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As smart grid technology rapidly advances, the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection. Current research emphasizes data security and user privacy concerns within smart grids. However, existing methods struggle with efficiency and security when processing large-scale data. Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge. This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities. The approach optimizes data preprocessing, integrates Long Short-Term Memory (LSTM) networks for handling time-series data, and employs homomorphic encryption to safeguard user privacy. It also explores the application of Boneh Lynn Shacham (BLS) signatures for user authentication. The proposed scheme's efficiency, security, and privacy protection capabilities are validated through rigorous security proofs and experimental analysis. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) generate correlated and redundant data. This redundancy increases energy consumption during transmission and aggregation, which reduces the network lifespan. Eliminating data redundancy using appropriate data aggregation mechanisms in the dynamic environment is challenging. To address these issues, we designed the Data Aggregation with Redundancy Removal (DARR) protocol and implemented it in two phases. In Phase I, the DARR protocol identifies redundant nodes by calculating the spatial distance between the adjacent nodes. Over time, nodes may run out of energy and stop working after continuously sensing, aggregating, and transmitting the data. The dead nodes can obstruct data forwarding to intermediate nodes, so it is important to check periodically whether the nodes are alive or dead. The periodic time check identifies the status of each node, allowing the protocol to focus only on active nodes. It sets redundant nodes to sleep, which conserves network energy. In Phase II, the protocol reduces data redundancy at the source nodes using temporal correlation between data measurements. We enhanced the DARR protocol by incorporating a High Compression Temporal (HCT) mechanism, which further reduces data redundancy. Simulations show that the DARR protocol reduces data transmissions by 24% and lowers network energy consumption by up to 31% by eliminating redundant data at both the network and node levels. [ABSTRACT FROM AUTHOR]

Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality‐of‐service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well. [ABSTRACT FROM AUTHOR]

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]

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. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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 R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model. [ABSTRACT FROM AUTHOR]

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]

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]

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.

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.

As a cornerstone technology of smart manufacturing, the industrial Internet of things (IIoT) harnesses efficient data sharing to propel the networking, digitization, and intellectualization of industrial production, thereby assisting manufacturing enterprises in cost reduction, efficiency enhancement, and bolstering their core competitiveness. Nevertheless, the limited resources inherent in industrial production field devices have posed significant hurdles to IIoT’s development, primarily due to the high computational costs of maintaining system security and vulnerabilities that render it susceptible to various attacks. To address these challenges and enhance the robustness, security, and efficiency of industrial systems, an efficient unpaired verification and aggregation (EUVA) protocol was proposed. Within the context of an IIoT environment based on elliptic curve cryptography, homomorphic encryption was emploied to safeguard data privacy and a verification key management scheme was introduced, facilitating secure and efficient unpaired verification. Furthermore, security analysis demonstrates that the proposed protocol meets the outlined security objectives. Finally, performance analysis conducted using MIRACL reveals that the EUVA protocol outperforms previous similar mechanisms in terms of computational communication costs and energy consumption.

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.

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.

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node's energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods. [ABSTRACT FROM AUTHOR]

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Due to some limitations in the data collection process caused either by human-related errors or by collection electronics, sensors, and network connectivity-related errors, the important values at some points could be lost. However, a complete dataset is required for the desired performance of the subsequent applications in various fields like engineering, data science, statistics, etc. An efficient data imputation technique is desired to fill in the missing data values to achieve completeness within the dataset. The fuzzy integral is considered one of the most powerful techniques for multi-source information fusion. It has a wide range of applications in many real-world decision-making problems that often require decisions to be made with partially observable/available information. To address this problem, algorithms impute missing data with a representative sample or by predicting the most likely value given the observed data. In this article, we take a completely different approach to the information fusion task in the ordered weighted averaging (OWA) context. In particular, we empirically explore for different distributions how the weights/importance of the missing sources are distributed across the observed inputs/sources. The experimental results on the synthetic and real-world datasets demonstrate the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

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]

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]

The Internet of Things (IoT) has ushered in an era of interconnected devices and sensors that generate vast amounts of data. While the potential of IoT is vast, resource- constrained IoT environments present unique challenges, particularly in the context of data aggregation. This research focuses on developing secure data aggregation scheme tailored to resource-constrained IoT environments. In these settings, limitations on processing power, memory, and bandwidth necessitate innovative solutions to ensure both the efficiency and security of data collection and transmission. This research proposes a comprehensive framework that optimizes data aggregation algorithms. The key objectives of this research are to enhance data aggregation efficiency by minimizing redundant data transfer, optimizing data compression, and reducing the burden on constrained resources. The findings of this research provide valuable insights for IoT applications operating under resource limitations. By improving the efficiency and security of data aggregation in resource-constrained IoT environments, this research contributes to the realization of the full potential of IoT technologies in scenarios where resources are limited. [ABSTRACT FROM AUTHOR]

Data aggregation using the shortest path is a challenging issue in wireless sensor networks. Data aggregation slows the communication process and consumes many resources available to sensor nodes. So, while transmitting the aggregated data, it is crucial to identify the shortest and most reliable route for energy-efficient data aggregation from the source to the sink node. However, the nodes closer to the sink node are heavily utilized in multi-hop transmission and succumb to early death, which causes energy imbalance and an energy hole problem in the network. To overcome this problem, we propose an innovative node utilization index-based data routing and aggregation (NUIDRA) protocol. The NUIDRA protocol is designed and implemented in two phases. The first phase is to determine the shortest path from the source node to the sink node based on the amount of bandwidth utilized by each sensor node, minimum hop count, node's residual energy, and data aggregation factor. In the second phase, the selected shortest path is used for data transmission and aggregation using the dynamic selection of the aggregator node. The choice of aggregator node is based on the node's utilization index (UI) and adjacent node count from which it receives the data. The NUIDRA protocol is compared and analysed with I-LEACH and QADA protocols. The extensive simulation results show that in the proposed NUIDRA protocol, there is an increase in the average throughput of 70%, packet delivery ratio by 41.93%, and the average latency is reduced by 58.15% as compared to the I-LEACH protocol. Further, there is an increase in the average throughput of 24%, packet delivery ratio by 7.31%, and the average latency is reduced by 53.23% as compared to the QADA protocol for a data packet size of 512 bytes. [ABSTRACT FROM AUTHOR]

This study measures territorial competitiveness at the municipal level in Italy, by proposing a robust composite indicator based on variables not yet used in the literature. The underlying theoretical framework is identified based on the literature on regional competitiveness. The proposed indicator consists of the following seven dimensions of competitiveness: Education, Job, Economic Wellbeing, Territory and Environment, Entrepreneurship, Innovation, and Infrastructures and Mobility. Data are retrieved mainly from administrative sources, for 2014 and 2015. In the building process, three aggregation methods are compared: a compensatory method, the arithmetic mean, and two partially compensatory methods, the geometric mean and the Adjusted Mazziotta-Pareto Index (AMPI). The arithmetic mean turns out to be the most robust method among the three considered, but the AMPI is the most robust method among the two partially compensatory methods. All the methods considered agree in identifying Innovation and Entrepreneurship as the most influential pillars in 2014 and 2015, respectively. The detailed geographical focus provides specific insights into territorial competitiveness in Italy. It emerges a rather heterogeneous picture of municipal competitiveness within the Italian regions. Highly competitive municipalities are present in every region, though with different concentration levels. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the most important disease of the heart, and the stage of the disease is diagnosed, the disease can be diagnosed anytime. The following method is used to find out its status. The heart disease prediction is based on Bagging Ensemble Technique with Deep Belief Network (DBN) algorithms. The problem for heart disease prediction from the collection of the dataset. Feature extraction using the Bag of Words methods to the correct data and discontinuities collect the heart disease-based matching data's extraction from the dataset and the various combination of the data set is available in Kaggle. It is mostly used for text classification methods. The proposed system of data mining is the most important technique for data aggregation. Data mining has various methods available, and one of the techniques is the bagging Ensemble Technique. In this method using for homogeneous data are quickly collected and parallel processing for the data collections. The first process for collecting data using the bagging Ensemble Technique is based on collected preprocessing. The Cardiovascular Disease prediction using Deep Belief Network (DBN) algorithm is compared with the existing system heart disease prediction, to prediction for display the data past and present movement in classification time and prediction accuracy, sensitivity and specificity. The proposed system for classification is compared with various techniques, and the proposed methods are DBN algorithms compared to compare to 5000 data'. The performance of CNN is 89%, RNN is 90%, LSTMs is 92%, and DBM is 95.6%. Finally, the heart disease prediction or classifications are given by the DBN algorithm. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach's exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion. [ABSTRACT FROM AUTHOR]