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dataflow Management in the Internet of Things: Sensing,Control, and Security

Tsinghua science and Technology 2021年第6期26卷 918-930页

作者： Dawei Wei Huansheng Ning Feifei Shi Yueliang Wan Jiabo Xu Shunkun Yang Li Zhu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China Run Technologies Co. Ltd.Beijing 100192China School of Information Engineering Xinjiang Institute of EngineeringUrumqi 830023China School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China Northern Cloud Research Team Engineering University of PAPXi'an 710086China

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

关键词： Internet of Things(IoTs) dataflow security privacy management

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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IEEE International Conference on Cluster Computing

作者： Yang Zhou Fang Wang Zhan Shi Dan Feng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%.

关键词： Schedules Layout Interference Parallel processing Predictive models Throughput data models Resource management Decision trees Low latency communication

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GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Sun, Yanguang Xuan, Hanyu Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Big Data and Statistics Anhui University Hefei230601 China

Recently, biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local spatial information of diverse scales from convolutional operations to optimize initial features. A commonly neglected point in these methods is the long-range dependencies between feature pixels from different scale spaces that can help the model build a global structure of the object, inducing a more precise image representation. In this paper, we propose a novel Global-Local Collaborative Optimization Network, called GLCONet. Technically, we first design a collaborative optimization strategy from the perspective of multi-source perception to simultaneously model the local details and global long-range relationships, which can provide features with abundant discriminative information to boost the accuracy in detecting camouflaged objects. Furthermore, we introduce an adjacent reverse decoder that contains cross-layer aggregation and reverse optimization to integrate complementary information from different levels for generating high-quality representations. Extensive experiments demonstrate that the proposed GLCONet method with different backbones can effectively activate potentially significant pixels in an image, outperforming twenty state-of-the-art methods on three public COD datasets. The source code is available at: https://***/CSYSI/GLCONet. © 2024, CC BY.

关键词： Object detection

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Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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An Advanced data Fabric Architecture Leveraging Homomorphic Encryption and Federated Learning

arXiv

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arXiv 2024年

作者： Rieyan, Sakib Anwar News, Raisul Kabir Muntasir Rahman, A.B.M. Khan, Sadia Afrin Jawad Zaarif, Sultan Tasneem Alam, Golam Rabiul Hassan, Mohammad Mehedi Ianni, Michele Fortino, Giancarlo Department of Computer Science and Engineering School of Data and Sciences BRAC University 66 Mohakhali Dhaka1212 Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Informatics Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning. © 2024, CC BY.

关键词： Medical imaging

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Stabilization Guarantees of Human-Compatible Control via Lyapunov Analysis

Stabilization Guarantees of Human-Compatible Control via Lya...

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European Control Conference (ECC)

作者： Sirui Li Roy Dong Cathy Wu Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering the Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL USA Department of Civil and Environmental Engineering Laboratory for Information & Decision Systems the Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA

Autonomous vehicles (AVs) enable more efficient and sustainable transportation systems. Ample studies have shown that controlling a small fraction of AVs can smooth traffic flow and mitigate traffic congestion. However, deploying AVs to real-world systems is challenging due to transparency and safety concerns. An alternative approach deployable in the imminent future is human-compatible driving, where human drivers are guided by real-time instructions to stabilize the traffic. To respect human drivers’ reaction time $\Delta$, a class of piecewise-constant policies is considered, where periodic instructions are given at every $\Delta$ seconds to human drivers, who hold the instructed action constant until the next instruction. While previous work provides a basic theoretical analysis by considering a single driver setting in the absence of traffic, a principled control theoretic analysis that takes into account the full traffic system is lacking. This work uses Lyapunov stability theory to analyze piecewise-constant policies in a traffic system governed by the Optimal Velocity Model (OVM). We provide sufficient conditions for piecewise-constant controls with hold length $\Delta$ to stabilize the system. Numerical simulations demonstrate that our theoretical analyses closely follow simulated results, and can be used to interpret meaningful relationships between system parameters and maximum stable hold lengths.

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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No Victim Left Behind: Developing a Dual-Use LEO Satellite System for Real-Time Population Mapping and Everyday Connectivity 23

No Victim Left Behind: Developing a Dual-Use LEO Satellite S...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Ohashi, Takumi Noda, Futo Tomura, Takashi Laosunthara, Ampan Suzuki, Koudai Tanaka, Yuno Kishimoto, Maki Seto, Rie Sakamoto, Hiraku Tran, Gia Khanh Shirane, Atsushi Tokyo Institute of Technology Department of Transdisciplinary Science and Engineering Tokyo Japan Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Tokyo Institute of Technology Department of Electrical and Electronic Engineering Tokyo Japan Tokyo Institute of Technology Department of Civil and Environmental Engineering Tokyo Japan Tokyo Institute of Technology Department of Architecture and Building Engineering Tokyo Japan Meteorological Research Institute Department of Observation & Data Assimilation Tsukuba Japan Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350353952

Disasters often disrupt communication infrastructure, impeding damage assessment and rescue operations. This paper presents a method for real-time population distribution mapping during disasters using the widespread prevalence of smartphones and Low Earth Orbit (LEO) satellites. The system detects signals emitted by smartphones using LEO satellites equipped with directional antennas. By analyzing the received signal strength indicators (RSSI), it generates high-resolution population distribution maps in affected areas, providing critical information for resource allocation and rescue efforts. The system's design allows for functionality during both normal and emergency conditions, offering connectivity and location-based services in daily life while supporting disaster response efforts. A proof-of-concept experiment demonstrates the detection of smartphone signals from tall buildings, confirming the system's ability to distinguish between the presence and absence of smartphones in the observed area based on the detected spectra. This preliminary result suggests the feasibility of estimating population distribution by detecting leaked signals from smartphones during disasters. Further research is needed to address challenges such as precise localization, indoor signal detection, and system scalability. This research explores the integration of satellite technology with mobile devices for effective disaster management, contributing to the development of resilient and efficient emergency response strategies. © 2024 IEEE.

关键词： Directive antennas

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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