Search

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process.ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.Contactpoh.chuehloo@nus.edu.sg

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various Home Area Networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This paper proposes a Federated Learning (FL) based model with privacy preservation of smart grids data using Serverless cloud computing. The model considers the Blockchain-enabled Dew Servers (BDS) in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

The rapid advancement of cloud computing has promoted the development of cloud storage services. One of the biggest concerns of cloud users is whether the completeness and recoverability of data can be guaranteed when cloud servers encounter problems. Only when the integrity of data is fully guaranteed can users consume cloud storage with confidence, especially in a complicated cloud environment with multiple clouds. However, the literature still lacks a thorough survey on cloud data integrity auditing for both single copy and multiple replicas. In this article, we survey and compare existing auditing schemes for single copy and multiple replicas based on a set of criteria. Based on our review and analysis, we discuss open issues, potential applications and future directions in the field of the integrity auditing in the cloud, including the implications of such trendy topics as merging blockchain and edge computing into data integrity auditing.

In the assembly pipeline of Whole Genome Sequencing (WGS), read mapping is a widely used method to re-assemble the genome. It employs approximate string matching and dynamic programming-based algorithms on a large volume of data and associated structures, making it a computationally intensive process. Currently, the state-of-the-art data centers for genome sequencing incur substantial setup and energy costs for maintaining hardware, data storage and cooling systems. To enable low-cost genomics, we propose an energy-efficient architectural methodology for read mapping using a single system-on-chip (SoC) platform. The proposed methodology is based on the q-gram lemma and designed using a novel architecture for filtering and verification. The filtering algorithm is designed using a parallel sorted q-gram lemma based method for the first time, and it is complemented by an in-situ verification routine using parallel Myers bit-vector algorithm. We have implemented our design on the Zynq Ultrascale+ XCZU9EG MPSoC platform. It is then extensively validated using real genomic data to demonstrate up to 7.8× energy reduction and up to 13.3× less resource utilization when compared with the state-of-the-art software and hardware approaches.

With the advent of cloud computing, it has become more and more popular to outsource various services to the cloud for releasing the burden of local data storage and maintenance. However, it may cause serious privacy problems because the cloud may be untrusted. In this paper, we study the privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. First, we introduce the concept of reference-locked order-preserving encryption (RL-OPE) with its construction and security proof, which reveals less information than traditional order-preserving encryption (OPE). Then, we present a novel PPRNN scheme in static setting based on structured encryption (SE) and the proposed RL-OPE, called sPPRNN. After that, we design a generic method that extends a PPRNN scheme in static setting to the counterpart in dynamic setting, called dPPRNN. Furthermore, we present a thorough privacy analysis of our proposal. Finally, we demonstrate its efficiency and effectiveness for practical deployment through extensive experiments.

Federated learning facilitates the collaborative training of a global model among distributed clients without sharing their training data. Secure aggregation, a new security primitive for federated learning, aims to protect the confidentiality of both local models and training data. Existing secure aggregation solutions, however, fail to defend against Byzantine failures which are common in distributed computing systems. It is challenging to realize efficient and secure aggregation schemes while mitigating Byzantine faults simultaneously. In this work, we propose a new secure and efficient aggregation framework, SEAR, for Byzantine-robust federated learning. Relying on a trusted execution environment, Intel SGX, SEAR protects the privacy of clients' models while enabling Byzantine resilience. Considering current Intel SGX's architecture (i.e., limited trusted memory), we propose two data storage modes for implementing aggregation algorithms efficiently in SGX. Choosing appropriate storage mode to implement aggregation algorithms brings significant efficiency improvements. To balance the efficiency and performance of aggregation, we propose a sampling-based method to detect the Byzantine failures efficiently without degrading the performance of the global model. We implement and evaluate SEAR in a LAN environment, and the experiment results show that SEAR is computationally efficient and robust to Byzantine adversaries.

Internet of Things (IoTs) composed of large number of sensing devices with a variety of features applicable for various applications. In such scenarios, due to low data handling capabilities, limited storage and security aspects, it is quite challenging to protect networks against illegal information access and utilizes storage efficiently. Though researchers provide various solutions for security and data storage, but a few solutions are appropriate for WSNs enabled IoTs. Therefore, a blockchain-based decentralized framework integrated with authentication and privacy preserving schemes is developed for the secure communication in wireless sensor networks (WSNs) enabled IoTs. Registration, certification and revocation process are employed for the communication with sensor nodes and Base Station (BS) in a cloud computing environment. In this scheme cluster heads forward the collected information to the BS. Consequently, BS records all the key parameters on the distributed blockchain and large data is forwarded to clouds for the storage. The revoked certificates of all malicious nodes are eliminated from blockchain by BS. The performance of the proposed scheme is scrutinized in terms of detection accuracy, certification delay, computational, and communicational overheads. The simulated results, comparative analysis and security validation supports the superiority of the proposed solution over the existing approaches.

The Industrial Internet of Things (IIoT) which introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. Those data will be analysed to improve manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal of ciphertext. Therefore, encrypted data sharing should be implemented to analyse the devices data. In this paper, the cloud storage solution for sensors is considered. To achieve secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication firstly. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.

Cloud Manufacturing (CMfg) merging concepts such as the Industry Internet of Things has been paving the road of Industry 4.0. However, as the cornerstone of CMfg, cross-domain data acquisition from heterogeneous data sources faces the issues of too many low-quality data, dumb machines and engineering knowledge loss. Hence, this paper proposed an ontology-based data acquisition for the CMfg which is built on a cloud-fog-edge architecture. Special edge node with a recognition model is designed to collect data from dump machines; fog nodes used multi-threaded fog computing are placed at the center to connect cloud servers, and machines / edge nodes; and an acquisition ontology in cloud servers is used to produce machine template and store cross-domain semantic knowledge. To validate the proposed data acquisition system, we compared vital metrics like upload times, floating point operations, and data storage requirements to a conventional data acquisition system, and have also conducted preliminary evaluations of the system in two real-word factory testbeds, which show that the FADA can save 2.9 TB of disk space per day, and reduce the server's processing time by 184.8ms per time over the conventional data acquisition system(CDAS) when 20,000 fog nodes simultaneously access the server.

This paper proposes an efficient structure-aware storage system, called GraphSO. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5, 2.1-4.3, 1.9-4.1, 1.9-2.9, 1.5-3.1, 1.3-1.5, and 1.3-2.7 times after integrating with them, respectively.

As inter-vehicle communication and automatic driving technology continue to develop, but are not yet popular, regular vehicles, connected vehicles and connected autonomous vehicles (CAVs) will coexist on the road for a long time. This mixed traffic environment highlights the need to theoretically analyze the impacts of some connected and autonomous technologies (i.e., accurate detection technology, inter-vehicle communication technology, data storage technology and inter-vehicle cooperation technology) on the stable operation of heterogeneous traffic. According to the characteristics of the vehicles equipped with different technologies, this paper extends the corresponding car-following models based on the optimal velocity model. Through these analytical models, these connected and autonomous technologies are quantified and the linear stability analyses are conducted. Numerical simulation shows that the inter-vehicle communication between three vehicles, and two previous time-step data storage or two future time-step inter-vehicle cooperation are sufficient to stabilize the mixed traffic. As CAV penetration rates increase, the stability of heterogeneous traffic is improved. Furthermore, the stability of heterogeneous traffic is weakened when the size of the largest single fleet increases. These theoretical results can serve as a quantitative tool for scholars and vehicle designers before drawing any qualitative conclusions of related technologies on heterogeneous fleet stability to avoid wasting resources such as data storage capacity and inter-vehicle communication ranges.

Cloud computing is a model that enables users to store their data remotely and enjoy the applications and services provided by this model on demand through a common set of configurable computing resources, without the burden of local data storage as well as maintenance. However, users no longer have physical possession of external data, and this makes the task of protecting data integrity in cloud computing extremely important so that users can use the cloud storage as if it were local, without worrying about the need to verify its integrity. Hence, public cloud storage auditing is critical, so users can use a Third-party auditor (TPA) to verify data integrity. In order to provide effective TPA security, the audit should not achieves any security vulnerabilities to the privacy of user data and does not provide any additional online burden to the user. In this paper, we propose an effective public auditing system for cloud data based on the signature of Boneh-Lynn-Shacham (BLS), in order to ensure public auditing and maintain data privacy. The proposed system also realizes batch auditing and data dynamic process. In addition, the proposed system enhances the level of security authentication through an Automatic Blocker Protocol (ABP) to protect the system from unauthorized TPA. An extensive security and performance analysis shows that the proposed system is very secure and effective.

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.

As a smart transportation application of the Internet of Things (IoT), the Internet of Vehicles (IoV) depresses the chances of traffic accidents, while improving transportation efficiency and user driving experience. However, as the number of vehicles continues to grow, the original ground-based IoV system is difficult to meet the ever-increasing demand. To this end, Space-Air-Ground Integrated Network (SAGIN) incorporates satellite systems, aerial network and terrestrial communications. However, because SAGIN integrates multiple network services and communication modes, which makes SAGIN more vulnerable to various types of attacks and security threats. This paper first presents the dominating security threats in data storage, transmission and sharing of space-air-ground integrated vehicular network (SAGIVN). Moreover, for guaranteeing the safety and effectiveness of the model, we advance a safe and effective encrypted data retrieval and sharing scheme in SAGIVN(ERDSS) for possible threats, the ERDSS can execute fuzzy retrieval over misspelling keywords and sort results by relevance scores to realize precise retrieval. We perform a comprehensive security discussion and execute experiments based on real-world data sets. The consequences demonstrate that the ERDSS is safe and efficient.

The multilevel storage capability of nonvolatile resistive random access memory (ReRAM) is greatly desired to accomplish high functioning memory density. In this study, Ta2O5 thin film with different thicknesses (2, 4, and 6 nm) was exploited as an appropriate interfacial barrier layer for limiting the formation of the interfacial layer between the 10 nm thick sputtering deposited resistive switching (RS) layer and Ta ohmic electrode to improve the switching cycle endurance and uniformity. Results show that lower forming voltage, narrow distribution of SET-voltages, good dc switching cycles (103), high pulse endurance (106 cycles), long retention time (104 s at room temperature and 100°C), and reliable multilevel resistance states were obtained at an appropriate thickness of ∼2 nm Ta2O5 interfacial barrier layer instead of without Ta2O5 and with ∼4 nm, and ∼6 nm Ta2O5 barrier layer, ZrO2-based memristive devices. Besides, multilevel resistance states have been scientifically investigated via modulating the compliance current (CC) and RESET-stop voltages, which displays that all of the resistance states were distinct and stayed stable without any considerable deprivation over 104 s retention time and 104 pulse endurance cycles. The I-V characteristics of RESET-stop voltage (from −1.7 to −2.3 V) of HRS are found to be a good linear fit with the Schottky equation. It can be seen that Schottky barrier height rises by increasing the stop-voltage during RESET-operation, resulting in enhancing the data storage memory window (on/off ratio). Moreover, RESET-voltage and CC control of HRS and LRS revealed the physical origin of the RS mechanism, which entails the formation and rupture of conducting nanofilaments. It is thoroughly investigated that proper optimization of the barrier layer at the ohmic interface and the switching layer is essential in memristive devices. These results demonstrate that the ZrO2-based memristive device with an optimized ∼2 nm Ta2O5 barrier layer is a promising candidate for multilevel data storage memory applications.

Encrypted deduplication combines encryption and deduplication in a seamless way to provide confidentiality guarantees for the physical data in deduplicated storage, yet it incurs substantial metadata storage overhead due to the additional storage of keys. We present a new encrypted deduplication storage system called Metadedup, which suppresses metadata storage by also applying deduplication to metadata. Its idea builds on indirection, which adds another level of metadata chunks that record metadata information. We find that metadata chunks are highly redundant in real-world workloads and hence can be effectively deduplicated. We further extend Metadedup to incorporate multiple servers via a distributed key management approach, so as to provide both fault-tolerant storage and security gaurantees. We extensively evaluate Metadedup from performance and storage efficiency perspectives. We show that Metadedup achieves high throughput in writing and restoring files, and saves the metadata storage by up to 93.94% for real-world backup workloads.

Because of the limited memory of the increasing amount of information in current wearable devices, the processing capacity of the servers in the storage system can not keep up with the speed of information growth, resulting in low load balancing, long load balancing time and data processing delay. Therefore, a data load balancing technology is applied to the massive storage systems of wearable devices in this paper. We first analyze the object-oriented load balancing method, and formally describe the dynamic load balancing issues, taking the load balancing as a mapping problem. Then, the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed. Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node. According to the load information of each data storage node collected by the scheduler in the storage system, the load weight of the current data storage node is calculated and distributed. The data load balancing of the massive storage system for wearable devices is realized. The experimental results show that the average time of load balancing using this method is 1.75 ​h, which is much lower than the traditional methods. The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time, high load balancing, strong data processing capability, short processing time and obvious application.

In recent years, traditional logistics systems are developing towards intelligence based on the Internet of Things (IoT). Sensing devices throughout the logistics network provide strong support for smart logistics. However, due to the insufficient local computing and storage resources of IoT (Internet of things) devices, logistics records with sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this study, we propose a blockchain-assisted secure storage scheme for logistics data. To be specific, this scheme can be briefly divided into two parts. The first part involves data generation and aggregation, session establishing, records encryption and storage, wherein a blockchain network is used to assist the cloud server with data storage, and smart contracts are deployed to provide reliable storage interfaces. In the second part, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process. Also, the stored records can be securely audited by leveraging the deployed blockchain network. Finally, we analyze the security and privacy properties of this scheme and evaluate its performance in terms of computation and communication overhead by developing an experimental platform. The experimental results indicate that the performance of our scheme is acceptable.

The evolution of the Industrial Internet of Things (IIoT) greatly increases the volume of data generated by the connected IIoT devices. IIoT data is playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities, and IIoT entities’ distrust of data-sharing platforms may hinder the realization of data sharing. In this paper, a decentralized IIoT data sharing scheme based on blockchain and edge computing is proposed. A proof of storage and transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMM) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

Hydrogen energy storage system (HESS) has attracted tremendous interest due to its low emissions and high storage efficiency. In this paper, the HESS is considered as an essential tool in hydrogen-integrated transportation and power systems to alleviate EV charging demand forecast error in a fast-charging station (FCS) and to solve voltage deviation problem due to the huge uptake of fast chargers on the utility grid. First, the wavelet transform (WT) method and long short-term memory (LSTM) neural network are combined to precisely predict the non-stationary traffic flow (TF). Then, a queuing theory-based model is developed to convert the predicted TF to the expected EV charging demand in FCS by considering charging service limitations and driver behaviors. Third, the charging demand prediction error is used to schedule the components in a HESS by considering their inherent properties and operational limits. As a result, the HESS configuration can be determined by analyzing the tradeoff between the investment cost and the monetary penalty due to charging demand forecast error and voltage deviation. The proposed solution is validated through a case study with mathematical justifications and simulation results.

The past decades have witnessed the tremendous success of Conventional Magnetic Recording (CMR) based Hard Disk Drives (HDDs) in data storage. To eliminate the bottleneck of CMR based HDDs in providing higher areal density, an emerging Interlaced Magnetic Recording (IMR) is capable of achieving higher areal density with limited changes to disk makeup. Nevertheless, existing approaches for IMR based HDDs may suffer serious read and write performance degradation as compared with CMR based HDDs. Thus, this paper presents a device-level solution, namely MAGIC translation layer, which aims at MAkinG IMR based HDDs perform like CMR based HDDs in terms of comparable access performance. Specifically, not merely trying to improve the performance of raw IMR based HDDs, this work, for the first time, moves one step forward to minimize the performance gap between IMR and CMR based HDDs. Technically, by 1) fully utilizing two special CMR-like potentials of IMR and 2) gracefully trading the sequential access performance as space usage increases, MAGIC minimizes track rewriting overheads to achieve CMR-like performance. Our results reveal that MAGIC not only improves the write performance compared with existing designs, but also has potential to approach read and write performance of CMR based HDD.

Hydrogen energy plays a fundamental role during the decarbonization transition of electricity systems. However, there is still a lack of a systematic framework analyzing the carbon-reduction capability of integrating a hydrogen-energy storage system (HESS) into a power grid. This research combines the grid dispatch model and storage-control model to define the metric and formulate a framework for assessing the HESS's value of reducing carbon emissions in a power system. A dynamic programming algorithm is developed to estimate the maximal carbon-reduction value of HESS, which is a nonconvex optimization problem. We apply our model to assess the carbon value of HESS in the ERCOT market and discover the trade-off between using HESS to reduce carbon and fuel costs. Using the HESS can even cause an increase in $CO_2$ emissions in ERCOT if the charging strategy is designed only to reduce the economic cost. We further identify the factors determining the carbon value of the HESS. The wind energy penetration level decisively determines how much carbon emissions the HESS can reduce. The technical characteristics of HESS also affect its carbon value.

This paper describes a technical feasibility study of on-board metal hydride storage systems. The main advantages of these systems would be that of being able to replace counterweights with the weight of the storage system and using the heat emissions of fuel cells for energy, making forklifts a perfect use case. The main challenge is designing a system that supplies the required energy for a sufficiently long period. A first draft was set up and analyzed to provide a forklift based on a fuel cell with hydrogen from HydralloyC5 or FeTiMn. The primary design parameter was the required amount of stored hydrogen, which should provide energy equal to the energy capacity of a battery in an electric vehicle. To account for highly dynamic system requirements, the reactor design was optimized such that the storage was charged in a short time. Additionally, we investigated a system in which a fixed amount of hydrogen energy was required. For this purpose, we used a validated simulation model for the design concepts of metal hydride storage systems. The model includes all relevant terms and parameters to describe processes inside the system's particular reactions and the thermal conduction due to heat exchangers. We introduce an embedded fuel cell model to calculate the demand for hydrogen for a given power level. The resulting calculations provide the required time for charging or a full charge depending on the tank's diameter and, therefore, the necessary number of tanks. We conclude that the desired hydrogen supply times are given for some of the use cases. Accordingly, the simulated results suggest that using a metal hydride system could be highly practical in forklifts.

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the (w,σ) threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional q-parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.

In this paper, we propose a hybrid storage framework WukaStore, which offers a scalable, configurable and reliable big data storage service by integrating stable storage (such as Cloud storage or durable storage utilities) and volatile storage (such as idle storage harnessed from desktop PCs over the Internet). By configuring different storage strategies, WukaStore delivers cost-effective storage service to satisfy users' requirements. We present trace-driven simulations to study the impact of different strategies and how to ensure high availability and durability in WukaStore. We present the prototype implementation of WukaStore based on BitDew, an open source middleware for Big Data management. We conduct performance evaluations to validate the effectiveness of WukaStore, through the deployment of WukaStore in the French Grid'5000 experimental platform. In particular, we also present a case study where WukaStore is deployed in a hybrid environment taking advantage of storage provided by Amazon S3, Dropbox and a local Desktop Grid at the same time. Our evaluation results show that WukaStore provides users with an alternative method to store their big data on hybrid volunteered cloud and desktop systems and to decrease the cost of data storage, while providing great data access performance, scalability as well as reliability.

Fueled by the proliferation of IoT devices and increased adoption of sensing environments the collection of spatiotemporal data has exploded in recent years. Disk based storage systems provide reliable archives but are far too slow for efficient analytics and spatiotemporal datasets quickly exceed the memory capacity of cluster environments. Current solutions focused on in-memory analytics suffer from memory contention and unnecessary network I/O, failing to provide a suitable platform for iterative, exploratory analytics in shared environments. In this work we propose Anamnesis, the first in-memory, sketch aligned, HDFS compliant storage system. Data sketching algorithms reduce dataset sizes by summarizing feature values and inter-feature relationships. Anamnesis leverages data sketches to alleviate memory contention and vastly reduce network I/O during analytics. Upon request, we generate accurate full-resolution datasets with negligible resource and time costs. Datasets are available using a fully HDFS compliant interface allowing Anamnesis to achieve unprecedented compatibility with popular analytics engines. This facilitates adoption into existing workflows by serving as a "drop-in" replacement for canonical HDFS. We evaluate the system using 2 spatiotemporal datasets, a variety of popular analytics engines, and real-world analytical operations.

Modern advanced phasor measurement units are developed with ultrahigh reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of ultrahigh-density (UHD) synchrophasor and point-on-wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This article proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an improved time-series special compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a delta-difference Huffman method is combined with the time-series special compression algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

DNAs exist in nature and could provide solutions to computing, storage and communications as existing ones approach their physical limits. Plenty of research work has been conducted on DNA-based computing, data storage and molecular communications, but largely in isolation. There is a lack of a unified place where the triples are put together to be discussed. This paper aims to filling in this gap by providing an overview of each triplet. Starting from an overall description of DNA features and their reading and writing in practical terms, the paper goes on to describe each of the three from three aspects: requirements and differences from current electronic-dominating technologies, their working principles and practical considerations. Recent advancement in each area is summarized and discussed. Furthermore, the paper intends to call for researches that go beyond the boundary of each and encourages interconnection and joint research among the three. It proposes a molecular ICT system architecture with all its three components underpinned by DNAs. The paper also identifies and discusses some new future directions such as joint coding for storage and communications, directional DNA-based molecular communications, interfaces between molecular DNA systems and electronic systems. It is hoped that this paper can spark more joint research across computing, storage and communications in this exciting field of DNA-based molecular ICT systems.

Steel is one of the most important technical resources in the world and already consists of a large proportion of recycled scrap metal. The automotive recycling industry also contributes to this material recycling or the circular economy and has so far run as a high energy process chain starting with dismantling and shredding and ending with the sorting of heavy and light fractions. Different physical principles like magnetic, mechanical, aerodynamic or optical separation are used. The high energy and logistics costs of material recycling offer potential for optimization in terms of the CO2 balance. The manufacturing chain of metallic vehicle components produces such high-quality products that questions inevitably arise about alternative component utilization such as reuse, refurbish and remanufacturing. Based on this key question, the following article is about novel technologies for exploiting steel from end-of-life vehicles. In detail, the fabrication of brake covers from roofs of end-of-life vehicles and the brazing and desoldering of structural joints in the vehicle body were explored. In addition to the technical requirements and feasibility of the innovative manufacturing technologies, the need for information and data from product manufacturing for product utilization was also investigated. The use of special RFID data storage devices that can provide utilization processes with important parameters and properties of the original product was examined. The use of RFID data storage has been identified as a necessity for future 'second-life-material' processes, especially because of its ability to increase efficiency and economy of processes.

Overcoming the drawbacks of the existing ferroelectric tunnel junction (FTJ) models which ignore the dynamic or multi-domain switching behaviors, we develop a more comprehensive FTJ model by combining the Time-Dependent Landau-Ginzburg (TDLG) equations to solve the multi-domain dynamic switching of ferroelectric layer and the Non-Equilibrium Green Function (NEGF) to solve the tunneling current. The model successfully reproduces the experimental results of our fabricated metal-ferroelectrics-insulator-semiconductor (MFIS) FTJ. This model empowers us to predict both the dynamic and multi-state switching of FTJ, showing its promise for applications in the high-density data storage and analog computing.

Recent research findings indicate that the non-monotonic consumption of energy from lithium-ion (Li-ion) batteries results in a higher heat generation in electrical energy storage systems. During peak demands, a higher heat generation due to high discharging current increases the temperature from 80 °C to 120 °C, thereby resulting in thermal runaway. To address peak demands, an additional electrical energy storage component, namely supercapacitor (SC), is being investigated by various research groups. This paper provides insights into the capability of SCs in lightweight electric vehicles (EVs) to address peak demands using the worldwide harmonized light-duty driving test cycle (WLTC) driving profile in MATLAB/Simulink at different ambient temperatures. Simulation results indicate that temperature imposes a more prominent effect on Li-ion batteries compared with SCs under peak demand conditions. The effect of the discharging rate limit on the Li-ion battery current is studied. The result shows that SCs can accommodate the peak demands for a low discharging current limit on the battery, thereby reducing heat generation. Electrochemical impedance spectroscopy and cyclic voltammetry are performed on SCs to analyze their thermal performance at different temperatures ranging from 0 °C to 75 °C under different bias values of −0.6, 0, 0.6, and 1 V, respectively. The results indicate a higher specific capacitance of the SC at an optimum operation temperature of 25 °C for the studied bias. This study shows that the hybrid combination of the Li-ion battery and SC for a light-weight EV can address peak demands by reducing thermal stress on the Li-ion battery and increasing the driving range.

Ferroelectric tunnel junctions (FTJs) as the artificial synaptic devices have been considered promising for constructing brain-inspired neuromorphic computing systems. However, the memristive synapses based on the flexible FTJs have been rarely studied. Here, we report a flexible FTJ memristor grown on a mica substrate, which consists of an ultrathin ferroelectric barrier of BiFeO3, a semiconducting layer of ZnO, and an electrode of SrRuO3. The obtained flexible FTJ memristor exhibits stable voltage-tuned multi-states, and the resistive switchings are robust after 103 bending cycles. The capability of the FTJ as a flexible synaptic device is demonstrated by the functionality of the spike-timing-dependent plasticity with bending, and the accurate conductance manipulation with small nonlinearity (−0.24) and low cycle-to-cycle variation (1.77%) is also realized. Especially, artificial neural network simulations based on experimental device behaviors reveal that the high recognition accuracies up to 92.8% and 86.2% are obtained for handwritten digits and images, respectively, which are close to the performances for ideal memristors. This work highlights the potential applications of FTJ as flexible electronics for data storage and processing.

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is important in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware encrypted deduplication scheme is vulnerable to the stub-reserved attack. Furthermore, we propose a secure data deduplication scheme with efficient re-encryption based on the convergent all-or-nothing-transform (CAONT) and randomly sampled bits from the Bloom filter. Due to the property of hash function, our scheme can resist the stub-reserved attack and guarantee the data privacy of data owners' sensitive data. Moreover, instead of re-encrypting the entire package, data owners are only required to re-encrypt a small part of it through the CAONT, thereby effectively reducing the computation overhead. Finally, security analysis and experimental results show that our scheme is secure and efficient in re-encryption.

The world today is continuously tending toward clean energy technologies. Renewable energy sources are receiving more and more attention. Furthermore, there is an increasing interest in the development of energy storage systems which meet some specific design requirements such as structural rigidity, cost effectiveness, life-cycle impact, and increased energy capacity. Gravity energy storage (GES) is one of those innovative storage technologies that is still under development. Hence, this study proposes a new methodology which aims to optimally design and deploy a large-scale GES system in a hybrid PV-Wind plant to make it more competitive technically and economically. The objective of this study is to minimize GES construction cost restricted by handling mechanical load applied on the system's structure. The sizing methodology is based on genetic optimization algorithm which aims to determine the optimum dimensions of GES components. A case study has been used to verify the effectiveness of the proposed model. The findings of this study have shown that the optimal system's height is about 48 m with a diameter of 24 m and a wall thickness of 3m. The construction cost of the system reaches 6.7 M€ with the piston representing the largest cost share. The cost of electricity was found equals to 0.19 €/kWh. In addition, this work investigates the integration of GES with a hybrid wind-solar grid-connected power plant to properly dispatch renewable power by incorporating an efficient storage strategy preventing overcharging/discharging of GES. The findings demonstrate the ability of this new storage system to balance the energy supply and demand. A comparison between the obtained results with that of a battery energy storage has shown that GES performs better due to its high DOD and lifetime, as well as its good efficiency.

Popular data concentration is a widely accepted storage energy-saving technique which places frequently-accessed data on a small subset of hard disks and spins-down other infrequently-accessed disks. Many previous studies use intuitive heuristic algorithms for data placement that promote the imbalance in the access frequencies across hard disks. However, the relevance and the optimality of such file placements have not been rigorously investigated. In this paper, we formally define the energy-saving file placement problem under the capacity and performance constraints as a combinatorial optimization problem and show the theory of the optimal file placement where the file access rates in the next period are given. Our analysis based on a stochastic process of disk state transitions gives the theoretical support for the common heuristic placement method. To examine the effectiveness of the optimal file placement, we experimentally evaluate the energy-efficiency of a test storage system using the file access rates generated from the real access traces from Flickr. The experimental results show that the energy consumption can be reduced by 31.8% with the optimal file placement compared to the evenly distributed file placement. We also conduct simulation experiments to confirm the energy-saving impacts in larger-scale storage systems.

This paper studies prediction-based energy management for onboard hybrid energy storage system (HESS), combining engine-generator, battery, and ultracapacitor. Each of these energy sources has a specific utility function to represent its unique preference. Thus, a game-theoretic strategy is presented to model the different preferences of these energy sources and their interactions, and hence to properly dispatch the power load demand among them. To further improve this power dispatch, i.e., the energy management, that may be influenced by the fluctuation of the uncertain power load demand, a prediction is included in the basic game-theoretic strategy to form a prediction-based game-theoretic strategy. Since the power load demand can be derived from the velocity in HESS, the velocity prediction is implemented by a long short-term memory network. Improvement in the accuracy of this prediction is achieved by utilizing feature extraction and time-series analysis. A multiple time-series method is newly applied to group the input features according to the target prediction horizon. The solution, Nash equilibrium, of this proposed prediction-based game-theoretic management strategy is reached based on the best response functions of the energy sources and its performance is quantified by four criteria. Both simulation and experiment results demonstrate the efficiency of proposed strategy.

We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.

A web-based futsal field reservation system was developed to help facilitate the process of booking or leasing a futsal field. The system uses PHP and HTML programming languages by using MySQL as data storage. The purpose of designing this system is to make it easier for users to rent futsal fields. With the futsal field reservation system is expected to facilitate in data processing, scheduling and registration of members or customers. In the end, this system is expected to help the company's performance, and can also facilitate customers in the ordering process.

By considering on-site hydrogen refueling stations, investigations are still needed on how the integration between electrolysis units and compressors can be better matched by tuning the operating parameters, such as temperature and pressure, and how it could affect the energy consumption and the overall efficiency. This paper presents a comprehensive characterization of a hydrogen production facility, composed of a water-based electrolysis unit, a volumetric compressor, and a storage system, up to 350 bar. Their operation is investigated through a sensitivity analysis: pressure and temperature are used as parametric inputs to forecast the different energy performance of the system in terms of voltage efficiency, stack efficiency, cell voltage, and system efficiency, as well as the influence of the over-voltages. An economic analysis is also presented, to compare the benefits of a high-pressure electrolyzer (up to 30 bar), showing that its installation can lead to important savings, up to 670 k€ of energy saved by a less demanding compression process in 20 years for an electrolysis capacity of 1.5 ton/day.

The savings and loans cooperative are a cooperative that has an important purpose in its reach and services, such as the Permata Ngijo Savings and Loans Cooperative. Activities within the cooperative itself include managing member data as well as savings and loan transactions. In systems that are still manual, such as Microsoft Excel, there is a risk of inaccurate data and takes a long time. This manual system is still less effective because the number of members and transactions continues to grow. Therefore, an application is needed as a more effective data processing and information system for members that is easier to access. This study aims to build an android-based savings and loan application using the hypertext preprocessor (PHP) programming language and my structured query language (MySQL) as data storage. Based on the android application that has been made, it shows that the black box functionality of the application runs as planned, the success rate for users reaches 90%.

Energy storage systems in recent days are witnessing an increased trajectory of hybridization to decrease the burden on the single energy storage systems in renewable energy sources. The hybridization of energy storage imposes the need for an efficient power-sharing strategy. This article proposes the interval type2 fuzzy logic controller-based power-sharing strategy to utilize hybrid energy storages in the solar-powered charging station effectively. Under this study, the hybrid energy storage system consists of a battery and supercapacitor with solar power generation as a primary source. The battery supports the slow varying power and the supercapacitor supports the fast-varying power. Electric vehicles are connected as load and a multi-step constant current charging technique is proposed to charge its battery. In order to inject/absorb the power, the hybrid energy storage utilizes bidirectional DC-DC converters. The perturb and observe algorithm is used to track the maximum power from the solar panel. The proposed system is designed in MATLAB/Simulink platform and the real-time validation has been carried out through the dSPACE (DS1202) real-time processor. An obtained result shows the performance of the interval type2 fuzzy logic controller-based power-sharing strategy under variable generation and load scenarios.

A multistate data memory device can store more than one bit in each cell. Such devices have broad application prospects because they provide a simple and low-cost way to improve memory capacity. As a biomaterial, silkworm hemolymph (SH) has the advantages of being easily accessible, biodegradable and biocompatible. In this study, performance-tunable multistate data storage devices with an indium tin oxide (ITO)/graphene oxide (GO)/SH/GO/Al structure were fabricated with SH and GO as dielectric layers. The results show that the proposed device structure has stable nonvolatile and bipolar switching characteristics. The conductivity and ON/OFF current ratio (1–103) of such a device can be adjusted by modifying the GO concentration. With appropriate control of the compliance current (ICC), the device can be switched to six different resistance states, enabling multistate data storage capabilities and allowing high-density data storage to be realized. These devices have a long retention time (104 s) and extended endurance. Their switching behavior is attributed to the formation and fracture of oxygen vacancy conductive filaments. This research introduces a new approach to the development of biocompatible high-density memory devices.

The voltage equalizers based on the switched capacitor are widely used in energy storage system because of its simple circuit structure and easy realization. However, the voltage stress and the number of bypass capacitors in traditional switched-capacitor equalizers are enormous due to the large number of energy storage cells, and they can only realize balance of a single string. Therefore, three improved switched-capacitor equalizers based on reutilization technique are proposed to solve the problems. The supercapacitors in the circuit can be used as energy storage cells and energy transfer cells through the reutilization of energy, thus avoiding the extensive use of bypass capacitors and achieving the balance between strings. Meanwhile, the design difficulty of equivalent equalization resistance and the switch number are greatly reduced. The circuit configuration, operation principle, and modeling are illustrated in detail. Finally, three experiment prototypes are established. The results verify the feasibility of the proposed topologies.

Magnetic skyrmions are particle-like, nanometer-sized topological spin textures observed in several magnetic materials. They have emerged as an alternative to conventional spintronic memories and domain walls (DWs) and offer high storage density, more robust stability, low critical currents, and increased scalability. Recent advances have set the stage for their use in quantum computing, logic circuits, and neuromorphic computing. With the aid of electrical methods, it is possible to precisely create, manipulate, and destroy skyrmions in device-compatible materials. However, the maximum speed achievable by magnetic skyrmions and the reliable detection of data have been restricted by the skyrmion Hall effect (SkHE). Other issues include a low read margin and a lack of proper skyrmion motion control in nanowires. Most of these can be addressed by exploiting novel materials, such as antiferromagnets; employing specialized fabrication techniques; tuning driving current profiles; and circuit-level engineering. In this article, theoretical and experimental breakthroughs and challenges relevant to magnetic skyrmions and their applications in data storage, logic computing, and neuromorphic computing are highlighted.

In this research paper, a Redundant Residue Number System (n,k) code is introduced to enhance Cloud ERP Data storage. The research findings have been able to demonstrate the application of Redundant Residue Number System (RRNS) in the concept of Cloud ERP Data storage. The scheme contributed in addressing data loss challenges during data transmission. The proposed scheme also addressed and improved the probability of failure to access data compared to other existing systems. The proposed scheme adopted the concept of Homomorphic encryption and secret sharing whiles applying Redundant Residue Number System to detect and correct errors.The moduli set used is {2m, 2m + 1, 2m+1 - 1, 2m+1 + 1, 2m+1 + k, 22m - k, 22m + 1} where k is the number of the information moduli set used. The information moduli set is {2m, 2m + 1, 2m+1 - 1} and the redundant moduli is {2m+1 + 1, 2m+1 + k, 22m - k, 22m + 1}. The proposed scheme per the simulation results using python reveals that it performs far better in terms of data loss and failure to access data related concerns. The proposed scheme performed better between 41.2% for data loss to about 99% for data access based on the combination of (2, 4) and (2, 5) data shares respectively in a (k, n) settings.