Total Amount Of Data Research Articles

Private and Secure Distributed Matrix Multiplication With Flexible Communication Load

Large matrix multiplications are central to large-scale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, i.e., the total amount of data to be downloaded from the workers. In this paper, in addition to exact recovery requirements, we impose security and privacy constraints on the data matrices, and study the recovery threshold as a function of the communication load. We first assume that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, we introduce a novel class of secure codes, referred to as secure generalized PolyDot (SGPD) codes, that generalize state-of-the-art non-secure codes for matrix multiplication. SGPD codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. We then study a connection between secure matrix multiplication and private information retrieval. We specifically assume that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, we present a variant of generalized PolyDot codes that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers.

Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data

AbstractThis study investigates the spatial–temporal evolution of aerosols, their optical properties (aerosol optical depth [AOD] and ångström exponent [AE]) and the trend over a period of 19 years in the Eastern European countries. The data used for the study are from moderate‐resolution imaging spectroradiometer (MODIS) Terra Collection 6.1 aerosol products with the use of Dark Target algorithm for the period from 2000 to 2018. The satellite‐based AOD products were validated against the AERONET AOD measurements in four stations, located in different regions. The results demonstrate high coherence in the setting of high values of correlation (R = 0.8–0.9) and low values of root‐mean‐square error (RMSE [0.066–0.130]) and mean absolute error (MAE [0.048–0.067]). However, of the total amount of data from AERONET stations, only 68.9% of available data fall within expected error (EE) over land. Generally, all countries recorded a low AOD (83.2% less than 0.2) and high AE (82.5% higher than 1). Mean AOD and AE values varied from 0.17 ± 0.12 and 1.31 ± 0.27 (Russia) to 0.24 ± 0.14 and 1.31 ± 0.33 (Czech Republic). There was a gradual decrease in aerosol load in all countries, the highest tendencies of AOD reduction are observed in Czech Republic, Bulgaria, Slovakia and Hungary (by −0.0028, −0.0027, −0.0026 and −0.0025 per year), while the lowest tendencies of reduction are observed in Russia and Moldova (by −0.001 and −0.0006 per year). Seasonal averaged AOD values have maximum values in summer and minimum values in winter. We studied predominant aerosol types, which are based on AOD and AE interrelation, the results showed the predominance of clean continental and mixed aerosols over all countries of the region.

Energy-Aware 3D Unmanned Aerial Vehicle Deployment for Network Throughput Optimization

Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.

VASEM: visual analytics system for electron microscopy data bank

Electron Microscopy Data Bank (EMDB) provides biological researchers with a platform for uploading and downloading data obtained mainly by electron microscopy. With the continuous expansion of the total amount of data stored in EMDB in recent years, its influence has also been continuously improved. In order to make it easier for domain scientists to make better use of these data, we analyzed the EMDB meta-information structure and developed a visual analytics system, called VASEM, to provide convenient data exploration, searching, and analysis tools. Domain scientists can use our system to interactively filter massive EMDB data into relatively small subset for analysis and obtain subset depiction from visualization results. A modified parallel coordinates plot suitable for large-scale multi-dimensional ranking data visualization is also proposed for EM data subset depiction tasks. We present two usage scenarios to describe how the proposed system achieves the design goals.

Energy-Efficient Power Control of Train–Ground mmWave Communication for High-Speed Trains

High speed train system has proven to be a very flexible and attractive system that can be developed under various circumstances and in different contexts and cultures. As a result, high speed trains are widely deployed around the world. Providing more reliable and higher data rate communication services for high speed trains has become one of the most urgent challenges. With vast amounts of spectrum available, the millimeter wave (mmWave) system is able to provide transmission rates of several gigabits per second for high speed trains. At the same time, mmWave communication also suffers from high attenuation, thus higher energy efficiency should be considered. This paper proposes an energy efficient power control scheme of train-ground mmWave communication for high speed trains. Considering a beam switching method for efficient beam alignment, we first establish position prediction model, the realistic direction antenna model and receive power model. And then we allocate the transmission power rationally through the power minimization algorithm while ensuring the total amount of transmission data. Based on this, this paper also develops a hybrid optimization scheme and finds the limit of total energy consumption when the number of segments goes to infinity. Through simulation with various system parameters and taking velocity estimation error into account, we demonstrate the superior performance of our schemes.

Reinforcement learning-based cell selection in sparse mobile crowdsensing

Sparse Mobile Crowdsensing (MCS) is a novel MCS paradigm which allows us to use the mobile devices to collect sensing data from only a small subset of cells (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality guarantee. Since selecting sensed data from different cell sets will probably lead to diverse levels of inference data quality, cell selection (i.e., choosing which cells in the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of data quality. To address this issue, this paper proposes the reinforcement learning-based cell selection algorithm for Sparse MCS. First, we model the key concepts in reinforcement learning including state, action, and reward, and then propose a Q-learning based cell selection algorithm. To deal with the large state space, we employ the deep Q-network to learn the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Then, we modify the Q-network to a deep recurrent Q-network with LSTM to catch the temporal patterns and handle partial observability. Furthermore, we leverage the transfer learning techniques to relieve the dependency on a large amount of training data. Experiments on various real-life sensing datasets verify the effectiveness of our proposed algorithms over the state-of-the-art mechanisms in Sparse MCS by reducing up to 20% of sensed cells with the same data inference quality guarantee.

An Intelligent Operation and Maintenance System for Power Consumption Based on Deep Learning

With the advent of the era of power big data, power companies can easily access more and more user power data, but they are not making full use of such massive datasets to reflect their value. Due to the huge amount of data collected by the power system, the total amount of abnormal data collected cannot be ignored. These abnormal data cause great interference to subsequent data analysis and maintenance; It’s also important to find how to identify user’s abnormal behaviour based on the power dataset. In this paper, a smart operation and maintenance system based on deep learning is proposed. Based on the composite mathematical statistics method, the data is efficiently reviewed and cleaned. The isolated forest algorithm is used to quickly identify the users with abnormal power consumption and GAN is used to help evaluate this model. This system can help power companies efficiently complete data review and anomaly identification, and can integrate data information from multiple dimensions to guide the actual maintenance and verification.

Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD).

Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.

Систематизація технологій стиснення зображень у системах поліграфічного репродукування

This work investigate the popular data compression techniques, which can be used to reduce the size of graphic image formats in printing reproduction systems. The modern printing production process is characterized by a high degree of automation and computerization, which is characterized by a large dependence on the productivity and efficiency of the desktop publishing system and the capacity of information exchange networks. Therefore, the use of data compression methods for digital images can reduce the total amount of data for illustrative original and increase the efficiency of the printing reproduction system.The analysis of previous studies on compression technologies for various areas: prepress printing processes, digital photography, video processing, various multimedia and telecommunication technologies. The main generalizations concern the consideration of lossless and lossy methods of data compression, which differ in coding algorithms.Based on the conducted research the systematization of basic image compression technologies in printing reproduction systems has been developed. The present systematization different in the main features: data compression methods, coding algorithms, image classes, compression value and image quality after coding.The basic encoding algorithms for compressing digital images RLE, JBIG, CCITT Group, LZW, Huffman, Wavelet (recursive), fractal and JPEG. The features of the data compression process according to Lossy JPEG (lossy) and Lossless JPEG2000 (lossless) algorithms are analyzed. Possible criteria for determining the efficiency and quality of image compression using various coding algorithms, which can be used for practical comparison of various compression techniques, are defined.

Determination of parameters of the Automated backup process of digital data for printing houses and publishing houses without use of external network technologies transformations

Results of pilot studies of influence of the average size of files, total amount of data and volume of the recovered data for the period of backup when using of the automated processes of creation of backup copies of figures for the medium-sized and small printing and publishing enterprises are provided. Experimental and analytical methods revealed the critical parameters influencing the speed of process of backup on condition of use of exclusively internal opportunities of the enterprises. As showed research, the most significant parameters are the optimum size of the file and total amount of data. The option of use of an archiving before direct copying of data is analyzed, positive and negative sides of this approach are shown. The optimum size of the file for process of backup at its use on one computer and without use of network technologies and external hard drives was established, its mean value makes 685 Mb. These researches allow increasing quality of backup process; will help with creation of the automated, autonomous and flexible systems of backup.

Impacts of forest spatial structure on variation of the multipath phenomenon of navigation satellite signals

Abstract The GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver’s antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quantification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.

A Stochastic-Based Rate Control Approach for Video Streaming Over Cognitive Radio Networks

Recently, the problem of video streaming over cognitive radio networks has attracted the attention of the research community due to the multiple challenges facing it. These challenges stem from the nature of the video content itself, the stochastic nature of wireless links as well as the intermittent availability of the licensed primary channels. Motivated by the above, this paper follows a different approach to tackle such a problem. Specifically, the proposed approach relies on obtaining an exact closed-form expression for the statistical distribution of the total amount of data that can be transmitted over the available primary users’ channels for any arbitrary interval of time. The validity of the obtained expression is verified through a comparison with Monte Carlo simulations. We then use this expression to propose a simple low complexity stochastic-based rate control approach for video streaming over cognitive radio networks. Due to its low computational complexity, the proposed approach can be easily implemented by any base station. The performance of the proposed approach is evaluated using the SimEvents simulator through reporting the average buffer occupancy, the peak signal-to-noise ratio and the inter-starvation interval. These metrics were reported for different parameters settings.

Dynamic self-reconfiguration of unmanned aerial vehicles to serve overloaded hotspot cells

Unmanned Aerial Vehicle (UAV) networks can provide wireless coverage to users when cellular infrastructure is unavailable. Each UAV covers a circular area, called a hotspot cell, and serves the users in it. Natural disasters can cause unpredictable mobility of users, randomly overloading some cells, called hot zones, for different time intervals. Along with an efficient initial deployment, this paper presents a two-fold approach to handle hot zones. First, the loads in overlapped sections are redistributed to neighbors. Second, a distributed algorithm is run that dynamically repositions the least number of UAVs to serve the maximum possible users. Additionally, to balance the user demand and supply of wireless coverage, adjacent UAVs are allowed to swap their positions. Simulation results show improvement in the network performance using the parameters: packet delivery ratio, data served per unit of depleted energy, service discontinuity time, and the total amount of data served.

State-dependent self-adaptive sampling (SAS) method for vehicle trajectory data

Mobile sensors provide new opportunities for traffic monitoring and data collection, which at the same time also impose challenges on data storage and transmission from the perspectives of the sensors (users). In this paper, we present a self-adaptive sampling (SAS) method for mobile sensing data collection. It uses the estimated vehicle flow states from individual trajectory to adjust the sampling rate. The methodology uses a Hidden Markov Model (HMM) classifier to classify individual data points into one of four basic vehicle flow states (Free Flow, Stopped, Acceleration, and Deceleration) and a Support Vector Machine (SVM) classifier to identify ‘stop and go’ segments in the trajectory. The identification of vehicle flow state is used for varying the sampling rate. The proposed SAS method can reduce the total amount of data by 67–77%, while keeping the most critical data points. The reduced data using SAS also show promising result in traffic modeling applications.

Survey of Big Data applications: health, education, business & finance, and security & privacy

Nowadays, Big Data is experiencing an exponential growth in all domains of life. The total amount of data created in the world from the beginning of time up until 2005 is now created every 48 hours! Big Data represents large datasets that cannot be analyzed using traditional computing techniques. Big Data has the potential to transform many aspects of our modern life, such as healthcare services, traditional businesses, politics, and security. Today, Big Data analytics (BDA) is used to predict whether a marriage will be successful or not. Scientists are currently using BDA to predict diseases that will affect humans, in order to invent suitable personal genome-based drugs. This study provides an abstracted review of Big Data applications in order to emphasize the uses of BDA in the following four domains of life: health, education, business and finance, and security and privacy. It also presents summarized reviews of the amazing aspects of Big Data applications, especially in the last four years. Most of the studies reviewed in this article were conducted and published in the last four years.

Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Notice of Violation of IEEE Publication Principles <br><br> “Research on Urban Land Price Assessment Based on Artificial Neural Network Model” <br> by Shousong Cai, Xiaomin Gu, Xiaoguang Wang, Ying Chen <br> in IEEE Access, Special Section on Urban Computing and Intelligence, December 2019, pp. 180738-180748 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This paper copied content from the paper cited below. The original content was translated and copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “Research on Urban Residential Land Price Assessment Based on Artificial Intelligence Reasoning Model” <br> by Biao Zhang <br> in the Masters Thesis, University of Electronic Science and Technology of China, June 2018 <br><br> <br/> At present, there are many problems to be solved in the study of residential land price evaluation, such as the imbalance of residential land price data categories, the small sample of residential land price data sets, the possibility of the evaluation model falling into the local optimal value, and the large error of feature quantification. This paper introduces the problems in the process of residential land price evaluation from three aspects: land price data, evaluation model and feature quantification. In this paper, an extraction method for land price features based on transfer learning is proposed in connection with defects such as the small total amount of residential land price data and unbalanced class distribution, and different land price assessment models are used to carry out a precision evaluation and explore the models and methods suitable for land price assessment. In this paper, the extraction method for residential land price features is improved. The experimental results show that the extraction algorithm for land price features based on transfer learning has better classification accuracy than the commonly used principal component analysis and extraction method and linear normalization method, with a mean average precision 10.9% and 4.73% higher, respectively. The optimal feature set classification accuracy reached 90.28%; therefore, the algorithm for residential land price assessment proposed in this paper is able to satisfy the precision requirement of actual land price assessment.

EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems

With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading.

A Novel Scheduling Algorithm to Improve Average User Perceived Throughput for LTE Systems

In recent years, the network operators have paid more attention to user experience. The design of scheduling algorithm is a great challenge in long term evolution (LTE) system to improve the overall system Quality of Experience (QoE). Many papers have been developed to solve the scheduling problem for the full buffer traffic. In this paper, a new scheduling algorithm named percent proportional fair (PPF) is proposed for the reality burst traffic. To evaluate the user experience, we introduce two measure indicators, average user perceived throughput (UPT) and user perceived throughput-cut (UPT-cut). In order to improve the average UPT with fairness guarantee, we design the scheduling priority by considering the ratio of the amount of transmitted data to the total amount of burst data. At the same time, an allocation matrix is introduced so as to avoid bandwidth waste. Finally, comparing the PPF scheduler with the classical proportional fair (PF) scheduler and fair allocation high throughput (FAHT) scheduler, the simulation results show the improvement of average UPT while ensuring fairness among the users. Besides, the performance of PPF scheduling is analyzed in various system scenarios.

An Energy Efficient Cooperative Communication Scheme in Ambient RF Powered Sensor Networks

The ambient radio frequency (RF) energy harvesting technology has recently been regarded as a potential solution for powering the wireless sensor networks (WSNs). However, the ultra-low power density of ambient RF energy is the main impediment to its further application. In this paper, we propose a novel energy efficient cooperative communication scheme (EECCS), which combines energy beamforming communication and ambient backscatter communication, to overcome the energy problem of WSNs powered by ambient RF energy harvesting. Moreover, to further reduce the energy consumption of nodes, we present an optimal resource allocation problem for EECCS. It can be formulated as a signomial geometric programming (SGP), which is nonconvex and NP-hard. We develop a Sequential Convex Approximation (SCA) algorithm for finding a solution of this SGP problem, which transforms the SGP problem into a sequence of geometric programming (GP) problems that are convex. The simulation results indicate that the EECCS can improve the energy efficiency of the ambient RF powered WSNs and maximize the total amount of data received by the sink.

Improving Synchronous Dataflow Analysis Supported by Petri Net Mappings

Whereas most of the work that analyses Synchronous Dataflow (SDF) stays in the dataflow framework, this work pushes its analysis into another framework level, thereby addressing issues that are not well addressed or are even unexplored in SDF. In this manner, the paper proposes a model-driven engineering (MDE) method, combining Synchronous Dataflow (SDF) and Petri nets, to highlight and reinforce their interoperability in digital signal processing applications, cyber-physical systems, or industrial applications. Improvements regarding the settlement and exploitation of the initial conditions associated with SDF are demonstrated; this issue is crucial for every cyber-physical system, since a system’s initial conditions are crucial to ensuring the system’s liveness. The improvements outlined in this work exploit an innovating mapping in the Place/Transition (P/T) Petri net domain that is intended to reduce and predict the total amount of initial data in SDF channels. The relevance of the firing semantics engaged with the equivalent Petri net model is discussed. This paper proposes a new approach to estimate whether an SDF has a static schedule by performing simulation and property verification of the equivalent-based P/T Petri net system achieved, framed by a Petri net invariant analysis and based on the stubborn set method of Petri nets. In this way, this new approach will allow mitigating the state explosion problem. Finally, a strategy is applied to two case studies to discover all the elementary circuits (static schedules) associated with the generated model’s state-space.