Aggregation Scheme Research Articles

Learning asymmetric embedding for attributed networks via convolutional neural network

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods.

SEMDA: Secure and Efficient Multidimensional Data Aggregation in Smart Grid without a Trusted Third Party

Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user’s electricity consumption or user’s main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user’s multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users’ privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption.

Edge-Enabled: A Scalable and Decentralized Data Aggregation Scheme for IoT

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\sqrt{n})$</tex-math></inline-formula> with constant rounds, as opposed to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\log n)$</tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.

FC-SEEDA: fog computing-based secure and energy efficient data aggregation scheme for Internet of healthcare Things

FC-SEEDA: fog computing-based secure and energy efficient data aggregation scheme for Internet of healthcare Things

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection.

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods.

Estimation of Panel Data Models with Mixed Sampling Frequencies*

AbstractStandard panel models usually assume that data are available at the same frequency. Occasionally, researchers might work with variables sampled at different frequencies. A common practice is to aggregate all variables to the same frequency by an equal weighting scheme. We show that such a simple aggregation scheme results in biases for common estimators. We propose a data‐driven method to determine weights for aggregation. We further demonstrate that, in contrast with single‐frequency panel models, the Mundlak device and the Chamberlain's approach lead to different estimators for panels with mixed sampling frequencies. The proposed estimators have satisfying finite sample performances in various simulation designs. As an empirical illustration, we apply the new method to the estimation of the effects of temperature fluctuations on economic growth. The empirical evidence shows that the temperature shocks mainly work through the level effect instead of the growth effect for poor countries.

Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

Energy-efficient secure data fusion scheme for IoT based healthcare system

The Internet of Medical Things (IoMT) ecosystem is enormous and complicated, with several smart devices constantly transferring large amounts of data. Many IoT-based healthcare devices are used to gather and transmit medical data. Consequently, IoMT faces many challenges related to energy efficiency, security, and privacy-preserving due to heterogeneous data. In IoMT, healthcare devices generate a huge volume of data and transfer it to the server, which is less secure and results in data redundancy. Therefore, there is a requirement for an energy-efficient secure data aggregation scheme as the traditional data aggregation paradigms encountered several issues for safely aggregating and transmitting healthcare data, including higher energy consumption, security, privacy, and vulnerability to attacks. In this paper, a secure data fusion-based data aggregation scheme is proposed, which optimizes energy usage and improves data quality. The proposed method uses extended belief propagation to determine the link quality of the shortest routes between sensor nodes and base station (BS) for data transmission and to reduce energy consumption. Further, the active sensor selection problem is optimized using the Archimedes Optimization Algorithm (AOA), which maximizes the spatial and temporal correlation with the passive sensor nodes to improve data quality. The proposed scheme has been implemented in NS2.35 by generating trace files and extracting results with AWK scripts. The simulation results demonstrate that our method outperforms the other state-of-the-art methods in terms of energy usage, network connectivity, reliability, computational cost, latency, and communication overhead. Thus, the proposed method is an efficient and secure data aggregation scheme for IoT-based healthcare.

Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least 38%, and the computational efficiency in the billing phase is improved by at least 40 times.

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.

Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach

The deep integration of industrial internet of things technologies in the industrial smart grid (ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying system infrastructures and associated services. That, in turn, raises the necessity for anomaly detection staged by appropriate authorities. Deep learning can provide a promising solution for anomaly detection, but it remains untrustworthy as it fails to do well with small-size labeled data and class-imbalanced data. To solve these issues, this article introduces a novel privacy-preserving federated semisupervised class-rebalanced (Fed-SCR) framework for the detection of anomalous power data in fog-assisted smart grids. Fed-SCR introduces a semisupervised generative network to enhance the quality of generated minority samples and model the relationships between labeled and unlabeled data. Moreover, the generator and discriminator are block-structured leveraging temporal convolutions to improve the representation power during the training. A novel aggregation scheme [termed federated geometric median aggregation (Fed-GMA)] is also introduced based on selective and periodic geometric-median-based aggregation with the main aim to increase the robustness of the federated semi supervised class-rebalanced (Fed-SRC) against noisy gradients while maintaining communication efficiency. The evaluations of Fed-SCR on public power grid datasets reveal its efficiency in improving the trustworthiness of the ISG platform, outperforming the competing methods in terms of binary classification (accuracy: 97.28) and multiclass classification (accuracy: 96.36–95.04).

National Land Cover Database 2019: A Comprehensive Strategy for Creating the 1986–2019 Forest Disturbance Product

The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products .

A two dimensional Markov chain model for aggregation-enabled 802.11 networks

Frame aggregation technique opts for optimising channel usage efficiency for 802.11-based networks by amortising the transmission overhead over several aggregated packets. Despite its potential benefit, the gain achieved by this technique is still far from the expected levels. The underlying causes are attributed to certain deficiencies in the specification as well as the implementation of the conventional frame aggregation scheme. Throughout this paper, we focus on MAC protocol data unit aggregation (A-MPDU) technique. We provide a simple, yet, highly accurate mathematical model for the conventional A-MPDU technique that reflects the effect of the block acknowledgement (Block Ack) window limit on the maximum aggregation size. The effectiveness of our model is validated by ns-3 simulator. An analytical-based study is further conducted to compare the performance of the greedy A-MPDU aggregation scheme and that of the conservative scheme supported by most of wireless fidelity (Wi-Fi) card drivers.

Temporal Upscaling of MODIS 1-km Instantaneous Land Surface Temperature to Monthly Mean Value: Method Evaluation and Product Generation

The monthly mean land surface temperature (MMLST) reflects more stable intra- and inter-annual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), a simple average model for two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is <20 and ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is ≥20. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error of 1.6 K. The intercomparison with MMLSTs from geostationary satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with AIRS3STM product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, it is important to select an aggregation point well. The total number of transmitted data may vary, depending on the aggregation point to which the data are delivered. Therefore, in this paper, we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme. In addition, we evaluate the proposed scheme with extensive computer simulations. Better performances in the proposed scheme are achieved compared to the conventional approach.

Secure Identity-Based Designated Verifier Anonymous Aggregate Signature Scheme Suitable for Smart Grids

Safe and efficient collection of users’ electronic consumption data is a basic function of the smart grid. Anonymous aggregate signature is a fine option to realize this feature, which can not only reduce the computing cost of the data center (DC) but also protect the user’s identity information from leaking. The security proofs of all known identity-based aggregate signature (IBAS) schemes were completed in the random oracle model (ROM). Regrettably, these schemes are not necessarily secure in real life. In this article, we first propose the system model and security demands of an identity-based designated verifier anonymous aggregate signature (IB-DVAAS) scheme for smart grids. We then come up with a concrete scheme, only the designated DC can check the validity of an aggregate signature, but it cannot recognize the real identity of the signers. Next, we display the security proofs in the standard model. Finally, we conduct an analysis on performance for six aggregate signature schemes. Only three pairing operations are required and hash-to-point operations are not used, the calculation cost of the new scheme is lower than that of other schemes.

Cross-Tier Interference Mitigation in 3D HetNets for LTE and Wi-Fi Access

Due to the unprecedented rate of transformation in the field of wireless communication industry, there is a need to prioritise the coverage, network power and throughput as preconditions. In Heterogeneous Networks (HetNets) the low power node inclusion like Femto and Pico cells creates a network of Multi-Tier (M-Tier) which is regarded as the most significant strategy for enhancing the coverage, throughput, 4G Long Term Evolution (LTE) ability. This work mainly focuses on M-Tier 3D Heterogeneous Networks Energy Efficiency (EE) based Carrier Aggregation (CA) scheme for streaming real-time huge data like images. At first, M-Tier 3D HetNets scheme was made for investigating Signal to Noise Interference Ratio (SNIR) on assessing the collective Pico-tier and Femto-tier interference. Next, the scheme of channel allocation is scrutinised so as to estimate throughput from the multiple tiers. Additionally, with the use of CA technique, the problem of energy efficiency for M-Tier 3D Heterogeneous Network (HetNet) in relation to energy metrics and throughput was evaluated with the use of LTE and Wireless Fidelity (Wi-Fi) coexistence. The simulation is carried out in a MATLAB setting, and the outcomes reveal a huge impact on EE. The simulation is carried in terms of EE, transmission time, throughput, packet success rate, convergence probability, and coverage region. The analysis from simulation shows that on improving the output of the device, interference among small cell base stations is reduced on increasing EE. The outcomes attained aid in the effective creation of M-Tier 3D HetNets for enhancing EE by employing Multi-Stream Carrier Aggregation (MSCA) in HetNets.

Energy-efficient Data Aggregation in Low-power Wireless Networks with Sensors of Discrete Transmission Ranges: A Mathematical Framework for Network Design

The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such big data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Secure and Lightweight Data Aggregation Scheme for Anonymous Multi-Receivers in WBAN

Wireless body area networks (WBANs) provide healthcare providers with early warning of medical emergencies by monitoring vital parameters of a patient's body with wearable devices embedded with biosensors. However, the resource-constrained nature of WBAN devices makes the data collected from them vulnerable to internal and external attacks during transmission. By aggregating medical data, data aggregation technology is used to conduct statistical analysis of the data and achieve confidentiality of patient information, which can help doctors make the correct diagnosis and assist medical insurance companies choose the right insurance for patients. However, the existing data aggregation schemes are inefficient or prone to potential safety risks to patients due to their high computational intensity and known identity of recipients. To address it, we propose a privacy-preserving health data aggregation scheme in the multi-receiver setting, which not only securely collects health data from IoT wearable devices but also supports the anonymity of multiple receivers while avoiding time-consuming pairing operations. Since the patient's health data and the recipient's identities are transmitted in cipher-text, it effectively prevents data leakage and achieves privacy protection of identity information among recipients. Compared with several existing schemes, the proposed scheme not only achieves anonymity of the collected data and the receivers' identities,but also makes it lightweight. Furthermore, experimental results demonstrate that the proposed scheme is cost-efficient in an all-around performance.