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Resources in IoT Through Bctechnology 3

Resources in IoT Through Bctechnology

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3rd international Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar Goyal, Manish Sharma, Karishma Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

With the exponential rise in IoTapplicationit difficult to keep up with the demand for services with lower latency, higher bandwidth, easier accessibility, and advanced intelligence. To meet such demand the distributed and decentralized computing architectureisproposed. Edge-centric computing resources don't need to be managed by a distant but centralized third party can managed locally and individually. However, because to a lack of trust between IoT devices and , obstacles (ECNs). The trust-absence issue can be effectively resolved by a blockchain, which is a decentralized, trustless, and immutable public ledger. discuss the concerns associated with we outline the main features of blockchains that make them ideal for edge-centric IoT applications. In addition, we offer a broad architecture that outlines the sequential steps required to complete for scenarios based on blockchain and edge computing that enable IoT. Additionally, to demonstrate how artificial intelligence (AI) and blockchains may function together, we create a We also go through the advantages of blockchains and AI coming together. The simulation's findings are then displayed. © 2023 IEEE.

关键词： Blockchain

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SSG-FLIM: Federated Learning Incentive Mechanism for Privacy Protection and Data Trading in IoT 22

SSG-FLIM: Federated Learning Incentive Mechanism for Privacy...

引用

22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Zeng, Siqin Wu, Xiaohong Gu, Yonggen Tao, Jie Huzhou University School of Information Engineering Huzhou313000 China

ISBN: (纸本)9798331509712

In this data-driven era, ensuring the effective utilization of data while protecting privacy, especially on devices in IoT environments, has become a critical challenge. We explore the data trading market in IoT networks under the federated learning framework, focusing on preventing mobile devices from excessively misreporting their privacy valuations and enhancing data trading efficiency under differential privacy, controlled by the cloud server (model buyer). To address this challenge, we introduce a group-based Stackelberg game model where mobile devices are randomly divided into several groups. Devices report their privacy valuations and optimize their privacy budget strategies, while the model buyer allocates and optimizes budget strategies. The proposed group-based Stackelberg Game-based Federated Learning Incentive Mechanism (SSG-FLIM) enables mobile devices to disclose their true privacy valuations, allowing the server to balance the conflict between privacy protection and data utility, thereby enhancing the efficiency of data trading. The theoretical analysis suggests that, when compared to the existing TDFL-IM method, the SSG-FLIM mechanism with its random grouping approach can significantly enhance the utilization efficiency of the cloud server. © 2024 IEEE.

关键词： Federated learning

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GPSSE: A GPU-Accelerated Dynamic SSE Scheme with Efficient Batch Updating 22

GPSSE: A GPU-Accelerated Dynamic SSE Scheme with Efficient B...

引用

22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Zhou, Jiancong Zhu, Xiaojie Li, Yong Yuan, Shuguang Chen, Chi King Abdullah University of Science and Technology Thuwal Saudi Arabia Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Key Laboratory of Cyberspace Security Defense Beijing China

ISBN: (纸本)9798331509712

Dynamic Searchable Symmetric Encryption (DSSE) allows cloud users to securely retrieve and update their data while outsourcing it to untrusted cloud service providers. Although extensive research efforts in recent years have notably improved the retrieval efficiency of DSSE, there remains potential for enhancing update efficiency, particularly in large-scale dataset updating scenarios. Therefore, we proposed a pioneering scheme called GPSSE (GPU-Accelerated Dynamic Searchable Symmetric Encryption Scheme) to explore accelerating batch updates of DSSE through GPU. In our design, we break the traditional chain-based data structure and build independent label-based data blocks to store entries. It facilitates the parallel updating of entries. Moreover, GPSSE achieves forward privacy and Type II backward privacy. In addition, we formally prove the security of the proposed GPSSE and show its practicality by conducting experiments using the publicly well-known Enron Email dataset. Experimental results demonstrate that GPSSE outperforms 40× to 194156× than other state-of-the-art schemes in updating efficiency. © 2024 IEEE.

关键词： Differential privacy

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Content Style-triggered Backdoor Attack in Non-IID Federated Learning via Generative AI 21

Content Style-triggered Backdoor Attack in Non-IID Federated...

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21st IEEE international symposium on Parallel and distributed Processing with applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cheng, Jinke Li, Gaolei Lin, Xi Peng, Hao Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering China Zhejiang Normal University Computer and Information Sciences China

ISBN: (纸本)9798350329223

Federated learning (FL) enables collaborative model training over multiple unvetted participants' data, making it vulnerable to backdoor attacks. While existing studies have presented many efficient methods to improve the persistence and stealthiness of those attacks, the assumption about the configurable space of adversaries may still be too conservative, as attackers may utilize any tool (e.g., Generative AI) to generate poisoned samples, especially under non-IID scenarios, where not every client has data for all styles. In this paper, we warn that the generative AI technique can be weaponized to produce poisoned samples, which is essential to implant hidden backdoors into the FL-based systems. Specifically, a novel content style-triggered backdoor attack (CSBA) scheme is designated to raise up more attention. The CSBA mainly consists of two processes: 1) style transferring-based poisoned data generation and 2) federated backdoor implantation. In the former stage, we use CycleGAN and Stable Diffusion to construct poisoned samples with specific styles, respectively, and mark them as predefined target labels. For the latter, a standard federated backdoor implantation process is executed and ultimately achieves a backdoored model that only is sensitive to inputs with a specific style. Experiments based on CIFAR10 and ImageNet datasets validate the effectiveness of the proposed methods. © 2023 IEEE.

关键词： Backdoor attack Federated learning Generated AI Non-IID Style transfer

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Identifying anti-tumor heat shock proteins based on evolutionary information using deep learning method 21

Identifying anti-tumor heat shock proteins based on evolutio...

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21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Fu, Yi Zhao, Ji Mei, Juan Ding, Yi School of Internet of Things Engineering Wuxi City College of Vocational Technology Wuxi China Wuxi Research Center for Environmental Science & Engineering Wuxi China

ISBN: (纸本)9781665454629

Heat shock proteins (HSPs) belong to stress proteins. The functions of HSPs are mainly reflected in three aspects: molecular chaperones, regulation of apoptosis and immune responses. Recent studies have shown that there is a certain correlation between HSPs and tumor cell. HSPs are participated in the invasion, proliferation and metastasis of tumor cells. Therefore, developing an accurate model for identification anti-tumor HSPs is a key step to understand molecular functions of HSPs and human tumor diseases. In this study, we propose using deep learning methods to identify anti-tumor HSPs. To seek out the optimal model for the dataset, several hyper-parameters are optimized according to the results of 10-fold cross-validation. Finally, the performance of the proposed model is further determined through an independent dataset. The experimental results indicated that the proposed model could classify anti-tumor HSPs with accuracy (ACC) of 93.76%, sensitivity (SN) of 92.80%, specificity (SP) of 93.33%, and Matthew's correlation coefficient (MCC) of 86.39% on the 10-fold cross-validation. Compared with other deep learing methods, using convolutional neural network (CNN) can achieve a significant improvement for identifying of anti-tumor HSPs. © 2022 IEEE.

关键词： Convolutional neural networks

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Research hotspots and trends of Rural revitalization assisted by Farmers' cooperatives -CiteSpace analysis based on 2097 articles 21

Research hotspots and trends of Rural revitalization assiste...

引用

21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Wang, Chenyu Lu, Qian School of Business Guilin University of Electronic Technology Guilin China Business School Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665454629

In order to clarify the current situation of rural revitalization assisted by farmers' cooperatives, dig out the research hotspot of rural revitalization assisted by farmers' cooperatives, and explore the trend of rural revitalization assisted by farmers' cooperatives, the paper carries out visual analysis of the literature in this field, in order to provide reference for further research. CiteSpacevisualization software was used to analyze the literatures collected by CNKI from 2000 to 2022. The results showed that the authors showed the characteristics of 'large dispersion and smallconcentration'. The research strength of 'cooperatives and rural revitalization' is not evenly distributed, and the overall trend is biased towards the central and southern regions of China. The backbone of research is mainly research institutes and universities, and the degree of cooperation between institutions needs to be improved. Through the keywords co-occurrence network visualization map, four research hotspots are obtained, namely, rural revitalization, farmer cooperatives, rural finance, agricultural and rural modernization. Industrial integration and industrial poverty alleviation have become new emerging directions. © 2022 IEEE.

关键词： Agriculture

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VSNT: Adaptive Network Traffic Forecasting via VMD and Deep Learning with SCI-Block and Attention Mechanism 21

VSNT: Adaptive Network Traffic Forecasting via VMD and Deep ...

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作者： Dong, Pingping Zuo, Yuning Chen, Liying Li, Shangyu Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

As the Internet evolves towards a dense network paradigm, advancements in mobile services have resulted in a consistent have consistently amplified the volume of mobile data. Consequently, precise network traffic forecasting has become critical for effective network planning and resource allocation. The Informer model, based on the Transformer architecture, has been optimized for time-series forecasting recently. However, the Informer tends to accentuate noise within individual time series, which leads to overfitting or decreased robustness, ultimately affecting forecasting performance. To address these challenges, an adaptive network traffic forecasting model named VSNT is introduced. VSNT employs Variational Mode Decomposition (VMD) to adaptively decompose original network traffic into Intrinsic Mode Function (IMF) components. For the preservation of the interrelation among IMF components, comprehensive representation learning is undertaken prior to their input into the neural network. This network utilizes an encoder-decoder structure and integrates tools such as the SCI-Block and attention mechanisms. The SCI-Block is instrumental in extracting multiscale network attributes, while the attention mechanism emphasizes pronounced features. Compared to the intricate Informer model, VSNT reduces the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) by approximately 51% and 35% respectively. This positions VSNT as a robust solution for accurate network traffic forecasting, providing a fruitful path for research in network traffic forecasting and time-series analysis. © 2023 IEEE.

关键词： Variational mode decomposition

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Performance Analysis of Data Processing in distributed File Systems with Near Data Processing

Performance Analysis of Data Processing in Distributed File ...

引用

2024 international symposium on Networks, Computers and Communications, ISNCC 2024

作者： Hou, Shiyue Tallent, Nathan R. Wang, Li Mi, Ningfang Northeastern University Department of Electrical and Computer Engineering MA United States Pacific Northwest National Laboratory Advanced Computations and Maths Division RichlandWA United States

ISBN: (纸本)9798350364910

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark [1] and Hadoop [2] manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape. © 2024 IEEE.

关键词： Data assimilation

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Ensuring Minority Group Rights in Social IoT with Fairness-aware Federated Graph Node Classification 21

Ensuring Minority Group Rights in Social IoT with Fairness-a...

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作者： Mao, Qinghua Lin, Xi Li, Gaolei Chen, Lixing Liu, Yuchen Qi, Yuxin Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China North Carolina State University Department of Computer Science NC United States

ISBN: (纸本)9798350329223

Graph neural networks (GNNs) are a powerful tool to predict the categories of IoT nodes for providing diverse high-quality services in the social Internet of Things (SIoT). Intuitively, SIoT could be divided into several communities based on similar interests and mutual trust, whereas interaction between cross-community nodes may intrigue data privacy. With the popularity of federated learning (FL), a global GNN model can be collaboratively trained by exploiting knowledge from multiple parties in a privacy-preserving way. However, since node attributes across different SIoT communities are usually multi-source and heterogeneous, the performance of an optimum global model may exhibit potential bias against minority social groups, i.e, unfairness, thereby undermining the efficiency of node classification and social behavior analysis. To address this problem, we propose PFedNC, a proportional fairness-aware federated learning framework for node classification in SIoT system. Specifically, we incorporate proportional fairness and related Nash bargaining solution into FL optimization objective, which imposes more fairness while maintaining a similar average performance. Moreover, we design a class-balanced re-weighting loss function to mitigate imbalanced class distribution caused by non-IID node distribution. Extensive experiments on two node-level SIoT simulation datasets demonstrate that our PFedNC framework effectively encourages performance fairness among social groups and achieves a favorable tradeoff between fairness and efficiency. © 2023 IEEE.

关键词： Internet of things

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Twin Branch Transformers-Based Re-Ranking for Image Retrieval 21

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

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作者： Wang, Ziqi Wang, Wei Institute of Information Engineering Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen China

ISBN: (纸本)9798350329223

Image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor;re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric);the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity. © 2023 IEEE.

关键词： Image retrieval

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