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As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users’ data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively.

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.

The characteristics of data aggregation with different network environments and dynamic changes in channel availability make some problems in IoT data aggregation. Therefore, this paper proposes an FMA-coverage model for algorithm design based on edge information. The FMA-coverage model includes the method of edge frequency, the method of primitive length (stroke), the texture energy metric of Laws and the method of fractal texture description. The FMA-coverage model can improve the network performance of IoT data aggregation. From the computational analysis, it can be seen that the security of data storage is only 17%. After the improvement of the fast matching algorithm, the security is up to 87%. After the network coding scheme, the IoT performance of data aggregation is up to 95%. It is important to note that, in this case, the required transmission volume in the network can be greatly reduced when the links are long. The IoT performance is up to 97% with the compression-aware scheme. By cross-sectional comparison, the IoT-based mobile model has the highest accuracy, with 98% accuracy of data aggregation. This paper extends the data aggregation mechanism by introducing fast-matching algorithms for device authentication and secure storage.

Secure data aggregation is essential in wireless sensor networks for lowering the amount of data transmitted and extending the lifetime of the network. The foundation underlying significantly greater industrial internet of things applications is primarily wireless sensor nodes. Sensors that have already been integrated within can be used to sense data in any form of real-time IoT application. In real-time physical surroundings, sensors utilize as little power as feasible to conduct operations including sensing, communicating, and processing data. Many investigations are being carried out to improve sensor node energy efficiency and network lifetime. To save energy, more attention must be paid to the clustering and routing aspects of communication. In this paper, we introduce Energy Efficient Greedy Tree based Data Aggregation (EE–GTDA) algorithm for efficient data aggregation with increased reliability and reduced energy consumption. It is a two-fold homogeneous technique that supervises safe energy-efficient connectivity and data aggregation with the greedy tree based solution that emphasizes multi-objective function. These methodologies are based on minimizing sensor energy consumption to maximize network lifetime simultaneously decreasing communication overhead. A trade-off between energy and safety is accomplished in order to increase efficient energy consumption with a higher packet delivery ratio.

Abstract For over‐the‐air computation (AirComp) systems, secure data aggregation is an important issue. This letter investigates joint transceiver optimization for secure data aggregation in orthogonal frequency division multiple access (OFDMA) AirComp systems. The aim is to minimize the computation mean square error of the fusion centre, while satisfying the security constraint of the computation mean square error of the eavesdropper and the power constraints of the sensors. The formulated optimization problem is non‐convex, and an effective iterative algorithm is proposed to solve it. Simulation results demonstrate the superiority of the proposed scheme.

Recently, the discipline of Brain-Computer-Interface (BCI) has attracted attention to exploiting Electroencephalograph (EEG) mental activities such as Motor Imagery (MI). Neurons in the human brain are activated during these MI tasks and generate an electrical potential of small magnitude reached to the scalp as a signal. Classification of MI data is a primary problem in BCI systems. Classification accuracy of these biomedical signals emerges as a significant task in the scientific community. This work proposes two main ideas: a new preprocessing technique based on four EEG frequency bands and a new stacking method for three deep-learning architectures used to decode three classes of MI signals. The preprocessing stage was introduced using Fast Fourier Transform to perform frequency analysis and data aggregation functions to enhance the data view. Performance was evaluated using well-defined metrics: accuracy, precision, recall, and f1-score for multiple batch sizes, optimizers, and epochs. Experimental results were evaluated using a publicly available dataset (BCI Competition IV dataset 2a) and local data collected from four subjects using the EMOTIV EPOC headset. The highest f1-scores achieved with the R-CNN model were 94% and 84% using the aforementioned datasets. Our proposed models also outperform many related models studied in the literature. [ABSTRACT FROM AUTHOR]

Monitoring and control systems integrated into agricultural machinery enable the development of agricultural analyses with advanced management tools, but the full use of all available data is often limited by the lack of uniformity among data transmitted from different agricultural machines. This paper presents an agricultural data aggregation and conversion model that allows for the collection and use of data captured from different agricultural machines in the course of work; these data differ in their original file formats and cannot be combined and used in a common analysis system. Programming work was carried out to create the model, and a specialised software interface enabled raster data processing using a Python library together with the open-source Hypertext Preprocessor and JavaScript programming language libraries. A PostGIS extension was utilised to engage field geometry and map-layering tools. Model validation showed that the data aggregation and conversion functions ensure the evaluation of semantic content and the transformation of the aggregated data into a unified format which is suitable for further use in intelligent farming management applications. The developed model will encourage precision agriculture, with the aim of improving work efficiency and the rational use of resources, the economy, and ecology in agriculture. [ABSTRACT FROM AUTHOR]

The security technology of digital twin is an important guarantee to ensure the security of digital twin operation, which mainly includes network security technology, data security technology and privacy protection technology. In wireless sensor networks, data aggregation technologies are known as a suitable solution to reduce energy consumption. In addition, due to wireless communications, wireless sensor networks are subject to many attacks. Therefore, it is very important to provide data security in the data aggregation process. In this paper, in order to protect data privacy and verify data integrity, moreover, balance the energy consumption and security during the data aggregation, we present a privacy and integrity–preserving data aggregation scheme for wireless sensor networks based on digital twins technology and homomorphic fingerprinting (HFPIDA). The HFPIDA adopts privacy function to protect data privacy and adopts homomorphic fingerprinting technology to verify the aggregation data integrity. Security analysis shows that the HFPIDA can effectively preserve data privacy and verify data integrity. Simulation results show that the HFPIDA requires less communication and energy overheads, and can achieve higher aggregation accuracy. [ABSTRACT FROM AUTHOR]

Most current data aggregation schemes treat data collected from smart devices as one-dimensional data and only support the aggregation of homogeneous types of data, but not the aggregation of multidimensional heterogeneous types of data. To address this problem, this paper proposes an edge-assisted hierarchical privacy-preserving multidimensional data aggregation mechanism (EPMA). In this mechanism, using a hierarchical aggregation framework assisted by edge computing, we propose a multi-region multidimensional data aggregation scheme that utilizes the homomorphic Paillier algorithm and Horner's law to achieve privacy aggregation while effectively reducing computation and communication overhead. It provides strong support for secure and efficient multidimensional data collection and communication. In particular, Horner's law allows different fine-grained aggregation results to be parsed from the aggregated ciphertexts, providing flexibility to meet different data analysis needs. In addition, we propose an efficient signature authentication method adopting lightweight elliptic curve encryption algorithms and bulk authentication techniques to ensure data integrity and identity validity. Finally, the security analysis proves that the EPMA mechanism is secure, and the theoretical analysis and simulation experiments illustrate that the EPMA mechanism has lower computational cost compared with other mechanisms and is more suitable for practical industrial application scenarios. [ABSTRACT FROM AUTHOR]

Summary: The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%. [ABSTRACT FROM AUTHOR]

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It's important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it's noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots. [ABSTRACT FROM AUTHOR]

During the past decades, Wireless Sensor Networks (WSNs) have become extensively used due to their prominent number of applications. The use of WSNs is a domineering need for future radical areas commencing from smart home to military surveillance in which hundreds or thousands of sensor nodes are positioned. The usage of mobile sink in those large scale WSNs, for data aggregation amends the functioning of the network by bringing down the energy conservation, amending the network lifetime and data transmission time lag between the nodes. In this paper Center of Energy -Reinforcement Learning based On-Demand Transition State Update algorithm (CERL-ODTST) is proposed to dynamically update mobile sink traversal path. Initially cluster formation and cluster head election for large scale WSNs are done by novel center of energy method. Clustering and data aggregation techniques are applied to reduce the amount of data transmission hence decreasing the energy consumption in the network. In this context cluster heads aggregates data from cluster members which are collected by mobile sinks. The amount of data transmission can be significantly reduced by using Machine Learning algorithms like neural networks and swarm intelligence and also using the distributive features of the network. It offers a reasonable study of the functioning of diverse methods to support the engineers for projecting suitable machine learning based results for grouping the nodes and data aggregation applications. Compared to traditional methods, in the proposed CERL-ODTST, reinforcement learning is used for intra cluster data aggregation to improve aggregation efficiency in the whole network. The implementation results show that proposed CERL-ODTST performs well in terms of overall tour length, energy efficiency and reduces the transmission delay hence increases network lifetime. [ABSTRACT FROM AUTHOR]

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With the emergence of IoT devices, data aggregation in the area of smart grids can be implemented based on IoT networks. However, the communication and computation resources of IoT devices are limited so it is not possible to apply conventional Internet protocols directly. On the other hand, gathering data from smart meters in the advanced metering infrastructure faces challenges such as privacy-preserving and heavy-loaded authentication and aggregation schemes. In this paper, we propose an improved lightweight, secure, and privacypreserving scheme for aggregating data of smart meters in largescale IoT-based smart grids. The proposed scheme adopts lightweight operations of cryptography such as exclusive-OR, hash, and concatenation functions. In comparison with the schemes in the literature, the analysis and simulation results show that the proposed scheme satisfies the same security levels, while at the same time burdens lower computation and communication overheads. This observation makes the proposed scheme more suitable to be employed in large-scale and IoT-based smart grids for data aggregation. [ABSTRACT FROM AUTHOR]

Reducing flood risk through disaster planning and risk management requires accurate estimates of exposure, damage, casualties, and environmental impacts. Models can provide such information; however, computational or data constraints often lead to the construction of such models by aggregating high‐resolution flood hazard grids to a coarser resolution, the effect of which is poorly understood. Through the application of a novel spatial classification framework, we derive closed‐form solutions for the location (e.g., flood margins) and direction of bias from flood grid aggregation independent of any study region. These solutions show bias of some key metric will always be present in regions with marginal inundation; for example, inundation area will be positively biased when water depth grids are aggregated and volume will be negatively biased when water surface elevation grids are aggregated through averaging. In a separate computational analysis, we employ the same framework to a 2018 flood and successfully reproduce the findings of our study‐region‐independent derivation. Extending the investigation to the exposure of buildings, we find regions with marginal inundation are an order of magnitude more sensitive to aggregation errors, highlighting the importance of understanding such artifacts for flood risk modelers. Of the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. This work provides insight into, and recommendations for, aggregating grids used by flood risk models. Key Points: Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study regionThis aggregation is shown to always positively bias inundation area when water depth grids are aggregatedFor example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada [ABSTRACT FROM AUTHOR]

One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision‐making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision‐makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision‐making. To the best of our knowledge, the integration of ambiguous information into decision‐making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision‐maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid. [ABSTRACT FROM AUTHOR]

This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data. [ABSTRACT FROM AUTHOR]

For example, in instances where existing data might include administrator-assigned race (e.g., birth certificate data), be explicit in writing about the impact on both data quality (i.e., how assigned race differs from self-reported race), the particular latent construct these assigned race data proxy (e.g., racialization processes as a domain of racism), and potential mechanisms underlying the effects of such constructs on health outcomes (e.g., how systems may interact with people and patients as a function of the assigned racial category to which they are assumed to belong). Keywords: data aggregation; data analysis; data collection; health equity; implementation science; race factors; systemic racism EN data aggregation data analysis data collection health equity implementation science race factors systemic racism 262 267 6 07/17/23 20230802 NES 230802 INTRODUCTION Improved data equity - specifically, a transparent, critically grounded approach to race and ethnicity data (dis)aggregation - is necessary to document, understand, and address the health effects of racism.[1] The absence of such a systematic process may result in unintended and unattended public health harms. Data aggregation, data analysis, data collection, health equity, implementation science, systemic racism, race factors. [Extracted from the article]

Multiple unmanned aerial vehicles (UAVs) are organized into clusters in a flying sensor network (FSNet) to achieve scalability and prolong the network lifetime. There are a variety of optimization schemes that can be adapted to determine the cluster head (CH) and to form stable and balanced clusters. Similarly, in FSNet, duplicated data may be transmitted to the CHs when multiple UAVs monitor activities in the vicinity where an event of interest occurs. The communication of duplicate data may consume more energy and bandwidth than computation for data aggregation. This paper proposes a honey-bee algorithm (HBA) to select the optimal CH set and form stable and balanced clusters. The modified HBA determines CHs based on the residual energy, UAV degree, and relative mobility. To transmit data, the UAV joins the nearest CH. The re-affiliation rate decreases with the proposed stable clustering procedure. Once the cluster is formed, ordinary UAVs transmit data to their UAVs-CH. An aggregation method based on dynamic programming is proposed to save energy consumption and bandwidth. The data aggregation procedure is applied at the cluster level to minimize communication and save bandwidth and energy. Simulation experiments validated the proposed scheme. The simulation results are compared with recent cluster-based data aggregation schemes. The results show that our proposed scheme outperforms state-of-the-art cluster-based data aggregation schemes in FSNet. [ABSTRACT FROM AUTHOR]

Modern cities, along with their researchers and innovators can benefit from applying “ big data” to their sustainability and infrastructure problems and policies, e.g., water and energy consumption. Unfortunately, current utility customer data (UCD) privacy rulemaking fails to ensure safe release of these data for the public benefit and does not currently strike a sound balance between the competing values of usability and privacy. This paper presents a statistical analysis of the tradeoff between usability and privacy for UCD in Los Angeles. The tradeoffs vary by economic sector (residential vs. commercial/industrial) and by utility type (water, electricity, natural gas). This paper provides guidance for safer and more ethically balanced aggregation and release of utility customer data.

In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.

The article highlights the issue of a comprehensive consideration of three forms of data collection on the migration histories of displaced persons and four ways of presenting them in scientific publications. It is emphasized that today the most commonly used formats among scholars are conventional questionnaires, the life history calendars, and the migration history charts, which can be used methodically alternatively as independent tools or complement each other in data collection. The author also identifies analytical situations in which it is appropriate to test these forms, namely: 1)conventional questionnaires are effective when it is necessary to record a few facts about an individual’s migration history, and the information itself is quantitative in nature; 2)the life history calendars are optimal for forming a complete migration history, being a tool aimed at covering various spheres of a person’s life throughout his or her existence; and 3)the migration history charts are more valuable when the focus is on long-term interpersonal ties (usually family) in a spatial and chronological sense. Additionally, emphasis is placed on disclosing the differences that arise from each form of collection and have relevant implications for the analysis of the data. The paper considers four possible ways of implementing data from migration histories in scholarly works, including: 1)case studies; 2)a basis for identifying and classifying patterns; 3)aggregation of quantitative data; and 4)informational background that is not explicitly expressed in the text. The author highlights the idea that the place of migrants’ histories has acquired a new meaning, since in recent years they have been perceived not exclusively as primary material for the researcher’s analytical work, which without further secondary comprehension will not be of cognitive value, but as a key source on the basis of which significant conclusions are drawn. The author marks that, due to their conceptual proximity to the idea of „oral history”, migrants’ histories can potentially become a powerful evidence base in political discourse and international law regarding crimes against humanity. It raises the current „Ukrainian issue” around the world in a particularly scientific and pragmatic way, given the events of last year’s military aggression by Russia, which continues to be ongoing.

The environmental heterogeneity–biodiversity relationship is generally hypothesised to be positive, with greater heterogeneity leading to greater biodiversity. However, the generality of positive environmental heterogeneity–species richness relationships is often debated, with some studies finding non-significant or even negative relationships. Negative relationships have primarily been found at fine spatial scales. Both negative and positive relationships have a basis in ecological theory. Environmental heterogeneity at coarse scales opens up niche space to allow more species to coexist; whereas high local heterogeneity, for instance in topography, may lead to increased local extinction due to micro-fragmentation, or dominance of species suited to heterogeneous conditions. However, it is difficult to attribute how much of the variance is explained at different scales within the same modelling framework.Here, we use a new data-aggregation method which enables us to include both fine- and coarse-scale environmental heterogeneity within the same analysis. Using this method, we were able to tease apart the fine- and coarse-grain effects of topographic heterogeneity on European tree species richness. At the coarse scale (0.5 degrees), we found a positive effect of range in elevation on tree species richness. However, when measuring range in elevation using a fine-scale moving window of radius 500 m, we found a negative relationship with tree species richness. This supports existing research that has shown negative relationships between environmental heterogeneity and species richness at finer spatial grains. Because we were able to include a measure of both local and landscape-scale topographic heterogeneity in the same model, for the first time we could fully capture the effects of both scales on coarse-grain species richness while accounting for the effect of the other scale.

Introduction: IoT networks require a variety of safety systems, because of evolving new technologies. They are subject to assaults and require a variety of security solutions. Because of the sensor nodes' limited energy, compute capabilities and storage resources, identifying appropriate cryptography is critical in wireless sensor networks (WSN). Objective: So, we need a new energy-aware routing method with an excellent cryptography-based security framework that fulfills critical IoT needs such as dependability, energy efficiency, attacker detection and data aggregation. Methods: Intelligent dynamic trust secure attacker detection routing (IDTSADR) is a novel energy-aware routing method suggested for WSN-IoT networks. IDTSADR fulfills critical IoT needs such as dependability, energy efficiency, attacker detection and data aggregation. IDTSADR is an energy-efficient routing technique that discovers routes that use the least amount of energy for end-to-end packet traversal and improves malicious node detection. Our suggested algorithms take connection dependability into account to discover more reliable routes, as well as a goal of finding more energy-efficient routes and extending network lifespan by finding routes with nodes with greater battery charge levels. We presented a cryptography-based security framework for implementing the advanced encryption approach in IoT. Conclusion: Improving the algorithm's encryption and decryption elements, which currently exist and provide outstanding security. From the below results, we can conclude that the proposed method surpasses the existing methods, this difference obviously prolonged the lifetime of the network.

The construction of smart grids provides many benefits. Computational cost, however, drastically grows with an increase in scale since a large amount of data is generated, transmitted, collected, and treated. Although data aggregation technology is helpful when adopting smart grid applications, corresponding security and performance issues should be considered while implementing the mechanism. In this paper, a blockchain-enabled authenticated conserved data aggregation scheme is proposed to balance security concerns and the computational cost of the smart grid. Furthermore, several functions are developed in our scheme to highlight the contributions. First, efficient cryptographic algorithms are integrated instead of computationally-expensive ones. Second, the proposed scheme seamlessly incorporates a blockchain system into a smart grid for better decentralization which can avoid the possible threats and high cost of a centralized system. Third, the scheme is scalable, which can be adapted to manage a great number of metering devices. Fourth, the aggregational operations in our design combine power data and signature, which is more practical. Finally, the proposed scheme covers a one-time key pair and signature mechanism, thus upgrading the privacy protection of blockchain applications in the smart grid.

Emerging Internet of Things (IoT) technologies and applications have enabled the Smart Grid Utility control center to connect, monitor, control, and exchange data between the smart appliances, smart meters (SMs), data concentrators (DCs) and control center server (CCS) over the Internet. In particular, DC receives different Advanced Metering Infrastructure (AMI) applications data from multiple SMs for processing, queuing, aggregation, and forwarding onward towards the CCS over the things networking. However, DCs are expensive component of the AMI network. Recently, SMs are used as relay-devices to accomplish a cost-effective AMI network infrastructure to avoid the DC placement and bottleneck problem. However, SMs are recourse constrained (limited CPU, RAM, storage, and network capacity) intelligent devices which faces numerous communication challenges during outage conditions and summer peak hours where bulk amount of data with different traffic rates and latency are exchanged with the Utility control center. Therefore, an efficient data aggregation is required at relay-devices to deal with high volume of data exchange rates in order to optimize the constrained-resources of the AMI network. In this article, we propose a hybrid data aggregation strategy implemented on an aggregator-head (AH) in the clustering topology which performs data aggregation on the Interval Meter Reading (IMR) application data. AH induction greatly reduces the workload of the cluster-heads (CHs), and efficiently utilizes the constrained-resource of AMI devices in a cost effective-manner. The proposed strategy is evaluated for different existing approaches using the CloudSim simulation tool. Experimental and simulation results are obtained and compared which show the effectiveness of the proposed strategy such that limited resources are optimized, CH workload is minimized, and QoS of AMI applications are maintained.

This article discusses the situation that exists in several European countries, whereby information about archaeological sites and monuments, and that about finds recorded by members of the public (primarily via metal detecting), is held in entirely separate databases. This prevents heritage management decisions being taken with full awareness of known archaeology, and makes research that seeks to draw on multiple information resources difficult. The article demonstrates how the European ARIADNE e-infrastructure has facilitated the integration of large-scale artefact and site information. Over one million records from the British Museum Portable Antiquities Scheme database and over one million records for English sites, monuments, and grey literature have been integrated in an open access interface for the first time, permitting entirely new research questions to be addressed.

This article focuses on the aggregation of Cypriot archaeological datasets, digitally archived in local repositories, into the ARIADNE portal. It considers, in particular, the development of an application profile for inscriptions and presents the integration of two collections, consisting of ancient coins and inscriptions carved on stones. It highlights the tools and ontologies developed for the aggregation and management of these digital resources, as well as the related pipeline and activities. The issues encountered are also presented, plus the solutions adopted and the successful results in the data aggregation of these collections into the infrastructure. Currently, thanks to the pipeline, and the semantic tools developed and used in ARIADNE, a collection of Cypriot medieval coins and a corpus of Ancient Greek inscriptions are now more widely accessible to the archaeological community.

The archaeological research community was an early adopter of digital tools for data acquisition, organisation, analysis, and presentation of research results of individual projects. However, the provision of e-infrastructure and services for data sharing, discovery, access, and reuse has lagged behind. The ARIADNE Research Infrastructure has sought to address this situation. Developed with European funding, ARIADNE has created an e-infrastructure that enables data providers to register and provide access to their digital resources through the ARIADNE data portal, facilitating discovery, access, and research. ARIADNE has aggregated resources from over 45 data providers, spanning over 40 countries and 4 continents. The portal now provides online access to over 3.9 million research resources. It is based upon Linked Open Data technologies and is underpinned by a flexible and extensible architecture, enabling multiple combinations and presentations of the same underpinning data. We have been keen not to 'make a great heap' of all the data and, learning from previous data aggregation projects, we have defined a subset of the CIDOC CRM to be used as a strict ontology and paid close attention to data standards and controlled vocabularies to achieve a high degree of interoperability. This article discusses some of the challenges of large-scale data integration and describes the approaches adopted to ensure that the ARIADNE Knowledge Base is an effective tool for archaeological heritage management and research at a national and international level.

The compression on replicated string characters of large-sized data diminishes the consumption of the sensor's memory storage and power dissipation in the wireless sensor networks (WSNs). The intruders attack during data transmission in the clusters that humiliating the throughput rate. Therefore, we present a lossless compression and blockchain-assisted aggregation (CBA) with the rating-based energy-efficient cluster overlapping (REEC Overlap) method for effective resource utilization and data integrity. The Lempel–Ziv–Welch (LZW) method compresses the sensed data, and the source node transmits the packets to cluster head on the routes established by ad-hoc on-demand distance vector (AODV) routing protocol. Head node aggregates the data using consortium blockchain in which a federated consensus mechanism validates the blocks. Eventually, the base station (BS) decodes the received data packets using LZW decompression to recover the actual data. Simulation results of CBA-REEC Overlap obtain the decreased energy power consumption with a better compression ratio and the minimized network overhead compared to existing conventional algorithms. [ABSTRACT FROM AUTHOR]

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices under Raspberry-Pi 4B and Redmi-K30 Pro, respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution encompassing data aggregation, incentive design, and privacy protection. Specifically, inspired by FA, CROWD FA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWD FA, which supports various common data aggregation operations and bidding incentives. To address privacy concerns, CROWD FA relies solely on an efficient cryptographic primitive known as additive secret sharing to simultaneously achieve privacy-preserving data aggregation and privacy-preserving incentive. To instantiate CROWD FA, this paper presents a privacy-preserving data aggregation scheme (PRADA) based on CROWD FA, capable of supporting a range of data aggregation operations. Additionally, a CROWD FA-based privacy-preserving incentive mechanism (PRAED) is designed to ensure truthful and fair incentives for each participant, while maximizing their individual rewards. Theoretical analysis and experimental evaluations demonstrate that CROWD FA protects participants’ data and bid privacy while effectively aggregating sensing data. Notably, CROWD FA outperforms state-of-the-art approaches by achieving up to 22 times faster computation time. [ABSTRACT FROM AUTHOR]

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to the aggregator, altering the data aggregation results. Existing verifiable LDP protocols, which can verify the perturbation process and prevent output poisoning attacks, usually incur significant computation and communication costs, due to the use of zero-knowledge proofs. In this paper, we analyze the attacks on two classic LDP protocols for frequency estimation, namely GRR and OUE, and propose two verifiable LDP protocols. The proposed protocols are based on an interactive framework, where the user and the aggregator complete the perturbation together. By providing some additional information, which reveals nothing about the raw data but helps the verification, the user can convince the aggregator that he is incapable of launching an output poisoning attack. Simulation results demonstrate that the proposed protocols have good defensive performance and outperform existing approaches in terms of efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things in 5G and next-generation data communication networks have relied heavily on Wireless Sensor Networks (WSNs). Data aggregation, Energy consumption, bandwidth utilization, and dynamic traffic routing play a significant role and impose challenges in achieving QoS in a sensor network. Therefore, it is crucial to concentrate on these factors in order to increase the network's lifetime and quality of service. The research's goal is to analyse IoT WSNs in terms of their architecture, framework, security, data aggregation, and routing techniques. In IoT WSNs, the data aggregation technique reduces the energy consumption of the network's nodes. This improves the network's energy and other QoS parameter's efficiency. The network layer hybrid data aggregation and routing protocol proposed in this study is new and efficient. In this work, a novel method for data aggregation based on anchor-based routing and matrix filling theory is proposed. It is suggested to use improved Anchor-based Routing protocol for Event Reporting (ARER), an anchor-based routing technique that includes dynamic clustering and constrained flooding. In order to achieve QoS in IoT WSN, the proposed hybrid network layer protocol has outperformed. The proposed protocol has performed better in terms of QoS metrics like throughput, end-to-end delay, routing overhead, packet delivery ratio, and energy consumption. [ABSTRACT FROM AUTHOR]

Data Aggregation for IoT-WSN, based on Machine Learning (ML), allows the Internet of Things (IoT) and Wireless Sensor Networks (WSN) to send accurate data to the trusted nodes. The existing work handles the dropouts well but is vulnerable to different attacks. In the proposed research work, the Data Aggregation (DA) based on Machine Learning (ML) fails the untrusted aggregator nodes. In the attack scenario, this paper proposes a Machine Learning Based Data Aggregation and Routing Protocol (MLBDARP) that verifies the network nodes and DA functions based on ML. This work is to authenticate the nodes to support the MLBDARP, a novel secret shared authentication protocol, and then aggregate using a secure protocol. MLBDARP types of the ML algorithm, such as Decision Trees (DT) and Neural Networks (NN). ML helps determine the probability of a successful Packet Delivery Ratio (PDR). This proposed ML model uses predictability value, Energy Consumption (EC), mobility, and node position. Simulation results proved that the proposed protocol of MLBDARP outperforms Differentiated Data Aggregation Routing Protocol (DDARP) and Weighted Data Aggregation Routing Protocol (WDARP) with Quality of Service (QoS) parameters of Network Throughput (NT), Routing Overhead (RO), End-to-End Delay (EED), Packet Delivery Ratio (PDR) and Energy Consumption (EC). [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) have become increasingly important in recent years. Small and low‐power sensor nodes make up these sensor networks. A random distribution of nodes is made throughout an unmanaged target region. One of WSN's key challenges is its limited and irreplaceable energy supply. In most situations, sensor nodes cannot be replaced since they operate in a hostile physical environment. The act of gathering and aggregating usable data from different sensor nodes situated to perceive almost the same attribute of the occurrence is known as data aggregation. The mathematical model is used in this research study to generate cluster‐based data aggregation, which is an effective technique to increase energy usage by minimising the number of data transfers. The proposed mathematical model‐based data aggregation (MM‐DA) attains a 97% packet delivery ratio with minimal energy consumption. The MM‐DA outperforms other existing approaches in terms of packet delivery ratio (PDR), energy consumption (EC), network lifetime and control overhead. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Network (WSN), the consumption of energy is high due to the data transmission in the sensing region. The Optimized Distributed Voronoi-based Collaboration (ODVOC) optimizes data delivery and it suffers from huge energy consumption. To overcome this drawback, a data aggregation scheme is introduced to expand the lifetime of the network and to minimize the energy utilization. Data aggregation is a significant approach that preserves the unwanted usage of energy during data transmission. In this paper, the Optimized Distributed Voronoi-based Cooperation scheme with Energy-aware Dual-path Geographic Routing (DAOHS-ODVOC-EDGR) is proposed which incorporates the polydisperse aggregation scheme and it aggregates the indispensable data in the intermediary nodes. The effective data aggregation is achieved by the estimation of the waiting time of the data at every intermediary node and cost function that is used for electing the next-hop node. In the proposed protocol, the buffer value of every node is divided to retain diversified kinds of flow for effective and fair delivery of data. The rate of transmission at the source and intermediary node is altered during the congestion. The proposed DAOHS- ODVOC-EDGR protocol is tested via simulation scheme. The result of the simulation exposes that DAOHS-ODVOC-EDGR outperforms the TESDA and DVOC protocol in terms of network lifetime, energy efficiency, and hop count delay. [ABSTRACT FROM AUTHOR]

Compared with the traditional power grid, smart grid involves many advanced technologies and applications. However, due to the rapid development of various network technologies, smart grid is facing the challenges of balancing privacy, security, efficiency, and functionality. In the proposed scheme, we design a privacy protection scheme for outsourcing smart grid aided by fog computing, which supports fine-grained privacy-protected data aggregation based on user characteristics. The fog server matches the encrypted characteristics in the received message with the encrypted aggregation rules issued by the service provider. Therefore, the service provider can get more fine-grained analysis data based on user characteristics. Different from the existing outsourcing smart grid schemes, the proposed scheme can achieve real-time pricing on the premise of protecting user privacy and achieving system fault tolerance. Finally, experiment analyses demonstrate that the proposed scheme has less computation overhead and lower transmission delay than existing schemes. [ABSTRACT FROM AUTHOR]

Wireless Sensor network has become hub for the industry and academia people due to its vibrant application and various characteristics like low cost, distributable, low-power technology, data compression and especially wireless communication. Moreover, in terms of application, it provides huge diversified monitoring flexibility for several important field like battlefield, agricultural monitoring, medical monitoring and environmental monitoring. Despite of such large application, there has been constant concern regarding the network lifetime and energy consumption is directly responsible for such issue. Meanwhile compressive sensing has been one of the popular data aggregation mechanism to reduce the data redundancy; hence, this research work design and develop a mechanism named ICCM (Integrated CS-clustering mechanism) which incorporates the clustering and compressive sensing mechanism to design and efficient WSN architecture which aims at network lifetime enhancement through Compressive sensing along with clustering. In ICCM approach, Cluster Heads utilize the novel and optimal CS mechanism for data transmission to Base station; further an novel optimized clustering approach is used for efficient clustering, also we design standalone logical link for data transmission. Furthermore, ICCM is evaluated considering the different parameter like network lifetime, energy consumption, functioning node and non-functioning node; also, comparative analysis with the existing model suggest that ICCM simply outperforms the existing model. [ABSTRACT FROM AUTHOR]

Among multi‐hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)‐hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%–20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission. [ABSTRACT FROM AUTHOR]

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT. [ABSTRACT FROM AUTHOR]