3,308 results on '"data aggregation"'
Search Results
2. Proposed Methodology for Obtaining Ballast Layer Performance Indicators
- Author
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Vivanco, Jorge Rojas, Breul, Pierre, Talon, Aurélie, Benz-Navarrete, Miguel, Barbier, Sébastien, Ranvier, Fabien, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Rujikiatkamjorn, Cholachat, editor, Xue, Jianfeng, editor, and Indraratna, Buddhima, editor
- Published
- 2025
- Full Text
- View/download PDF
3. Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network.
- Author
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Ilango, Poonguzhali, Ravichandran, Anitha, Sivarajan, Nagarajan, and Aiyappan, Asha
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- *
LEAST squares , *DATA transmission systems , *ENERGY consumption , *RESEARCH personnel , *HIGH technology , *WIRELESS sensor networks - Abstract
Summary: The advanced technology in recent years that has achieved more attention among researchers and the social community is the wireless sensor network (WSN) that includes a number of nodes that are commonly distributed in remote zones. While deploying the WSN in huge areas, WSNs produce a massive amount of data. Thus, there is a significant need to process the data through efficient models. The data aggregation technique is the common solution widely employed to obstruct congestion on large‐scale WSNs. However, the demanding part of the data aggregation scheme is to mitigate the network overhead without affecting the system efficiency. Most of the data transmitted by sensor nodes are repetitious and thus result in high power consumption. Therefore, sensor nodes should utilize an efficient data aggregation model for data transmission that minimizes duplicate data. In order to maintain such complications, this article proposes a hierarchical Taylor quantized kernel least mean square (HTQKLMS) filter for aggregating data in WSN. For this purpose, WSN is initially simulated, and then data aggregation is accomplished using developed HTQKLMS filter. Additionally, the HTQKLMS is derived by amalgamating the hierarchical fractional quantized kernel least mean square (HFQKLMS) filter with the Taylor series. Here, the data prediction mechanism is done by employing HFQKLMS model that is an integration of quantized kernel least mean square (QKLMS) and hierarchical fractional bidirectional least mean square (HFBLMS). Apart from this, data redundancy is achieved by broadcasting needed data utilizing data detected at the destination. Furthermore, HTQKLMS approach has delivered a minimum energy consumption of 0.0333 J and less prediction error of 0.0326. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. On aggregation invariance of multinomial processing tree models.
- Author
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Erdfelder, Edgar, Quevedo Pütter, Julian, and Schnuerch, Martin
- Subjects
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TREE size , *SAMPLE size (Statistics) , *DATA analysis , *MEASURING instruments , *PARAMETERIZATION - Abstract
Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense. After introducing the notions of structural and empirical aggregation invariance of MPT models, we show that any MPT model that holds at the level of single individuals must also hold at the aggregate level when it is both structurally and empirically aggregation invariant. Moreover, group-level parameters of aggregation-invariant MPT models are equivalent to the expected values (i.e., means) of the corresponding individual parameters. To investigate the robustness of MPT results for aggregate data when one or both invariance conditions are violated, we additionally performed a series of simulation studies, systematically manipulating (1) the sample sizes in different trees of the model, (2) model parameterization, (3) means and variances of crucial model parameters, and (4) their correlations with other parameters of the respective MPT model. Overall, our results show that MPT parameter estimates based on aggregate data are trustworthy under rather general conditions, provided that a few preconditions are met. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. THE EFFECTIVENESS OF JAYA OPTIMIZATION FOR ENERGY AWARE CLUSTER BASED ROUTING IN WIRELESS SENSOR NETWORKS.
- Author
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MALISETTI, NAGESWARARAO and PAMULA, VINAY KUMAR
- Subjects
INTERNET of things ,ENERGY consumption ,MATHEMATICAL optimization ,WIRELESS sensor networks ,QUALITY of life ,LONGEVITY - Abstract
The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Exploring secure and private data aggregation techniques for the internet of things: a comprehensive review.
- Author
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Aga, Dagmawit Tadesse, Chintanippu, Rakesh, Mowri, Rawshan Ara, and Siddula, Madhuri
- Abstract
The Internet of Things (IoT) is a notion in which smart devices seamlessly integrate with physical and virtual resources. These resources are accessible online and available to anyone to provide value-added information. The rapid advancement of technology has resulted in the creation of various features and capabilities for end-users and applications. Smart devices generate a large amount of data on the Internet of Things (IoT) and aggregate it on various server platforms, which is crucial for data processing, aggregating, and controlling. The accelerated development of technology has led to the creation of various features and capabilities for end-users and applications. Despite its benefits, IoT technology is plagued by several security and privacy concerns that must be addressed to ensure widespread acceptance. The accelerated technological progress has created a multitude of features and capabilities, yet it has also posed substantial challenges. To address this problem, researchers and practitioners have adopted numerous schemes to aggregate data while preserving data privacy. Data privacy is critical to protect against network entities inside the network, data operators, and external eavesdroppers. This survey paper delves into the security and privacy challenges associated with IoT and explores recent solutions, such as APPA, blockchain-enabled IoT, LPDA, EF-IDASC, and two secure privacy-preserving data aggregation schemes - PPLS. Additionally, it outlines several open research challenges and their anticipated solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach.
- Author
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Jaroszewicz, Joanna and Horynek, Hubert
- Abstract
A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R
2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
8. Privacy‐preserving data aggregation achieving completeness of data queries in smart grid.
- Author
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Li, Xinyang, Zhao, Meng, Ding, Yong, Yang, Changsong, Wang, Huiyong, Liang, Hai, and Wang, Yujue
- Subjects
DATA privacy ,DATA encryption ,ELECTRIC power consumption ,PRIVACY ,GRIDS (Cartography) - Abstract
In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine‐grained data. To address these challenges, we propose a privacy‐preserving data aggregation scheme supporting data query (PAQ). We designed a multi‐level data aggregation mechanism based on Paillier semi‐homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
9. An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network.
- Author
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Tharmalingam, Rajkumar, Nachimuthu, Nandhagopal, and Prakash, G.
- Subjects
DATA compression ,ENERGY conservation ,POWER resources ,DATA transmission systems ,ENERGY consumption ,WIRELESS sensor networks ,DEEP learning - Abstract
Heterogeneous wireless sensor networks (HWSNs) are energy-constrained networks. Data aggregation can conserve the energy of HWSN. Clustering protocols and data processing can be used at individual nodes to reduce the amount of transfers and extend the network's lifespan. Considering these advantages, the proposed research introduces an efficient energy supply and data aggregation using effective techniques. Initially, cluster head (CH) election and data transmission are done using an information entropy based-clustering algorithm (IECA). After successful data transmission, an efficient energy supply scheme is enabled between cluster members (CMs) and sink nodes. Then, data aggregation is performed in CH using Planar Flow-Based Variational Auto-Encoder-based data aggregation (PF-VAE-DA). Before performing data aggregation, the useless and redundant data is compressed using a Long-short-term-memory-based auto-encoder (LSTM-based auto-encoder). The compressed data is aggregated in CHs. Before transferring the aggregated data to the sink, efficient data stream collection is performed to equalize the data size utilizing self-adaptive adjustment of sliding window size (SASWS). Finally, the optimal path is selected to transmit the aggregated data from CH to the sink. The performance of the proposed method is evaluated for various performance metrics. The aim of the proposed study is to enhance the accuracy of sensing data by introducing a novel deep learning-based data aggregation approach. This will extract significant features from vast amounts of data and carry out data aggregation. In addition, to improve the dependability of aggregated data transfer, an effective Energy Supply Policy based on data transmission patterns is implemented. The results show that the proposed method outperforms other methods in terms of network energy consumption, packet delivery ratio (PDR), packet dropping ratio, data aggregation rate, transmission delay, and network lifetime. The proposed approach uses 50% less energy than the other methods. The model's transmission delay ranges from 0.1 to 0.4 s as the number of nodes increases. The proposed network contains 282 active nodes at the 400th round, which is much more than the existing networks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. Stroke recurrence prediction using machine learning and segmented neural network risk factor aggregation.
- Author
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Ding, Xueting, Meng, Yang, Xiang, Liner, and Boden-Albala, Bernadette
- Subjects
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RISK assessment , *RANDOM forest algorithms , *PREDICTION models , *RECEIVER operating characteristic curves , *PILOT projects , *LOGISTIC regression analysis , *DESCRIPTIVE statistics , *ARTIFICIAL neural networks , *CONTENT mining , *MEDICAL coding , *STROKE , *DISEASE relapse , *MACHINE learning , *SENSITIVITY & specificity (Statistics) , *NOSOLOGY , *DISEASE risk factors - Abstract
Stroke has remained a major cause of mortality and disability in the United States for years, and its recurrence significantly increased the risks. For predicting stroke recurrence, traditional data aggregation methods have limitations in effectively handling the numerous subcategories of stroke risk factors. This pilot study proposed a Segmented Neural Network-Driven Aggregation (SNA) method, and it aimed to improve the prediction model's accuracy. Utilizing the TriNetX diagnosis dataset, we processed various risk factors and demographic information through traditional and our proposed data aggregation techniques. We applied logistic regression and random forest classifiers to predict stroke recurrence. Our findings revealed that using the SNA method significantly outperformed other aggregation methods for both classifiers. Using the SNA method with a random forest classifier achieved higher accuracy (84.2%) and a better balance between sensitivity and specificity (AUC of ROC = 0.928, AUC of PR = 0.940) compared to other combinations. These results showed the potential of machine-learning supervised encoding methods in stroke recurrence predictions, providing implications for clinical practice and future epidemiological research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. A Generalized approach to the operationalization of Software Quality Models.
- Author
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Izurieta, Clemente, Reimanis, Derek, O'Donoghue, Eric, Liyanage, Kaveen, Manzi Muneza, A. Redempta, Whitaker, Bradley, and Reinhold, Ann Marie
- Subjects
COMPUTER software quality control ,SOFTWARE engineering ,QUALITY assurance ,ENGINEERING models ,COMPUTER software development - Abstract
Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
12. Dynamic and efficient device collaborations in 5G‐advanced and 6G networks
- Author
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Xianghui Han, Shuai Zhou, Shuaihua Kou, Jian Li, Ruiqi Liu, and Shi Jin
- Subjects
5G mobile communication ,6G ,data aggregation ,mobile communication ,Telecommunication ,TK5101-6720 - Abstract
Abstract Collaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup.
- Published
- 2024
- Full Text
- View/download PDF
13. Exploring secure and private data aggregation techniques for the internet of things: a comprehensive review
- Author
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Dagmawit Tadesse Aga, Rakesh Chintanippu, Rawshan Ara Mowri, and Madhuri Siddula
- Subjects
Data aggregation ,Data privacy ,Internet of things ,Internet of medical things ,Security ,Smart city ,Computer engineering. Computer hardware ,TK7885-7895 ,Computer software ,QA76.75-76.765 - Abstract
Abstract The Internet of Things (IoT) is a notion in which smart devices seamlessly integrate with physical and virtual resources. These resources are accessible online and available to anyone to provide value-added information. The rapid advancement of technology has resulted in the creation of various features and capabilities for end-users and applications. Smart devices generate a large amount of data on the Internet of Things (IoT) and aggregate it on various server platforms, which is crucial for data processing, aggregating, and controlling. The accelerated development of technology has led to the creation of various features and capabilities for end-users and applications. Despite its benefits, IoT technology is plagued by several security and privacy concerns that must be addressed to ensure widespread acceptance. The accelerated technological progress has created a multitude of features and capabilities, yet it has also posed substantial challenges. To address this problem, researchers and practitioners have adopted numerous schemes to aggregate data while preserving data privacy. Data privacy is critical to protect against network entities inside the network, data operators, and external eavesdroppers. This survey paper delves into the security and privacy challenges associated with IoT and explores recent solutions, such as APPA, blockchain-enabled IoT, LPDA, EF-IDASC, and two secure privacy-preserving data aggregation schemes - PPLS. Additionally, it outlines several open research challenges and their anticipated solutions.
- Published
- 2024
- Full Text
- View/download PDF
14. DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS
- Author
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Aigul Adamova and Tamara Zhukabayeva
- Subjects
internet of things ,security ,data normalisation ,data aggregation ,z-score ,leach ,Information technology ,T58.5-58.64 - Abstract
The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.
- Published
- 2024
- Full Text
- View/download PDF
15. Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning
- Author
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Abhishek Bajpai, Harshita Verma, and Anita Yadav
- Subjects
Wireless sensor network ,Principal component analysis (PCA) ,Reinforcement learning ,Data aggregation ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.
- Published
- 2024
- Full Text
- View/download PDF
16. Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.
- Author
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Sengodan, Boopathi Chettiagounder, Stanislaus, Prince Mary, Arumugam, Sivakumar Sabapathy, Sah, Dipak Kumar, Dhabliya, Dharmesh, Chenniappan, Poongodi, Hezekiah, James Deva Koresh, and Maheswar, Rajagopal
- Subjects
- *
DATA compression , *DATA transmission systems , *DISTRIBUTED sensors , *COMPRESSED sensing , *ENERGY consumption , *WIRELESS sensor networks , *DEEP learning , *AUTOENCODER - Abstract
Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
17. 边缘环境下基于移动群智感知计算卸载的数据汇聚.
- Author
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杨桂松 and 桑健
- Subjects
- *
ENERGY consumption , *CROWDSENSING , *RESOURCE allocation , *PROBLEM solving , *ALGORITHMS - Abstract
The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. A secure paillier cryptosystem based privacy-preserving data aggregation and query processing models for smart grid.
- Author
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Kumar, Jatinder and Singh, Ashutosh Kumar
- Subjects
- *
SMART meters , *ELECTRIC power consumption , *DATA privacy , *ELECTRIC equipment , *DATA warehousing , *GRIDS (Cartography) - Abstract
A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. ADAPTIVE REINFORCEMENT LEARNING-BASED DATA AGGREGATION AND ROUTING OPTIMIZATION (ARL-DARO) FOR ENHANCING PERFORMANCE IN WIRELESS SENSOR NETWORKS.
- Author
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Shobana, V. and Samraj, Jasmine
- Subjects
GREY Wolf Optimizer algorithm ,WIRELESS sensor networks ,REINFORCEMENT learning ,TRUST ,ENERGY consumption - Abstract
Wireless Sensor Networks (WSNs) are challenged by the need for optimized Energy Consumption (EC), efficient Data Aggregation (DA), and reliable routing due to their dynamic topologies and limited resources. Existing solutions like TEAMR and DDQNDA address these concerns but face significant drawbacks--TEAMR lacks adaptability to rapidly changing topologies, while DDQNDA suffers from high computational overhead and delayed convergence, hindering its effectiveness in real-time scenarios. To overcome these limitations, this paper introduces the Adaptive Reinforcement Learning (RL)-Based DA and Routing Optimization (ARL-DARO) algorithm. The proposed methodology follows a systematic approach, beginning with cluster formation and Cluster Head (CH) selection (CHS) using the Grey Wolf Optimizer (GWO), which ensures Energy-Efficient (EE) clustering and optimal CH selection. In the next step, trust factors such as Node Connectivity (NC), Residual Trust (RT), and Cooperation Rate (CR) are integrated into Quality of Service (QoS) metrics as part of the Fitness Function(FF) to enhance route reliability and security. Finally, the ARL-DARO algorithm is employed to dynamically optimize both data aggregation and routing. It leverages Q-learning to select optimal routes based on energy efficiency, security, and link reliability, further reducing data redundancy and improving adaptability to realtime network changes. Performance is assessed using parameters such EC, packet delivery ratio (PDR), end-to-end latency (E2E delay), throughput, and network lifetime (NL) across networks with 100, 200, 300, 400, and 500 nodes. Results show that ARL-DARO significantly reduces energy consumption by up to 45%, increases throughput by 30%, and extends network lifetime, proving its effectiveness over existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS.
- Author
-
Adamova, Aigul and Zhukabayeva, Tamara
- Subjects
INTERNET of things ,TECHNOLOGICAL innovations ,DATA acquisition systems ,ARTIFICIAL intelligence ,DATA security - Abstract
The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. IESDCC-KM: an improved energy-saving distributed cluster–chain K-communication scheme for smart sensor networks.
- Author
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Pius Agbulu, G., Joselin Retna Kumar, G., and Gunasekar, S.
- Abstract
Owing to the inexpensive, adaptable, and scalable features of WSNs (wireless sensor networks), the network is viewed as a vital technology to support distinct smart applications. The primary issue is to strengthen the lifespan of the network, as the node devices have bounded lifecycles owing to notable power constraints. Thus, to sustain the lifespan of WSNs, it is crucial to reinforce the energy administration at the node devices, as they are routinely deployed in secluded areas. Several energy management strategies have recently been used in this context. Among these alternatives, cluster-chain hybrid networks have demonstrated the ability to significantly reduce the node energy usage. However, the chain formation techniques often result in increased latency in large-scale scenarios. Similarly, it has been proven that coding techniques preserves the network reliability and energy efficiency. While utilising the benefits of these coding schemes it is necessary to carefully secure the topology, link quality, and coding vectors. In this paper, an improved energy-saving distributed cluster–chain K-communication mechanism for smart sensor networks is proposed. In the proposed solution named IESDCC-KM, a dual K-means technique is adapted to form unequal clusters. IESDCC-KM implements a competing and ideal weight function to select the cluster heads and establishes perpendicular chain trees among heads based on their distances and a threshold value. It establishes gradient-based dis-joint multiple routes from the source to the destination and implements discrete wavelet transform to compress the accumulated inter-cluster data. At the intermediate nodes on the path along the source and destination, the packets from the different link nodes are encoded utilizing linear network coding. MATLAB 2018b experimental analysis demonstrates the proposed IESDCC-KM improves the reception ratio by 0.20 to 0.03 at 0.99 to 0.96 precision rate. Furthermore, it showed a 28.57% throughput increase with a 1% reduction in delay and a 2.86% boost in energy-saving for 4000 rounds. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning.
- Author
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Bajpai, Abhishek, Verma, Harshita, and Yadav, Anita
- Subjects
INTERNET of things ,MACHINE learning ,TECHNOLOGICAL innovations ,ARTIFICIAL intelligence ,DATA science - Abstract
The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Data Aggregation Scheme Using Differential Evolution with Sailfish Optimization for Clustering and Routing in IoT.
- Author
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Puli, Srilakshmi, Nulaka, Srinivasu, Patnala, Lavanya, Mishra, Sangita, and Meena, Simhadri Venkata
- Subjects
ENERGY consumption ,INTERNET of things ,SMART homes ,HOME businesses ,WIRELESS sensor networks ,INTELLIGENT sensors ,FIREFLIES - Abstract
Internet of Things (IoT) facilitates connectivity in businesses and smart homes by integrating embedded technology, wireless sensor networks and data aggregation. Regular monitoring of energy usage in IoT networks is crucial due to the high energy consumption and delays in transmitting data to the Base Station (BS) by the sensor nodes. The most significant challenges in IoT include energy depletion and transmission delays. In this research, the proposed Differential Evolution with Sailfish Optimization (DESFO) model addresses large network handling, achieves maximum convergence rates, and reduces energy consumption. The Differential Evolution (DE) mutation and crossover operators enhance exploration capabilities, while SFO adaptive movement strategies improve the exploitation of the search space. Together, they achieve high convergence rates, prevent falling into local optima, provide iterative control and manage high-dimensional networks effectively. The DESFO method exhibits superior performance when compared to the existing methods, Firefly Optimization and Aquila Optimization (FF-AO), Fixed-Parameter Tractable Approximation Clustering (FPTAC), and Cluster based Reliable Data Aggregation-Sunflower Optimization (CRDA-SFO). The proposed DESFO method yields impressive results, achieving a Packet Delivery Ratio (PDR) of 96.12% at 250 nodes, a Delay of 3ms at 250node, Energy consumption of 12J at 250 respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. Dynamic Edge-Based High-Dimensional Data Aggregation with Differential Privacy.
- Author
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Chen, Qian, Ni, Zhiwei, Zhu, Xuhui, Lyu, Moli, Liu, Wentao, and Xia, Pingfan
- Subjects
DATA privacy ,UPLOADING of data ,EDGE computing ,INFORMATION sharing ,PRIVACY - Abstract
Edge computing enables efficient data aggregation for services like data sharing and analysis in distributed IoT applications. However, uploading dynamic high-dimensional data to an edge server for efficient aggregation is challenging. Additionally, there is the significant risk of privacy leakage associated with direct such data uploading. Therefore, we propose an edge-based differential privacy data aggregation method leveraging progressive UMAP with a dynamic time window based on LSTM (EDP-PUDL). Firstly, a model of the dynamic time window based on a long short-term memory (LSTM) network was developed to divide dynamic data. Then, progressive uniform manifold approximation and projection (UMAP) with differential privacy was performed to reduce the dimension of the window data while preserving privacy. The privacy budget was determined by the data volume and the attribute's Shapley value, adding DP noise. Finally, the privacy analysis and experimental comparisons demonstrated that EDP-PUDL ensures user privacy while achieving superior aggregation efficiency and availability compared to other algorithms used for dynamic high-dimensional data aggregation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Adaptive Clustering and Scheduling for UAV-Enabled Data Aggregation.
- Author
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Nguyen, Tien-Dung, Pham Van, Tien, Le, Duc-Tai, and Choo, Hyunseung
- Subjects
SCHEDULING ,INTERNET of things ,ENERGY consumption ,BENCHMARKING (Management) - Abstract
Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a network into clusters and perform aggregation within each cluster. Existing works solved the UAV trajectory planning problem, in which the energy consumption and/or flight time of the UAV is the minimization objective. The aggregation scheduling within each cluster was neglected, and they assumed that data must be ready when the UAV arrives at the cluster heads (CHs). This paper addresses the minimum time aggregation scheduling problem in duty-cycled networks with a single UAV. We propose an adaptive clustering method that takes into account the trajectory and speed of the UAV. The transmission schedule of IoT devices and the UAV departure times are jointly computed so that (1) the UAV flies continuously throughout the shortest path among the CHs to minimize the hovering time and energy consumption, and (2) data are aggregated at each CH right before the UAV arrival, to maximize the data freshness. Intensive simulation shows that the proposed scheme reduces up to 35% of the aggregation delay compared to other benchmarking methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Security enhanced privacy-preserving data aggregation scheme for intelligent transportation system.
- Author
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Zuo, Kaizhong, Chu, Xixi, Hu, Peng, Ni, Tianjiao, Jin, Tingting, Chen, Fulong, and Shen, Zhangyi
- Subjects
- *
INTELLIGENT transportation systems , *CHINESE remainder theorem , *INFORMATION technology security , *DATA security , *DATA privacy , *TRAFFIC safety - Abstract
The intelligent transportation system can make traffic decisions through the sensory data collected by vehicles to ensure driving safety, improve traffic efficiency and traffic environment. Thus, the system has been widely concerned by industry and academia. However, the intelligent transportation system confronts several challenges in location privacy protection and data security during data aggregation. To solve these challenges, we propose a security-enhanced privacy-preserving data aggregation scheme for the intelligent transportation system, named SEPDA. Specifically, the SEPDA scheme utilizes the Chinese Remainder Theorem, Modified Paillier Cryptosystem and T-N Threshold Sharing to protect the location privacy and information security of vehicles, and obtains the mean and variance in the data report reading and analytics process. The SEPDA also uses the threshold cryptosystem to enhance the security of the traffic management center, which can avoid single-point attacks. Meanwhile, SEPDA employs batch authentication technology to reduce authentication overhead. Detailed security analysis and performance evaluation show that the SEPDA can resist various security threats and has low computational complexity, communication overhead and communication delay. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. A Secure Data Aggregation Algorithm Based on a Trust Mechanism.
- Author
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Liu, Changtao and Ye, Jun
- Subjects
- *
TRUST , *MULTICASTING (Computer networks) , *DATA transmission systems , *ALGORITHMS , *DATA security , *TELECOMMUNICATION systems - Abstract
Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. A Generalized approach to the operationalization of Software Quality Models
- Author
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Clemente Izurieta, Derek Reimanis, Eric O’Donoghue, Kaveen Liyanage, A. Redempta Manzi Muneza, Bradley Whitaker, and Ann Marie Reinhold
- Subjects
Software quality ,Software engineering ,Quality assurance ,Quality models ,Data science ,Data aggregation ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.
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- 2024
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29. Energy-Efficient Data Aggregation Techniques in Wireless Sensor Networks
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Agnihotri, Atul Kumar, Awasthi, Vishal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Swaroop, Abhishek, editor, Kansal, Vineet, editor, Fortino, Giancarlo, editor, and Hassanien, Aboul Ella, editor
- Published
- 2024
- Full Text
- View/download PDF
30. A Verifiable Federated Learning Algorithm Supporting Distributed Pseudonym Tracking
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Xie, Haoran, Wang, Yujue, Ding, Yong, Yang, Changsong, Wang, Huiyong, Liang, Hai, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Onizuka, Makoto, editor, Lee, Jae-Gil, editor, Tong, Yongxin, editor, Xiao, Chuan, editor, Ishikawa, Yoshiharu, editor, Amer-Yahia, Sihem, editor, Jagadish, H. V., editor, and Lu, Kejing, editor
- Published
- 2024
- Full Text
- View/download PDF
31. On Resilience of Distributed Flooding Algorithm to Stochastic Link Failures
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Kenyeres, Martin, Kenyeres, Jozef, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor
- Published
- 2024
- Full Text
- View/download PDF
32. Smart Homes and Buildings
- Author
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Fotopoulou, Eleni, Zafeiropoulos, Anastasios, Gouvas, Panagiotis, Finlay, James, Managing Editor, Ziegler, Sébastien, editor, Radócz, Renáta, editor, Quesada Rodriguez, Adrian, editor, and Matheu Garcia, Sara Nieves, editor
- Published
- 2024
- Full Text
- View/download PDF
33. Comparative Analysis of BPSA and MESA2DA Sleep Awake Clustering Protocols
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Bhardwaj, Akanksha, Gupta, Bhupesh, Chhabra, Mohit, Sharma, Avinash, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rathore, Vijay Singh, editor, Manuel R. S. Tavares, João, editor, Tuba, Eva, editor, and Devedzic, Vladan, editor
- Published
- 2024
- Full Text
- View/download PDF
34. Computational Approach for Data Aggregation in Wireless Sensor Networks (WSNs)
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Kaur, Navjyot, Vetrithangam, D., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, Jaiswal, Ajay, editor, and Kumar, Prabhat, editor
- Published
- 2024
- Full Text
- View/download PDF
35. The Impact of Sentiment in Social Network Communication
- Author
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Kuntur, Soveatin, Cena, Anna, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ansari, Jonathan, editor, Fuchs, Sebastian, editor, Trutschnig, Wolfgang, editor, Lubiano, María Asunción, editor, Gil, María Ángeles, editor, Grzegorzewski, Przemyslaw, editor, and Hryniewicz, Olgierd, editor
- Published
- 2024
- Full Text
- View/download PDF
36. PRIDA: PRIvacy-Preserving Data Aggregation with Multiple Data Customers
- Author
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Bozdemir, Beyza, Özdemir, Betül Aşkın, Önen, Melek, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Pitropakis, Nikolaos, editor, Katsikas, Sokratis, editor, and Markantonakis, Konstantinos, editor
- Published
- 2024
- Full Text
- View/download PDF
37. DAML: Practical Secure Protocol for Data Aggregation Based on Machine Learning
- Author
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Zhang, Guanglin, Zhao, Ping, Zhang, Anqi, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, Zhang, Guanglin, Zhao, Ping, and Zhang, Anqi
- Published
- 2024
- Full Text
- View/download PDF
38. LocMIA: Membership Inference Attacks Against Aggregated Location Data
- Author
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Zhang, Guanglin, Zhao, Ping, Zhang, Anqi, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, Zhang, Guanglin, Zhao, Ping, and Zhang, Anqi
- Published
- 2024
- Full Text
- View/download PDF
39. A Survey of Network Protocols for Performance Enhancement in Wireless Sensor Networks
- Author
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Gupta, Abhishek, Sharma, Devendra Kumar, Sahai, D. N., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Devendra Kumar, editor, Peng, Sheng-Lung, editor, Sharma, Rohit, editor, and Jeon, Gwanggil, editor
- Published
- 2024
- Full Text
- View/download PDF
40. Comments on a Double-Blockchain Assisted Data Aggregation Scheme for Fog-Enabled Smart Grid
- Author
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Lin, Pei-Yu, Chang, Ya-Fen, Chang, Pei-Shih, Tai, Wei-Liang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor
- Published
- 2024
- Full Text
- View/download PDF
41. Trust Aware Distributed Protocol for Malicious Node Detection in IoT-WSN
- Author
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Bhaskar, S., Shreehari, H. S., Shobha, B. N., Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Jacob, I. Jeena, editor, Piramuthu, Selwyn, editor, and Falkowski-Gilski, Przemyslaw, editor
- Published
- 2024
- Full Text
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42. A Distributed Cross-Layer Protocol for Sleep Scheduling and Data Aggregation in Wireless Sensor Networks
- Author
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Xia, Zhenxiong, Li, Jingjing, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor
- Published
- 2024
- Full Text
- View/download PDF
43. Routing and Data Aggregation Techniques in Wireless Sensor Networks: Previous Research and Future Scope
- Author
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Kaur, Navjyot, Vetrithangam, D., Das, Swagatam, Series Editor, Bansal, Jagdish Chand, Series Editor, Tavares, João Manuel R. S., editor, Rodrigues, Joel J. P. C., editor, Misra, Debajyoti, editor, and Bhattacherjee, Debasmriti, editor
- Published
- 2024
- Full Text
- View/download PDF
44. Model-Based Controlling Approaches for Manufacturing Processes
- Author
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Rüppel, Adrian Karl, Ay, Muzaffer, Biernat, Benedikt, Kunze, Ike, Landwehr, Markus, Mann, Samuel, Pennekamp, Jan, Rabe, Pascal, Sanders, Mark P., Scheurenberg, Dominik, Schiller, Sven, Xi, Tiandong, Abel, Dirk, Bergs, Thomas, Brecher, Christian, Reisgen, Uwe, Schmitt, Robert H., Wehrle, Klaus, Padberg, Melanie, Series editor, Brecher, Christian, Series Editor, Padberg, Melanie, Series Editor, Schuh, Günther, editor, van der Aalst, Wil, editor, Jarke, Matthias, editor, and Piller, Frank T., editor
- Published
- 2024
- Full Text
- View/download PDF
45. Efficient and Secure Data Aggregation for UAV-to-Ground Station Communication in Smart City Environment
- Author
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Verma, Girraj Kumar, Mishra, Dheerendra, Kumar, Neeraj, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Roy, Bimal Kumar, editor, Chaturvedi, Atul, editor, Tsaban, Boaz, editor, and Hasan, Sartaj Ul, editor
- Published
- 2024
- Full Text
- View/download PDF
46. Data aggregation by enhanced squirrel search optimization algorithm for in wireless sensor networks
- Author
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Kathiroli, Panimalar and Kanmani, S.
- Published
- 2024
- Full Text
- View/download PDF
47. Two‐step attribute reduction for AIoT networks
- Author
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Chao Ren, Gaoxin Lyu, Xianmei Wang, Yao Huang, Wei Li, and Lei Sun
- Subjects
data aggregation ,data communication digital communication ,Internet of Things ,Telecommunication ,TK5101-6720 - Abstract
Abstract The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.
- Published
- 2024
- Full Text
- View/download PDF
48. EFTA: An Efficient and Fault-Tolerant Data Aggregation Scheme without TTP in Smart Grid.
- Author
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Mei, Xianyun, Wang, Liangliang, Qin, Baodong, Zhang, Kai, and Long, Yu
- Subjects
- *
DATA privacy , *FAULT-tolerant computing , *DATA encryption , *SMART meters , *GRID computing - Abstract
With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Transformer-Based User Charging Duration Prediction Using Privacy Protection and Data Aggregation.
- Author
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Zeng, Fei, Pan, Yi, Yuan, Xiaodong, Wang, Mingshen, and Guo, Yajuan
- Subjects
ELECTRIC charge ,DATA privacy ,USER charges ,FEDERATED learning ,ELECTRIC vehicles ,ELECTRIC vehicle charging stations ,DATA protection - Abstract
The current uneven deployment of charging stations for electric vehicles (EVs) requires a reliable prediction solution for smart grids. Existing traffic prediction assumes that users' charging durations are constant in a given period and may not be realistic. In fact, the actual charging duration is affected by various factors including battery status, user behavior, and environment factors, leading to significant differences in charging duration among different charging stations. Ignoring these facts would severely affect the prediction accuracy. In this paper, a Transformer-based prediction of user charging durations is proposed. Moreover, a data aggregation scheme with privacy protection is designed. Specifically, the Transformer charging duration prediction dynamically selects active and reliable temporal nodes through a truncated attention mechanism. This effectively eliminates abnormal fluctuations in prediction accuracy. The proposed data aggregation scheme employs a federated learning framework, which centrally trains the Transformer without any prior knowledge and achieves reliable data aggregation through a dynamic data flow convergence mechanism. Furthermore, by leveraging the statistical characteristics of model parameters, an effective model parameter updating method is investigated to reduce the communication bandwidth requirements of federated learning. Experimental results show that the proposed algorithm can achieve the novel prediction accuracy of charging durations as well as protect user data privacy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Two level data centric aggregation scheme for wireless sensor networks.
- Author
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Batool, Tahira, Ahmed, Atiq, and Gaiti, Dominique
- Abstract
Wireless sensor networks (WSNs) sense and collect information from a desired phenomenon with the help of sensor nodes that have limited computational power, battery, and memory. Several data aggregation approaches are proposed to make the sensor networks energy-efficient, increasing the network's lifetime by controlling data redundancy at aggregator nodes. Redundant data is suppressed before transmission to the sink. In this work, our aim is to enhance the network lifetime by efficiently utilizing the network's energy through controlled data redundancy and minimizing data transmission to the sink. Data aggregation occurs in two steps: firstly, within clusters where the cluster-head serves as the aggregation point, and secondly, at a central point in the network where the gateway node acts as the aggregation point. Experiments demonstrate that our proposed approach yields better results compared to a benchmark clustering protocol in terms of network stability, the number of data packets transferred to the destination, energy dissipation of nodes, and overall network lifetime. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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