Low-cost Storage Research Articles

Optimal Caching for Low Latency in Distributed Coded Storage Systems

Erasure codes have been widely considered as a promising solution to enhance data reliability at low storage costs. However, in modern geo-distributed storage systems, erasure codes may incur high data access latency as they require data retrieval from multiple remote storage nodes. This hinders the extensive application of erasure codes to data-intensive applications. This paper proposes novel caching schemes to achieve low latency in distributed coded storage systems. Assuming that future data popularity and network latency information are available, an offline caching scheme is proposed to explore the optimal caching solution for low latency. The proposed scheme categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal caching decision through market clearing price for each cache partition. Furthermore, guided by the optimal scheme, an online caching scheme is proposed according to the measured data popularity and network latency information in real time, without the need to completely override the existing caching decisions. Both theoretical analysis and experiment results demonstrate that the online scheme can approximate the offline optimal scheme well with dramatically reduced computation complexity.

Feasible Region of Secure and Distributed Data Storage in Adversarial Networks

Large volumes of data are being generated daily from IoT networks, healthcare, and many other applications, which makes secure, reliable, and cost-effective data storage a critical infrastructure of the computing system. Existing data storage largely depends on a centralized cloud, which is not only costly but also vulnerable to single points of failure and other types of security attacks. Moreover, cloud providers will have full access to user data and revision history beyond user control. To provide data security, data encryption has to be used, which requires extensive computing power and cumbersome key management. Distributed storage system (DSS) is being widely viewed as a natural solution to future online data storage due to improved access time and lower storage cost. However, the existing DSS also has the limitations of low storage efficiency and lack of data security. In this paper, we investigate multi-layer code-based distributed data storage systems that can achieve inherit content confidentiality and optimal storage efficiency. Our comprehensive performance analysis shows that the optimal code can improve the feasible region in reliable data storage by 50% under various adversarial attack scenarios.

Sustainable production of lignin-derived porous carbons for high-voltage electrochemical capacitors

• CuCl 2 activation mechanism is elucidated. • Lignin-derived porous carbon with high specific surface area. • Lignin-derived porous carbon with high yields up to 49.4%. • High-voltage electrochemical capacitors are fabricated. The high energy storage cost is a crucial factor limiting the wide application of electrochemical capacitors. Herein, we proposed a comprehensive strategy to reduce the cost of electrochemical capacitors with aqueous electrolytes, i.e. , reducing the cost of electrode materials and increasing the energy density of electrochemical capacitors. Low-cost lignin-derived porous carbon electrode materials with high specific surface area and hierarchical porous structure were prepared by CuCl 2 activation. The CuCl 2 activation agent was recycled and thus can be reused. What is more, we proposed that the mechanism of CuCl 2 activation is a solid-phase reaction in which the chloride ions in copper chloride deprive the hydrogen atoms in lignin, resulting in the loss of hydrogen atoms and the formation of pores. High energy density C//C symmetric electrochemical capacitors, Zn//C and Pb//C asymmetric electrochemical capacitors based on lignin-derived porous carbon were fabricated using sulfate electrolytes which endow high working voltage window. This work provides a comprehensive strategy for designing electrochemical capacitors with high energy densities and low cost of energy storage, which is expected to promote the industrial application of electrochemical capacitors with aqueous electrolytes.

Trusted Blockchain-Based Signcryption Protocol and Data Management for Authentication and Authorization in VANETs

Vehicular Ad hoc Networks (VANETs) are the industrial cornerstone of intelligent transportation system (ITS), which are widely used in traffic management, automatic driving, and road optimization. With the expansion of the scale of the mobile ad hoc networks (MANETs) and smart vehicles (SV), VANETs will produce a large amount of data. In the open access environment of VANETs, the security of information transmission and the authenticity of user identity need to be considered when different vehicles communicate. In order to solve the cybersecurity risks of large-scale deployment of VANET, this paper proposes a trusted blockchain-based signcryption protocol and data management (TB-SCDM) for authentication and authorization (A&A) in VANETs. In the existing attack model, TB-SCDM can ensure the confidentiality and undeniability of information, as well as can effectively resist 51% attacks, eclipse attacks and double-spending attacks, etc. Through benchmark analysis, this scheme has higher computing efficiency and lower storage cost compared with other existing schemes.

High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network

Harvesting solar energy through photovoltaic (PV) power systems plays an important role in achieving the goal of carbon neutrality. However, the early microdefects in PV cells considerably affect the efficiencies of PV power systems. In addition, the growing number of PV power systems require more efficient and economic detection methods to ensure the long-term efficiency of PV power systems. To address this problem, recent state-of-the-art methods have attempted to use a convolutional neural network (CNN) model. However, these methods involve large amounts of parameters and require too many calculations, considerably affecting the efficiency, economy, and flexibility in real applications. In this work, we propose a lightweight dual-flow defect detection network (DDDN) and accelerate it with a field programmable gate array (FPGA) based on the developed dual-flow parallel computing architecture (DPCA). The DDDN can accurately detect early microdefects in PV cells with low calculations and storage costs, while the DPCA optimizes the data access and computation process to improve detection efficiency and reduce power consumption. Benefiting from the designed DDDN and DPCA, the proposed system achieves a high detection accuracy (88.26%), low power consumption (22 W), and competitive detection efficiency, making it more suitable than previous methods for ensuring the long-term efficiency of PV power systems.

BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability approaching but not 100%. Also, the process of detecting a click fraud is executed by a single publisher, which makes a chance for the publisher to obtain illegal income by deceiving advertisers and media sites. Besides, the identity privacy of consumers is also exposed because the schemes deal with the plain text of consumers’ real identity. Therefore, in this paper, a blockchain-based click fraud detection and prevention scheme (BCFDPS) for online advertising is proposed to deal with the above problems. Specifically, the BCFDPS mainly introduces bilinear pairing to implicitly verify whether a consumer’s real digital identity is contained in a click message to significantly avoid click fraud and employs a consortium blockchain to ensure the transparency of the detection and prevention process. In our scheme, the clicks by machines or fraud ones by a human can be accurately detected and prevented by media sites, publishers, and advertisers. Furthermore, ciphertext-policy attribute-based encryption is adopted to protect the identity privacy of consumers. The implementation and evaluation results show that compared with the existing click fraud detection and prevention schemes based on machine learning and statistical analysis, BCFDPS achieves detection of each fraudulent click with a probability of 100% and consumes lower computation cost; furthermore, BCFDPS adds functions of consumers’ privacy protection and click fraud detection and prevention, compared to the existing blockchain-based online advertising scheme, by introducing limited communication cost ( 4,984 bytes) at lower storage cost.

Monitoring and Evaluating College Students' Mental Health Based on Big Data Analysis

Objectives: Our aim was to overcome the low evaluation accuracy of traditional random sampling methods for college students' mental health, and to use the values of big data of college students' social network behaviors in the prediction and evaluation of their mental health. Methods: We monitored and evaluated college students' mental health through big data analysis. After generating the samples of college students' social network behaviors, a mental health monitoring and evaluation model was established based on a support vector machine (SVM) and decision tree (DT). Then, the DT model was pruned, and input data of the model were optimized by genetic algorithm (GA). Results: The optimal parameter combination was derived for our model. The maximum number of iterations was 60; the smallest number of samples needed for reclassifying internal nodes was 6; the number of samples with the fewest leaf nodes was 30. The mental health scores of most students fell in the interval [0, 6] for unobvious symptoms of mental crisis. The binary classification results of several models were as follows. On anxiety, all models surpassed the accuracy of 60%, except the traditional SVM. The optimal model, ie, Model 5, achieved an accuracy of 86.7%. On depression, all models exceeded the accuracy of 60%, and the GA-optimized DT 5 realized an accuracy as high as 83.1%. On drooping spirit, the optimal model, ie, GA-optimized DT 5, reached an accuracy of 89.5%, which is comparable to that of the GA-optimized SVM 4. Conclusions: The characteristic dimensions extracted by GA are representative. The primary mental states of college students can be estimated quickly and accurately by our model with a low cost of data storage, through the feature analysis of social network behaviors.

Testing the role of economic complexity on the ecological footprint in China: a nonparametric causality-in-quantiles approach

China is known for its large industrial sector and diversified energy mix, which could contribute to environmental pollution, as fossil fuels remain China's main source of energy. With the recent drive by the Chinese government to achieve low carbon emissions and further reduce greenhouse gases, this study adds to the existing literature by combining the quantile-on-quantile (QQ) regression and non-parametric techniques to examine the role of economic complexity, nonrenewables energy and renewable energy consumption on the ecological footprint in China over the period 1985Q1–2019Q4. Overall, results show that renewable energy, non-renewable energy use, economic growth and economic complexity affects ecological footprint positively. In addition, the nonparametric causality outcomes revealed that renewable energy, non-renewable energy use, economic growth and economic complexity can significantly predict variations in ecological footprint at different quantiles. We are of the opinion that policymakers in this region should work on the pro-growth mentality of China, which is majorly fossil fuel-driven. This requires an immediate replacement with more eco-friendly sources and energy-saving technologies for economic activities. Otherwise, fulfilling the SDG 13 goals in China will be challenging. For a sustainable renewable energy investment, China should shift to ancillary and spot markets, where the low energy storage and low marginal cost of renewable energy could facilitate higher reduction in electricity cost and encourage higher trading of electricity.

Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling

Data-driven building energy load prediction models should be updated dynamically to adapt to building performance degradation and changes of outdoor environment. Conventional dynamic modeling methods usually have to either increase the costs of model training and data storage for obtaining high accuracy, or decrease accuracy for reducing the costs of model training and data storage. Therefore, this paper presented an elastic weight consolidation-based sliding window fine-tuning method for dynamic building energy load prediction modeling with high accuracy, low computational costs, and low data storage costs. It adopted elastic weight consolidation to make a model be able to remember historical knowledge and learn new knowledge without storing historical data. Three common dynamic modeling methods (sliding window retraining, accumulative retraining, and sliding window fine-tuning) and a static modeling method were adopted for performance comparisons with the proposed method using the five-year operational data of a public building. According to the results, the mean absolute error of the proposed method decreased by 66.58%, 9.06%, and 8.70% on average compared with sliding window retraining, sliding window fine-tuning, and static modeling, respectively. It was further demonstrated that the proposed method had better stability than sliding window fine-tuning. Moreover, the proposed method showed similar accuracy to accumulative retraining, while it had lower costs of model training and data storage than accumulative retraining.

A Geospatial Cost Comparison of CO2 Plume Geothermal (CPG) Power and Geologic CO2 Storage

CO2 Plume Geothermal (CPG) power plants can use gigatonne-levels of CO2 sequestration to generate electricity, but it is unknown if the resources that support low-cost CPG power align with the resources that support low-cost CO2 sequestration. Here, we estimate and compare the geospatially-distributed cost of CPG and CO2 storage across a portion of North America. We find that the locations with lowest-cost CO2 storage are different than the locations with lowest-cost CPG. There are also locations with low-cost CO2 storage (<$5/tCO2) that do not support CPG power generation due to insufficient reservoir transmissivity or temperature. Thus, CPG development may require electricity prices that are greater than the levelized cost of electricity (LCOE) to offset the increased cost of sequestration. We introduce the “Additional Cost of Electricity (ACOE)” metric to account for this cost and add it to the LCOE to calculate breakeven electricity prices that are required for CPG development. We find that breakeven prices are lower when new CO2 injection wells are drilled specifically for CPG (i.e., “greenfield” CPG development) compared to if only existing CO2 sequestration injection wells are used (i.e., “brownfield” CPG development). This is because comparatively few wells are needed for sequestration-only, and the increased power capacity from having more CPG wells outweighs the increased costs from more drilling. We also find that sequestered CO2 could be used to approximately triple the United States geothermal electricity power capacity via a single CPG “sweet spot” in South Dakota, but that breakeven electricity price for this development is on the order of $200/MWeh.

Renewables and Advanced Storage in Power Systems: The Iberian Case

Storage has many benefits for power systems with a high share of renewable energy. It reduces renewable curtailment, can participate in ancillary services and contributes to system adequacy. However, its business model is far from clear since most of its revenues come from arbitrage in energy markets, and this is usually not enough to recover the investment. Advanced storage can facilitate the profitability of storage and ease the integration of renewables in power systems by reducing costs and allowing an enhanced performance. The profitability requirements of future advanced storage systems (batteries) are assessed in this paper by means of an optimization method and an uncertainty analysis for an optimal Iberian (Spain and Portugal) power system that meets the targets of their National Energy and Climate Plans. Results show that needed storage capacity is only a small part of the demanded energy, but technical advances are required for optimal performance. High prospective storage cost leads to a wind-dominated renewable mix, while low storage cost favours photovoltaics. Arbitrage with storage may cover its investment costs under carbon prices close to the actual Social Cost of Carbon.

A comparative study of different low-cost sensible heat storage materials for solar air heating: an experimental approach

ABSTRACT Among various applications of solar energy, solar air heater is a common method to fulfill the demand of hot air for space heating. In this experimental work, a low-cost solution is discussed for solar air heating. This solution deals with integration of modified models of air heaters with graphite powder, brick powder, and desert sand. These sensible heat storage materials have been filled inside a small cylindrical-shaped container to be placed over the absorber of modified heaters for performance enhancement. Total three different configurations have been developed for comparative analysis such as, configuration 1 for graphite powder testing, configuration 2 for brick powder testing, and configuration 3 for desert sand testing. Results are compared to the same design conventional heater to find out the best configuration, which shows that the configuration 1 is the best among all tested configurations. Under the configuration 1, enhanced heat transfer is observed about 541.2 W/m2K, thermal efficiency about 37.62%, overall heat loss about 6.71 W/m2K, and maximum exhaust air temperature about 41.2°C. For a better comparative analysis, configuration 1 is further compared to some other designs of solar heaters purposely for the plate temperature and thermal efficiency. Total cost of the best configuration (model) is about $59.5.

Information construction of construction project management based on internet of things blockchain

AbstractBecause of the complexity of construction projects and the frequency of project transactions, smart project management has a huge amount of transaction and operation data. Massive transaction and operation data urgently need a safe, efficient and low–cost storage and data analysis to realize fine management of buildings, low–cost transaction and interconnection of project information. Therefore, the construction project management system based on internet of things blockchain has been gradually improved, and more and more researches have been made on it. This article proposes a parallel design scheme. Parallel algorithms disperse the repeated calculation work on each node, reduce the calculation load and calculation time of a single node, effectively improve the efficiency of the algorithm, and enable it to process large–scale data of different sizes in parallel. Improve the algorithm according to the experiment, and analyze the performance of the parallel algorithm according to the experimental results. Experiments show that Apriori algorithm and K‐means algorithm based on MapReduce are more than 85° optimized than traditional data mining.

Study on Co-firing Characteristics and NOx emission of Ammonia/Propane

Reducing the use of carbon fuel is one of the important measures to achieve zero carbon emission. As a carbon-free and high-hydrogen fuel, ammonia has promising application prospects because of its high energy density and low transportation and storage cost. Due to the characteristics of high ignition temperature, low flame propagation combustion speed and high NOx concentration in the combustion process, its wide application needs further research. In this paper, the co-combustion and the NOx emission characteristics of ammonia mixed with propane are experimental investigated for pure ammonia, 5% propane and 10% propane mixtures. The effects of ammonia/propane ratio, air excess coefficient and inlet gas oxygen concentration on combustion and NOx emission characteristics are explored. The results obtained show that the highest NOx emission is formed by 10% propane ratio case, and the lowest NOx is for pure ammonia. With the increase of propane, the concentrations of NOx, NO and NO2 increase and the concentration of oxygen decreases. With the increase of air excess coefficient from 15% to 21%, NOx, NO and NO2 also increase, and the concentration of oxygen goes up. With the increase of inlet oxygen concentration from 15% to 21%, the concentrations of NOx and NO at the exit of furnace changes less for pure ammonia, and 75% increase for 5% propane; while for 10% propane, the concentration of NO first decreases rapidly from 1498ppm to 1071ppm with O2 decreasing from 15% to 18%, then increases to about 1700ppm again when the inlet O2 concentration increases from 18% to 21%.

Cost-effective ultra-high temperature latent heat thermal energy storage systems

The availability of cost-effective energy storage technologies with durations from 10 to 100 h is key for intermittent renewable energies, like wind or solar, to become a large share of the electrical grid power. Battery prices forecasted for the upcoming years are still too expensive; and storing the energy as heat instead of electricity, arises as a promising cheaper solution. Even if there is an efficiency penalty when converting heat back to electricity, the low cost of thermal energy storage (TES) systems is an important advantage. Besides, not always the heat stored in a TES system needs to be converted to electricity, as heat corresponds to about 50% of the global energy demand. In this work, the potential of Ultra-High Temperature Latent Heat Thermal Energy Storage (UH-LHTES), which can reach energy capacity costs below 10 €/kWh by storing heat at temperatures well beyond 1000 °C, is presented with the help of a Computational Fluid Dynamics (CFD) model. Modelling results, together with real materials performance and cost data, allow to estimate cost-efficiency boundaries for UH-LHTES systems for a range of sizes from 10 kWhth to 10 MWhth. The CFD model has been validated reproducing the real size and materials of a UH-LHTES prototype developed in the framework of the AMADEUS project. The CFD model is proven to be a reliable tool to reproduce the operation of a UH-LHTES system and it predicts quite precisely UH-LHTES prototype discharge rates and heat losses.

A blockchain-based and privacy-preserved authentication scheme for inter-constellation collaboration in Space-Ground Integrated Networks

Space-Ground Integrated Networks (SGINs) make it convenient for users to access services anywhere. In the space segment, satellite constellations can cooperate to provide better space services. However, due to the lack of trust among different constellations, communication security and privacy protection are important issues that need to be solved during inter-constellation cooperation. In this work, with recent advances of blockchain, we present a trusted and privacy-preserved authentication scheme for inter-constellation collaboration. To preserve privacy, we design permanent and temporary identities for each satellite. The permanent one is used for inner-constellation communication, and the temporary identity is for inter-constellation cooperation, respectively. Meanwhile, satellites generate temporary identities for themselves to avoid privacy leakage. A consortium blockchain is introduced for sharing information among cooperative satellite constellations. Furthermore, to achieve efficient authentication under limited resources, a replica storage node method is proposed, where well-resourced satellites cache the replicated information shared through the blockchain. The replica storage node selection is formulated as an integer programming problem and solved with a branch and bound algorithm. Security analysis, including formal analysis using BAN Logic and informal verification, shows that the proposed authentication scheme is secure against various potential attacks. A comparative analysis among the proposed scheme and other blockchain-based schemes shows that the proposed scheme achieves efficiency in signaling, computation, and communication overheads with more functionality attributes. In addition, evaluations also demonstrate that the proposed on-board caching scheme achieves low communication latency and storage cost.

Review of Carnot Battery Technology Commercial Development

Carnot batteries are a quickly developing group of technologies for medium and long duration electricity storage. It covers a large range of concepts which share processes of a conversion of power to heat, thermal energy storage (i.e., storing thermal exergy) and in times of need conversion of the heat back to (electric) power. Even though these systems were already proposed in the 19th century, it is only in the recent years that this field experiences a rapid development, which is associated mostly with the increasing penetration of intermittent cheap renewables in power grids and the requirement of electricity storage in unprecedented capacities. Compared to the more established storage options, such as pumped hydro and electrochemical batteries, the efficiency is generally much lower, but the low cost of thermal energy storage in large scale and long lifespans comparable with thermal power plants make this technology especially feasible for storing surpluses of cheap renewable electricity over typically dozens of hours and up to days. Within the increasingly extensive scientific research of the Carnot Battery technologies, commercial development plays the major role in technology implementation. This review addresses the gap between academia and industry in the mapping of the technologies under commercial development and puts them in the perspective of related scientific works. Technologies ranging from kW to hundreds of MW scale are at various levels of development. Some are still in the stage of concepts, whilst others are in the experimental and pilot operations, up to a few commercial installations. As a comprehensive technology review, this paper addresses the needs of both academics and industry practitioners.

Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology-Spotlight on Predicting Well Integrity Failures.

Artificial intelligence (AI) and machine learning (ML) are transforming industries, where low-cost, big data can utilize computing power to optimize system performance. Oil and gas (O&G) fields are getting mature, where well integrity (WI) problems become more common and field operations are now more challenging. Hence, they are good candidates for transformation due to the low cost of data storage, highlighting the oil market decline, along with dynamic risk posed during operations. This paper is presenting a comprehensive compilation of different ML applications in diverse disciplines of the petroleum industry. The pool of AI and ML with respect to different areas of applications along with publication years has been categorized. The main focus of this study is classifying well integrity failures where the authors found that the potential of AI and ML in predicting well integrity failures has not been efficiently tapped, and there is an explicit gap in the literature. First, the applications of AI, ML, and data analytics in the O&G industry are discussed thoroughly, so this paper can be a comprehensive reference for readers and future researchers. Then data preprocessing is explained. This includes data gathering, cleaning, and feature engineering. Next, the different ML models are compared and discussed. Finally, model performance evaluation and best model selection are described. This study would be a concrete foundation in the design and construction of ML programs that can be deployed for WI risk management. The developed model can be simply used for any well stock, providing quick and easy assessment instead of subjective and tedious assessment. The layout can be simply adjusted to reflect the risk profile of any well type or any field.

Multi-Manifold Deep Discriminative Cross-Modal Hashing for Medical Image Retrieval.

Benefitting from the low storage cost and high retrieval efficiency, hash learning has become a widely used retrieval technology to approximate nearest neighbors. Within it, the cross-modal medical hashing has attracted an increasing attention in facilitating efficiently clinical decision. However, there are still two main challenges in weak multi-manifold structure perseveration across multiple modalities and weak discriminability of hash code. Specifically, existing cross-modal hashing methods focus on pairwise relations within two modalities, and ignore underlying multi-manifold structures across over 2 modalities. Then, there is little consideration about discriminability, i.e., any pair of hash codes should be different. In this paper, we propose a novel hashing method named multi-manifold deep discriminative cross-modal hashing (MDDCH) for large-scale medical image retrieval. The key point is multi-modal manifold similarity which integrates multiple sub-manifolds defined on heterogeneous data to preserve correlation among instances, and it can be measured by three-step connection on corresponding hetero-manifold. Then, we propose discriminative item to make each hash code encoded by hash functions be different, which improves discriminative performance of hash code. Besides, we introduce Gaussian-binary Restricted Boltzmann Machine to directly output hash codes without using any continuous relaxation. Experiments on three benchmark datasets (AIBL, Brain and SPLP) show that our proposed MDDCH achieves comparative performance to recent state-of-the-art hashing methods. Additionally, diagnostic evaluation from professional physicians shows that all the retrieved medical images describe the same object and illness as the queried image.

Computing One-Bit Compressive Sensing via Double-Sparsity Constrained Optimization

One-bit compressive sensing gains its popularity in signal processing and communications due to its low storage costs and low hardware complexity. However, it has been a challenging task to recover the signal only by exploiting the one-bit (the sign) information. In this paper, we appropriately formulate the one-bit compressive sensing into a double-sparsity constrained optimization problem. The first-order optimality conditions for this nonconvex and discontinuous problem are established via the newly introduced <inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula>-stationarity, based on which, a gradient projection subspace pursuit (GPSP) algorithm is developed. It is proven that GPSP can converge globally and terminate within finite steps. Numerical experiments have demonstrated its excellent performance in terms of a high order of accuracy with a high computational speed.