Article
Computer Science, Theory & Methods
He Fang, Zhenlong Xiao, Xianbin Wang, Li Xu, Lajos Hanzo
Summary: The conventional device authentication of wireless networks relies on a security server and centralized process, resulting in long latency and risk of single-point failure. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to distributively authenticate users. The scheme utilizes received signal strength indicator (RSSI) and mobility trajectory (TRA) estimation, and outperforms existing benchmark schemes.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Chemistry, Analytical
Salvatore Gaglio, Giuseppe Lo Re, Gloria Martorella, Daniele Peri
Summary: This methodology proposes a programming pattern inspired by natural language to verify applications interacting with physical environments, reducing the need for ontologies and encouraging the adoption of symbolic code execution on resource-constrained devices. The rule-based system supports real-time verification of software under test on target devices, evaluating the effects of SUT execution on hardware, and generating test code to highlight potential software issues that may arise during repeated execution.
Article
Construction & Building Technology
Wenjie Chen, Qiliang Yang, Ziyan Jiang, Jianchun Xing, Shuo Zhao, Qizhen Zhou, Deshuai Han, Bowei Feng
Summary: This paper proposes a user-friendly programming language called SwarmL to address the development difficulties of fully distributed intelligent building system applications. SwarmL establishes a language model, an overall framework, and an abstract syntax to describe the static physical objects and dynamic execution mechanisms of fully distributed intelligent building systems. It also designs a computational scope-based communication mechanism to adapt to dynamically changing network topologies. The results demonstrate that SwarmL effectively reduces the programming difficulty and improves the development efficiency of fully distributed intelligent building system applications.
Article
Multidisciplinary Sciences
Yeon Sik Choi, Hyoyoung Jeong, Rose T. Yin, Raudel Avila, Anna Pfenniger, Jaeyoung Yoo, Jong Yoon Lee, Andreas Tzavelis, Young Joong Lee, Sheena W. Chen, Helen S. Knight, Seungyeob Kim, Hak-Young Ahn, Grace Wickerson, Abraham Vazquez-Guardado, Elizabeth Higbee-Dempsey, Bender A. Russo, Michael A. Napolitano, Timothy J. Holleran, Leen Abdul Razzak, Alana N. Miniovich, Geumbee Lee, Beth Geist, Brandon Kim, Shuling Han, Jaclyn A. Brennan, Kedar Aras, Sung Soo Kwak, Joohee Kim, Emily Alexandria Waters, Xiangxing Yang, Amy Burrell, Keum San Chun, Claire Liu, Changsheng Wu, Alina Y. Rwei, Alisha N. Spann, Anthony Banks, David Johnson, Zheng Jenny Zhang, Chad R. Haney, Sung Hun Jin, Alan Varteres Sahakian, Yonggang Huang, Gregory D. Trachiotis, Bradley P. Knight, Rishi K. Arora, Igor R. Efimov, John A. Rogers
Summary: This study presents a transient closed-loop system that combines a time-synchronized, wireless network of skin-integrated devices with an advanced bioresorbable pacemaker to control cardiac rhythms, track cardiopulmonary status, provide multihaptic feedback, and enable transient operation with minimal patient burden.
Article
Computer Science, Cybernetics
Michael Coblenz, Gauri Kambhatla, Paulette Koronkevich, Jenna L. Wise, Celeste Barnaby, Joshua Sunshine, Jonathan Aldrich, Brad A. Myers
Summary: Designing programming languages requires consideration of usability, which can be challenging due to high costs and learning time. The PLIERS process, which adapts traditional HCI methods for programming language design, was evaluated by designing Glacier and Obsidian languages. Empirical studies showed that languages designed using the PLIERS process were effective and revealed opportunities for language improvement.
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION
(2021)
Article
Chemistry, Multidisciplinary
Dimitrios Zaikis, Nikolaos Stylianou, Ioannis Vlahavas
Summary: The paper introduces a media analytics framework for the Greek language, which utilizes subjectivity similarities among related classification tasks and has the potential for application to other low-resource languages. Media analysis is crucial for obtaining valuable insights from subjective text types, such as social media posts and news articles, to improve various areas of business and customer experience. The proposed unified framework incorporates sentiment, emotion, irony, and hate speech detection, enhancing the classification effectiveness for each task.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Yi Zhao, Ke Xu, Jiahui Chen, Qi Tan
Summary: This article introduces the collaboration-enabled intelligent Internet architecture, which enhances the intelligence of the existing Internet architecture through collaboration to tackle complex scenarios. By providing hierarchical computing and exploiting hierarchical capabilities, intelligent algorithms within the Internet architecture can collaborate effectively. Additionally, flexible algorithmic modules are built to meet the specific requirements of the Internet architecture.
Article
Computer Science, Hardware & Architecture
Wei Wang, Rui Qu, Haijun Liao, Zhao Wang, Zhenyu Zhou, Zhongyuan Wang, Shahid Mumtaz, Mohsen Guizani
Summary: This article proposes a 5G MEC-based intelligent computation offloading framework for power robotic inspection tasks. It addresses the low-latency computation offloading problem using an artificial intelligence algorithm and provides simulation results to verify its superiority.
IEEE WIRELESS COMMUNICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Yakun Huang, Xiuquan Qiao, Schahram Dustdar, Jianwei Zhang, Jiulin Li
Summary: This paper proposes a decentralized and collaborative deep learning inference system, DeColla, which migrates DNN computations from the cloud center to IoT devices to improve efficiency and robustness. Experimental results show that DeColla outperforms other methods in terms of latency and resource usage.
Article
Chemistry, Analytical
Shadab Khan, Yash Veer Singh, Prasant Singh Yadav, Vishnu Sharma, Chia-Chen Lin, Ki-Hyun Jung
Summary: This paper proposes an intelligent bio-inspired autonomous surveillance system using underwater sensor networks, which improves the energy efficiency and lifespan of the network by employing the tunicate swarm algorithm for cluster head selection.
Article
Automation & Control Systems
Andrea Camisa, Ivano Notarnicola, Giuseppe Notarstefano
Summary: This article addresses the distributed mixed-integer linear programming setup in control applications, proposing a fully distributed algorithm that can provide accurate feasible solutions in finite time with low suboptimality bounds.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Guido Carnevale, Andrea Camisa, Giuseppe Notarstefano
Summary: This article focuses on studying an online version of the emerging distributed constrained aggregative optimization framework for applications in cooperative robotics. Inspired by an existing scheme, a distributed algorithm named projected aggregative tracking is proposed to solve the online optimization problem. The article proves the bounded dynamic regret and linear convergence rate in the static case. Numerical examples also demonstrate the effectiveness of the approach in a robotic surveillance scenario.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Telecommunications
Zaheer Khan, Janne J. Lehtomaki, Valerio Selis, Hamed Ahmadi, Alan Marshall
Summary: This paper explores the challenges of using smart directional antennas in handheld devices for mmWave/TeraHertz communications, proposes adaptive link discovery algorithms, and evaluates their performance. The study shows that one method provides guaranteed discovery and achieves low-latency rediscovery in 3D space through the use of inertial sensors and link prediction methods.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2021)
Article
Optics
Lifeng Ma, Jing Li, Zhouhui Liu, Yuxuan Zhang, Nianen Zhang, Shuqiao Zheng, Cuicui Lu
Summary: Intelligent algorithms have shown vigorous development in the field of nanophotonic devices, breaking the restrictions of traditional methods and predicting novel configurations. They are universal and efficient for different materials, structures, modes, wavelengths, etc.
CHINESE OPTICS LETTERS
(2021)
Article
Engineering, Aerospace
Enrico Lagona, Samuel Hilton, Andoh Afful, Alessandro Gardi, Roberto Sabatini
Summary: Recent advances in AI, sensing, and computing technologies have led to the development of new concepts for the safe and efficient operation of Distributed Space Systems in near-Earth orbits. These technologies enable higher levels of autonomy in small satellite constellations, facilitating a more responsive and resilient approach to Space-Based Space Surveillance.
Article
Computer Science, Information Systems
Lei Yang, Yingqi Gan, Jiannong Cao, Zhenyu Wang
Summary: This paper proposes a resource-based aggregation frequency controlling method, RAF, to optimize the aggregation frequencies of edge devices in a hierarchical model training framework. RAF reduces waiting time and maximizes resource utilization, while dynamically adjusting aggregation frequencies for fast convergence speed and high accuracy during model training.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Jiang Xiao, Huichuwu Li, Minrui Wu, Hai Jin, M. Jamal Deen, Jiannong Cao
Summary: This article introduces the latest research progress in wireless device-free human sensing (WDHS) technology, classifying the systems into different categories and discussing various sensing task types and motion granularity. The article also proposes a new research framework to summarize WDHS systems, and presents future research directions in terms of data collection, sensing methodology, performance evaluation, and application scenarios.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Lei Yang, Jiaming Huang, Wanyu Lin, Jiannong Cao
Summary: Personalized federated learning (PFL) provides personalized models that fit the local data distribution of each client. We propose a Group-based Federated Meta-Learning framework (G-FML) that adaptively divides clients into groups based on data distribution similarity to achieve personalized models. Experimental results show that our framework improves model accuracy by up to 13.15% compared to state-of-the-art federated meta-learning.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Theory & Methods
Juncen Zhu, Jiannong Cao, Divya Saxena, Shan Jiang, Houda Ferradi
Summary: Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices without exchanging local data samples. Existing centralized solutions have disadvantages, and blockchain has been identified as a potential solution. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Electrical & Electronic
Rongling Yu, Ping He, Heng Li, Jiannong Cao, Feiqi Deng
Summary: This article investigates the consensus problem of linear multi-agent systems (MASs) with unknown external disturbances under intermittent communication. Firstly, the distributed extended observer is utilized to observe the relative output information and unknown disturbance. Then, a distributed active disturbance rejection intermittent consensus protocol is proposed using the observer information. Finally, a simulation example is provided to demonstrate the effectiveness of the consensus protocol.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Hardware & Architecture
Zhenyu Ning, Chenxu Wang, Yinhua Chen, Fengwei Zhang, Jiannong Cao
Summary: Processors nowadays come with debugging features for program analysis, but the security of these features has been under-examined, especially with the introduction of a new debugging model by ARM. This article provides a comprehensive security analysis of ARM debugging features and discovers a new attacking surface that exists universally in platforms with ARM-A architecture. It also presents the Nailgun attack as an example, which exploits the debugging features to obtain sensitive information and execute arbitrary payloads. The article suggests potential mitigations and proposes a practical defense mechanism based on ARM virtualization technology.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Hongbo Jiang, Jigang Wen
Summary: This paper proposes a novel Graph-based Tensor Recovery model (Graph-TR) for accurate Internet anomaly detection. By incorporating non-linear proximity information using nearest neighbor graphs and graph Laplacian, the proposed approach outperforms state-of-the-art algorithms in terms of detection accuracy.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Jigang Wen
Summary: This paper studies a novel sparse measurement scheduling problem in network management. By taking advantage of low-rank features and using tensor completion, the authors propose a scheme that can accurately obtain complete network-wide monitoring data with a low sampling ratio. Experimental results show that their approach outperforms other tensor completion algorithms in terms of sample efficiency.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Kun Xie, Yudian Ouyang, Xin Wang, Gaogang Xie, Kenli Li, Wei Liang, Jiannong Cao, Jigang Wen
Summary: This article proposes a novel Deep Adversarial Tensor Completion (DATC) scheme based on Deep Learning techniques to recover missing network traffic data. Extensive experimental results using two public real-world network traffic datasets demonstrate that DATC can significantly improve recovery accuracy and capture the data distribution of traffic data, even with very low sampling ratios.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Artificial Intelligence
Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen
Summary: Dynamic graphs are graphs whose structure changes over time. Existing approaches only consider dynamic graphs as a sequence of changes in vertex connections, ignoring the asynchronous nature of the dynamics where the evolution of each local structure starts at different times and lasts for various durations. To address this, we propose a novel representation of dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). We also introduce a time-aware Transformer to embed the dynamic connections and ToEs into learned vertex representations, along with encoding time-sensitive information. Our approach outperforms the state-of-the-art in various graph mining tasks and is efficient for embedding large-scale dynamic graphs.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jinlin Chen, Jiannong Cao, Zhiqin Cheng, Yuvraj Sahni
Summary: This paper proposes a middleware called ManiWare, which provides a team-level programming abstraction and a manipulator-level plugin mechanism to improve the efficiency of cooperative tasks. Experimental results demonstrate that ManiWare facilitates the completion of cooperative tasks effectively.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Qianyi Chen, Jiannong Cao, Songye Zhu
Summary: Estimating the health conditions and predicting the future behaviors of engineering structures are important for the safe and efficient operations of a city. Data-driven solutions using statistical models generated from measurement data have gained popularity due to advances in ICTs and big data analytics algorithms. However, most existing studies lack real-world implementation, hindering the extensive development of data-driven methods. In this survey, we provide an overview of the past decade's data-driven structural health monitoring (SHM) systems and algorithms, focusing on real-world implementation and proposing solutions to implementation challenges.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Kai Liu, Chunhui Liu, Guozhi Yan, Victor C. S. Lee, Jiannong Cao
Summary: This paper explores the acceleration of Deep Neural Network (DNN) inference in Vehicular Edge Computing (VEC) while ensuring reliability. The authors analyze the need for balancing DNN inference acceleration and reliability in VEC and propose a cooperative partitioning and offloading (CPO) problem. They also introduce two approximation algorithms, SA(3) and FMtR, for maximizing inference reliability. Simulation results demonstrate the effectiveness of the proposed solutions.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Information Systems
Mingyue Wang, Yu Guo, Chen Zhang, Cong Wang, Hejiao Huang, Xiaohua Jia
Summary: Electronic Health Record (EHR) and its privacy have gained significant attention. Existing systems for EHR sharing are vulnerable to DDoS attacks and single point of failure. In this article, we propose MedShare, a decentralized framework that utilizes blockchain technology to establish a trusted platform for secure EHR sharing. Our system incorporates a constant-size attribute-based encryption scheme for fine-grained access control and supports efficient multi-keyword boolean search operations. Evaluation results on Ethereum demonstrate the efficiency of MedShare.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Ping Wang, Zhetao Li, Bin Guo, Saiqin Long, Suiming Guo, Jiannong Cao
Summary: This paper proposes an Incentive-based Truth Discovery (ITD) scheme that incentivizes credible workers to submit high-quality data, thereby enhancing the accuracy of truth discovery. It introduces an unmanned aerial vehicle (UAV)-assisted split-aggregation mechanism to improve truth discovery accuracy and evaluates workers' trust. It also evaluates data quality and proposes a trust meter to guide future recruitment efforts. The experimental results show significant improvements in truth discovery accuracy and reduced sensing cost.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
