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Toward Optimal Broadcast Mode in Offline Finding Network

IEEE Transactions on Mobile computing 2025年第7期24卷 6550-6565页

作者： Li, Tong Ding, Yukuan Liang, Jiaxin Zheng, Kai Zhang, Xu Pan, Tian Wang, Dan Xu, Ke Renmin University DEKE Lab and Information School Beijing100872 China Delft Netherlands 2012 Labs Shenzhen Huawei518129 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Tsinghua University Department of Computer Science and Technology Beijing100084 China

This paper proposes ElastiCast, a novel Bluetooth Low Energy (BLE) broadcast mode that reduces the neighbor discovery latency in offline finding networks (OFNs). ElastiCast adapts the broadcast mode of the lost devices to the scan modes of the finder devices, considering their diversity. We start with an overview of OFNs, followed by a detailed analysis of the issues and challenges of existing solutions, which motivates the design of ElastiCast. Then we provide Blender, a simulator that models the neighbor discovery behavior of different broadcasters and scanners. By adopting Blender, ElastiCast can be implemented with three components: Local Optima Estimation, Common Interest Extraction, and Interval Multiplexing, in which we capture the key features of BLE neighbor discovery and globally optimize the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget. © 2002-2012 IEEE.

关键词： Bluetooth

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Factors influencing COVID-19 infection in the countries around the world 4

Factors influencing COVID-19 infection in the countries arou...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2024

作者： Sun, Ning Xiong, Jianling Tang, Tian School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510681637

This paper aims to analyze the impact of various factors on COVID-19 infection rates across different countries. Data on social, demographic, and economic indicators (comprising 16 variables) from 195 countries, as well as infection and mortality data up to January 8, 2023, were collected. Methodologies including factor analysis, cluster analysis, multiple stepwise linear regression, and random forest were applied. Factor analysis revealed four distinct categories (humanity, vaccination, economics, and living), while cluster analysis identified a total of 14 cluster categories. Results indicated that humanity had the most significant impact on both human infections and deaths, followed by economics, vaccination, and living conditions. Moreover, vaccination protection was found to be limited, with advanced age and poor living conditions identified as significant risk factors for infection. © 2024 SPIE.

关键词： COVID-19

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Linear Subspace Surrogate Modeling for Large-Scale Expensive Single/Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2023年第3期29卷 1-1页

作者： Si, Langchun Zhang, Xingyi Tian, Ye Yang, Shangshang Zhang, Limiao Jin, Yaochu School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Artificial Intelligence Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Faculty of Technology Bielefeld University Bielefeld Germany

Despite that the surrogate-assisted evolutionary algorithms have achieved great success in addressing expensive optimization problems, they still suffer from stiff challenges when the number of dimensions of problems becomes large. The primary reason lies in that it is very hard to build an acceptable surrogate model in the high-dimensional search space with small amounts of evaluated historical data in evolution. To tackle this issue, we suggest an effective surrogate modeling method for large-scale expensive optimization in this paper, where the models are built on a number of linear subspaces instead of the original search space. Specifically, a linear subspace is constructed by a pair of points/solutions which are generated based on the set of elite solutions. For each linear subspace, several historical solutions are first associated according to their distance to the linear subspace, and then a surrogate model is trained by the associated solutions and used to evaluate the offspring. To ensure the exploration and exploitation capacity of the proposed method, these linear subspaces and the surrogate models are updated after a few iterations. Experimental results on CEC’2010 and CEC’2013 single-objective optimization problems with up to 1500 decision variables show that the proposed algorithm is superior over six comparison algorithms. Moreover, we also extend the proposed algorithm to multi-objective optimization problems and verified its competitiveness on problems with up to 1500 decision variables. IEEE

关键词： Evolutionary algorithms

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EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element

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Digital Communications and Networks 2023年第2期9卷 358-366页

作者： Chintan Patel Ali Kashif Bashir Ahmad Ali AlZubi Rutvij Jhaveri School of Artificial Intelligence Information Technology and Cyber SecurityRashtriya Raksha UniversityGujaratIndia Department of Computing and Mathematics Manchester Metropolitan UniversityUnited Kingdom Woxsen School of Business Woxsen UniversityIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Computer Science Department Community CollegeKing Saud UniversityP.O.Box 28095Riyadh11437Saudi Arabia Department of Computer Science and Engineering School of TechnologyPandit Deendayal Energy UniversityGujaratIndia

Industrial IoT(IIoT)aims to enhance services provided by various industries,such as manufacturing and product *** suffers from various challenges,and security is one of the key challenge among those *** and access control are two notable challenges for any IIoT based industrial *** IoT based Industry 4.0 enterprise designs networks between hundreds of tiny devices such as sensors,actuators,fog devices and ***,articulating a secure authentication protocol between sensing devices or a sensing device and user devices is an essential step in IoT *** this paper,first,we present cryptanalysis for the certificate-based scheme proposed for a similar environment by Das et *** prove that their scheme is vulnerable to various traditional attacks such as device anonymity,MITM,and *** then put forward an interdevice authentication scheme using an ECC(Elliptic Curve Cryptography)that is highly secure and lightweight compared to other existing schemes for a similar ***,we set forth a formal security analysis using the random oracle-based ROR model and informal security analysis over the Doleve-Yao *** this paper,we present comparison of the proposed scheme with existing schemes based on communication cost,computation cost and security index to prove that the proposed EBAKE-SE is highly efficient,reliable,and trustworthy compared to other existing schemes for an inter-device *** long last,we present an implementation for the proposed EBAKE-SE using MQTT protocol.

关键词： Internet of things Authentication Elliptic curve cryptography Secure key exchange Message Queuing telemetry transport

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Beyond the Commute: Unlocking the Potential of Electric Vehicles as Future Energy Storage Solutions (Vision Paper) 24

Beyond the Commute: Unlocking the Potential of Electric Vehi...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic information Systems, ACM SIGSPATIAL 2024

作者： Cheema, Muhammad Aamir Wang, Hao Wang, Wei Toosi, Adel N. Tanin, Egemen Qi, Jianzhong Samet, Hanan Faculty of Information Technology Monash University Australia Guangzhou China HKUST Hong Kong Hong Kong School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science University of Maryland United States

ISBN: (纸本)9798400711077

Electric vehicles (EVs) have the potential to serve as energy storage solutions through bidirectional charging technology, which allows them to both draw power from and feed power back into the grid, homes, or other vehicles. This capability enables EVs to reduce emissions, optimize costs, and support the grid by storing energy during periods of high production and supplying it when demand is high. In this vision paper, we focus on unlocking the potential of EVs as energy storage solutions while ensuring they remain readily available for transportation, their primary purpose. A significant research gap exists in that most current studies prioritize energy management, often using simplistic approaches that inadequately address the travel needs of EV owners. We believe the database community can be instrumental in maximizing the dual role of EVs as transportation and energy storage. We present a non-exhaustive list of research directions for various EV stakeholders, including individual EV owners, groups of independent yet cooperative EVs, commercial EV fleets, and autonomous EVs, and hope to inspire the database community for further exploration. © 2024 Copyright held by the owner/author(s).

关键词： Electric Vehicles

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Securing Financial Applications from Inception: A Comprehensive Security Design Review 9th

Securing Financial Applications from Inception: A Comprehens...

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Aadil, Mushad Ohanyan, Chris Tahir, Sabeen Mohaisen, Linda Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Western Avenue CardiffCF5 2YB United Kingdom Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819739721

In today's ever-changing world of financial technology, safeguarding customer data is a top priority. This paper underscores the critical importance of a thorough security design review and offers a practical solution for integrating security measures right from the start of a project. It highlights the identification of key security objectives, a comprehensive vulnerability assessment, effective mitigation strategies, security-conscious API design and coding practices, data protection, user privacy considerations, and robust authentication and authorisation mechanisms. This paper serves as a practical guide for developers to craft cutting-edge financial applications that prioritise security, guaranteeing data integrity and earning customer confidence from the outset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Detecting and Diagnosing Alzheimer's Disease: A Transformer-Based Survey (DAD-T) 4

Detecting and Diagnosing Alzheimer's Disease: A Transformer-...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Selvam, Prabu Gunasundari, C. Tamilselvi, M. Haneef, Pinjari Suresh, T. Jini, R. School of Computing Srm Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Roever Engineering College Department of Computer Science & Engineering Tiruchirapalli India India K. Ramakrishnan College of Engineering Department of Aiml Tiruchirapalli India Sri Muthukumaran Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. The Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. This paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. This paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Cluster-Phys: Facial Clues Clustering Towards Efficient Remote Physiological Measurement 24

Cluster-Phys: Facial Clues Clustering Towards Efficient Remo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Qia, Nwei Li, Kun Guo, Dan Hu, Bin Wang, Meng School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology Hefei China CCAI Zhejiang University Hangzhou China Hefei Comprehensive National Science Center Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9798400706868

Remote photoplethysmography (rPPG) measurement aims to estimate physiological signals by analyzing subtle skin color changes induced by heartbeats in facial videos. Existing methods primarily rely on the fundamental video frame features or vanilla facial ROI (region of interest) features. Recognizing the varying light absorption and reactions of different facial regions over time, we adopt a new perspective to conduct a more fine-grained exploration of the key clues present in different facial regions within each frame and across temporal frames. Concretely, we propose a novel clustering-driven remote physiological measurement framework called Cluster-Phys, which employs a facial ROI prototypical clustering module to adaptively cluster the representative facial ROI features as facial prototypes and then update facial prototypes with highly semantic correlated base ROI features. In this way, our approach can mine facial clues from a more compact and informative prototype level rather than the conventional video/ROI level. Furthermore, we also propose a spatial-temporal prototype interaction module to learn facial prototype correlation from both spatial (across prototypes) and temporal (within prototype) perspectives. Extensive experiments are conducted on both intra-dataset and cross-dataset tests. The results show that our Cluster-Phys achieves significant performance improvement with less computation consumption. The source code will be available at https://***/VUT-HFUT/ClusterPhys. © 2024 ACM.

关键词： Photoplethysmography

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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