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33rd USENIX Security Symposium, USENIX Security 2024

作者： Wei, Jianghong Tian, Guohua Wang, Ding Guo, Fuchun Susilo, Willy Chen, Xiaofeng Xidian University China State Key Laboratory of Mathematical Engineering and Advanced Computing China College of Cyber Science Nankai University China School of Computing and Information Technology University of Wollongong Australia

ISBN: (纸本)9781939133441

Multi-signature schemes have attracted considerable attention in recent years due to their popular applications in PoS blockchains. However, the use of general multi-signature schemes poses a critical threat to the security of PoS blockchains once signing keys get corrupted. That is, after an adversary obtains enough signing keys, it can break the immutable nature of PoS blockchains by forking the chain and modifying the history from some point in the past. Forward-secure multi-signature (FS-MS) schemes can overcome this issue by periodically updating signing keys. The only FS-MS construction currently available is Drijvers et al's Pixel, which builds on pairing groups and only achieves forward security at the time period level. In this work, we present new FS-MS constructions that either are free from pairing or capture forward security at the individual message level (i.e., fine-grained forward security). Our first construction Pixel+ works for a maximum number of time periods T. Pixel+ signatures consist of only one group element, and can be verified using two exponentiations. It is the first FS-MS from RSA assumption, and has 3.5x and 22.8x faster signing and verification than Pixel, respectively. Our second FS-MS construction Pixel++ is a pairing-based one. It immediately revokes the signing key's capacity of re-signing the message after creating a signature on this message, rather than at the end of the current time period. Thus, it provides more practical forward security than Pixel. On the other hand, Pixel++ is almost as efficient as Pixel in terms of signing and verification. Both Pixel+ and Pixel++ allow for non-interactive aggregation of signatures from independent signers and are proven to be secure in the random oracle model. In addition, they also support the aggregation of public keys, significantly reducing the storage overhead on PoS blockchains. We demonstrate how to integrate Pixel+ and Pixel++ into PoS blockchains. As a proof-of-concept, we prov

关键词： Blockchain

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Blockchain-Based Intelligent Digital Credentialing System for Participatory Governance: Design, Implementation, and Potential Implications

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SN Computer science 2024年第8期5卷 1-15页

作者： Dhasarathan, Chandramohan Rajaguru, Dayalan Subash Chandra Bose, J. Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information Technology Perunthalaivar Kamarajar Institute of Engineering and Technology Karaikal Puducherry India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India

This research proposes a blockchain-based digital credentialing system for participatory governance to address issues with centralized, opaque, and insecure standard credential systems. Leveraging the Hyperledger Fabric platform and smart contracts, the proposed method increases credential data’s transparency, security, and immutability, resulting in a more efficient and cost-effective solution. The paper covers the technological design of the system, including the use of Hyperledger Fabric components and the incorporation of the digital credential system. It also discusses the potential benefits and challenges of the system, highlighting its potential to increase trust and accountability in participatory governance. This paper presents a promising solution for improving digital credentialing using blockchain technology with implications for participatory governance. It explores a participatory administration’s social and political consequences via a blockchain-enabled innovative certification system, including greater citizen engagement and participation and enhanced credibility and legitimacy of governance processes. However, it acknowledges data privacy issues and potential power imbalances. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence Blockchain technology Digital credentialing system Hyperledger Smart contract

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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud 5

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jesy Janet Kumari, J. Thangam, S. Saleem Raja, A. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas It Department Oman

ISBN: (纸本)9798350385649

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption. © 2024 IEEE.

关键词： Quality of service

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Deep-CNWO: a deep-chaotic nature whale optimization algorithm for early prediction of blood pressure disorder in smart healthcare settings

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Neural computing and Applications 2024年第24期36卷 15117-15136页

作者： Motwani, Anand Shukla, Piyush Kumar Pawar, Mahesh Arya, Monika Jain, Paras School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan MP Sehore466114 India Department of Computer Science & amp Engineering University Institute of Technology RGPV MP Bhopal462033 India Department of Computer Science & amp Engineering CMR Engineering College Kandlakoya Medchal Telangana Hyderabad501401 India Department of Information Technology University Institute of Technology RGPV MP Bhopal462033 India

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, ‘Deep-Chaotic Nature Whale Optimization’ (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

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Computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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OLBR: Optimization of Location-Based Routing Protocols in Vehicular Ad Hoc Networks

OLBR: Optimization of Location-Based Routing Protocols in Ve...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Thangam, S. Srikar Reddy, M. Vivek Ullas, Karthik Khadar Jilani, N. Abdul Venkatesh, K. Janet Kumari, J. Jesy Saleem Raja, A. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

The proposed work employs ns-3, SUMO, and NetAnim to create geographical routing in vehicular ad hoc networks (VANETs). The research attempts to assess the performance of three well-known protocols AODV, DSDV, and OLSR in VANET settings. Utilizing SUMO's realistic mobility models and NS3's simulation capabilities, the project simulates node communication, protocol behaviour, and vehicle movement. The main purpose is to know how well various routing techniques work in dynamic vehicular situations. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Study on the charge structure in thundercloud at LHAASO observatory 38

Study on the charge structure in thundercloud at LHAASO obse...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Yiyuan Jing, Hao Zhang, Jixin Wang, Yuxi Chang, Wanzhu Yang, Ci School of Physical Science and Technology Southwest Jiaotong University Sichuan Chengdu611756 China School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu611756 China School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China

Thunderstorms, accompanying with lighting flashes, are frequent phenomena at high altitudes. The intensity and polarity of thunderstorm electric field will change dramatically. The charge structure in thundercloud is one of the hottest topics in atmospheric electricity. To know more about the thunderstorm effects on the variation of cosmic rays detected by ground-based experiments, the thunderstorm activity at LHAASO observatory and the charge distribution in thundercloud are studied in this work. The diurnal variations of thunderstorms are analyzed. We find the thunderstorms are most active in late afternoon to evening, even a few of them occurring at midnight. Based on disk model, the near-earth electric field is calculated with different charge structures in *** the same time, using the electric field data from two ground observation stations, an inversion method is proposed to study the charge distribution in thundercloud. The evolution of several typical thunderstorm processes at LHAASO observatory are analyzed in detail. Our results will be helpful in understanding the acceleration mechanism of secondary cosmic rays caused by an atmospheric electric field,the cosmic ray variation detected by LHAASO during thunderstorms, and also be useful in the study of atmospheric physics. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Thangam, S. Jahnavi, M. Chandana, K. Dheeraj, K. Janet Kumari, J Jesy Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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