Sufficient conditions Temporal difference learning Stochastic processes Vectors System identification Iterative methods Adaptive control Convergence

LEO Satellite Access Network(LEO-SAN)

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中国通信（英文版） 2023年第7期20卷前插1-前插4页

作者： Zhenyu Xiao Yuanzhi He Chengchao Liang Haipeng Yao Zhili Sun Lipeng Zhu School of Electronics and Information Engineering Beihang UniversityBeijing Satellite Communication Commit-tee of China Communication Society School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqingChina Beijing University of Posts and Telecommunications Communication Networking with Institute for Communication Systems 5G&6G Innova-tion Centre University of Surrey Department of Electrical and Computer Engineering National University of Singapore

Low-Earth-Orbit satellite access network(LEO-SAN)can realize real-time and three-dimensional broad-band access on the earth anywhere and any *** has become a major development trend of satellite Internet and mobile communication networks,and has attracted more attention and research from academia and *** satellites can achieve global coverage through giant ***-pared with medium-and high-orbit satellites,LEO satellites have larger communication bandwidth and shorter communication delay,and they are becoming an integrated part of future 6G mobile communica-tion *** countries in the world have planned for LEO satellite constellations and spectrum allocation to support the process of implementation.

关键词： Special issues and sections Low earth orbit satellites Satellite communication Three-dimensional displays Broadband communication Real-time systems

Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

Modular Multilevel DC-DC Converter With Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

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CPSS Transactions on Power Electronics and Applications 2022年第1期7卷 37-48页

作者： Shanshan Zhao Yu Chen Shenghui Cui Jingxin Hu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Department of Electrical and Computer Engineering Seoul National University Korea Institute for Power Generation and Storage Systems E. ON Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype.

关键词： Multilevel converters Semiconductor devices Prototypes DC-DC power converters Transformers Valves Power electronics