Storage Usage Research Articles

Jump Linear Quadratic Control for Microgrids with Commercial Loads

Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of μGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of μGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models μGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach.

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

Objectives: To achieve improved performance of FP-Growth based Association Rule Mining algorithm for massive data by effective utilization of storage,execution capability and improved partition technique within the Hadoop MapReduce framework. Methodology: The proposed methodology has four main phases: In the first phase, the item sets for finding the frequent pattern are encoded and thus minimizes the expensive operation for large data set. In the second phase, improved hash partitioning reduces the network overhead and improves the communication speed within the MapReduce phase for each item set. The effective usage of network bandwidth and storage is obtained by the impact of compression in the third phase. The use of combiner in final phase for frequent item set mining minimizes the overhead of reduce phase by finding the pattern in each partition and minimizes the overall execution time of the FP-Growth algorithm. Findings: FP-Growth based association rule mining algorithm is designed for parallel execution on distributed cluster of servers. Changes to the MapReduce implementation of FP-Growth with the impact of encoding. Improved hash partitioning, compression and configuration results in a significant performance gain with better improvement in execution time.Novelty/Improvements: According to the experimental results, the changes in storage and processing level within the MapReduce framework improves the overall performance of the parallel frequent item set mining in Hadoop cluster. Keywords: Association rule mining; Hadoop; MapReduce; FP-Growth

Detecting personally identifiable information transmission in android applications using light-weight static analysis

This convenience of mobile devices has driven significant growth in the volume of personal information users store on their devices as well as everyday mobile application usage. However, users are becoming increasingly aware of the access these applications have to their personal information and the risk that applications may transmit Personally Identifiable Information (PII) to external servers, sometimes unknowingly to users. There is no easy way to know whether or not an application transmits PII. If this information could be made available to users as early as when they are browsing application markets looking for new applications to install on their devices, they can weigh the pros and cons to make an informed decision on the associated risk of their private information potentially being exposed. Previously, detection of PII transmission has been tackled using heavy-weight techniques such as static code analysis and dynamic behavior analysis requiring from several minutes to hours of testing and analysis per application. In constrast, we propose using light-weight methods to extract features that we then use to develop a classification model to detect PII transmission in under a minute with performance that rivals the heavy-weight techniques. We evaluate our model using an extensive set of more than 8700 top-ranked Android applications. Our approach is precise and fast, making it suitable for real-time detection and analysis of PII transmission in mobile applications.

Multi-Resource Allocation of Shared Energy Storage: A Distributed Combinatorial Auction Approach

This paper proposes a combinatorial auction approach for multi-resource allocation of an energy storage (ES) shared by multiple electricity end users in a residential community. Through the auction, a user buys a group of ES resources, including capacity, energy, charging power, and discharging power, from the ES operator. With the ES resources, users store grid energy during low-electricity-price hours, so that they can consume the cheap stored energy during high-electricity-price hours to reduce their electricity bills. In the auction, users submit their resource demands and corresponding bid prices, based on which the ES operator determines the winners and the final payments that the winners must pay. To solve the NP-hard winner determination problem, a fully polynomial time approximation scheme (FPTAS) is developed, which can optimize social welfare but may violate resource supply constraints. To deal with the constraint violation, the ES operator may buy extra energy outside the system to meet the winners’ actual demands. Further, a distributed implementation of the auction is designed to offload the auction computation onto the users while preventing the users from manipulating the auction outcomes in the course of computation. The proposed distributed auction can ensure that all users faithfully complete the assigned computation tasks in an ex-post Nash equilibrium. A real time-of-use (TOU) electricity tariff and actual home load data are used in the simulation, in which the proposed auction approach is evaluated in terms of social welfare and computational efficiency.

Multiple Quantization Parameter Optimization in Versatile Video Coding for 360° Videos

Consumer Electronics (CE) devices have started to support 360° videos in order to improve the perceived video quality by consumers. Higher compression in 360° videos not only lead to improved consumer perception, but also efficient energy and storage usage in CE devices. Consequently, efficient compression of such video content is becoming a critical design consideration of CE devices. To this end, this paper first proposes a residual weighting algorithm that utilizes the features of Weighted Craster Parabolic Projection Peak Signal-to-Noise Ratio (WCPPPSNR), to instigate the first stage of magnitude reduction of the residuals. The model parameters associated with the residual weighting algorithm are determined in the initial experimental stage. Next, the paper proposes a Quantization Parameter (QP) optimization technique which introduces the second stage of magnitude reduction for residuals with minimal impact on the overall video quality. The proposed method is tested on the Versatile Video Coding (VVC) test model with All-Intra configuration. In this context, the empirical results demonstrate that the proposed method improves the coding efficiency by 3.34% on average and outperforms the state-of-the-art techniques.

Security of Data` in Cloud Computing Based on Advanced Secret Sharing Key Management Scheme

Cloud Computing (CC) is a worldwide concept and there are no boundaries define with in the cloud computer which is used to access and store user data can be located anywhere on the global network, depending on where the capacities that are required are available in the global computer networks used for cloud computing. Because of the efficient features of (CC) many Organization are using cloud storage for storing their essential information. The data can be stored remotely in the cloud computing environment by the user and can be retrieved by the client at the time of need. Data security is the major concern in cloud computing. Data Storage is risky because of use of internet by cloud based services which means less control over the store data. Essential elements of cloud is how do we grab all the benefit of the cloud while maintain security controls over the organization assets. Our aim is to propose a more reliable, decentralized light weight key-management scheme (KMS) techniques for cloud system which provide more efficient data security.

Delivery phase in cache-based wireless networks with modified LT codes

Caching has emerged as an efficient technique to reduce delivery latency and network congestion. The focus of this paper is on content delivery in the caching-based wireless systems. In view of the fact that in such systems, users store fractional of popular contents during off-peak hours, we propose a modified version of the Luby Transform (MLT) encoding for the delivery phase to take the advantage of channel coding. The novelty of our proposed MLT encoding lies in employing the user’s partial information to create an appropriate degree distribution. To evaluate our proposed scheme, this study considers a realistic channel model inspired by the Gilbert-Elliott channel including the erasure mode and a binary symmetric channel model. The effectiveness of our MLT encoding is analyzed by the average degree and the number of encoding symbol. A comprehensive simulation evaluation is performed which shows that MLT encoding outperforms conventional LT coding in terms of the required number of encoding symbols and demonstrates a better performance on the recovered symbols. In particular, the MLT encoding, compared to the conventional LT encoding, reduces the amount of required symbols to recover the original file for at least 20%, when the user stored more than half a file.

Assessing the value of natural gas underground storage in the Brazilian system via stochastic dual dynamic programming

The Brazilian natural gas sector is currently characterized by low maturity and dynamism of the market. The stochastic behavior of the demand for natural gas added to its associated market price volatility motivates the usage of underground storage to provide supply flexibility and protection against price fluctuations. However, the existing literature lacks a proper analytical tool to assess the benefits of underground natural gas storage (UNGS) activity. In this work, it is proposed a stochastic dynamic programming model for long/medium-term operation planning to determine the optimal gas supply and storage policies. A markovian model characterizes the uncertainty over the thermoelectric demand and market price. The proposed model is efficiently solved using the stochastic dual dynamic programming algorithm for the Brazilian case study considering realistic data for the actual gas network and electric power system. For an exogenous but meaningful choice of underground storage location and size, it is observed the operational and economic benefits of the provided storage flexibility. Finally, our numerical simulations show that the economic benefit for the system surpasses the operational and capital expenses for the storage infrastructure in depleted fields and salt caverns.

Improving a fuzzy neural network for predicting storage usage and calculating customer value

Predicting the behavior of customers plays a crucial role in the quality of resource management and customer services. In this article, a fuzzy neural network model for predicting the customer storage usage is identified. The identified fuzzy neural network is improved and finally the result of the improved fuzzy neural network is compared with some other fuzzy neural network and other prediction methods.

Efficient logging and querying for blockchain-based cross-site genomic dataset access audit

BackgroundGenomic data have been collected by different institutions and companies and need to be shared for broader use. In a cross-site genomic data sharing system, a secure and transparent access control audit module plays an essential role in ensuring the accountability. A centralized access log audit system is vulnerable to the single point of attack and also lack transparency since the log could be tampered by a malicious system administrator or internal adversaries. Several studies have proposed blockchain-based access audit to solve this problem but without considering the efficiency of the audit queries. The 2018 iDASH competition first track provides us with an opportunity to design efficient logging and querying system for cross-site genomic dataset access audit. We designed a blockchain-based log system which can provide a light-weight and widely compatible module for existing blockchain platforms. The submitted solution won the third place of the competition. In this paper, we report the technical details in our system.MethodsWe present two methods: baseline method and enhanced method. We started with the baseline method and then adjusted our implementation based on the competition evaluation criteria and characteristics of the log system. To overcome obstacles of indexing on the immutable Blockchain system, we designed a hierarchical timestamp structure which supports efficient range queries on the timestamp field.ResultsWe implemented our methods in Python3, tested the scalability, and compared the performance using the test data supplied by competition organizer. We successfully boosted the log retrieval speed for complex AND queries that contain multiple predicates. For the range query, we boosted the speed for at least one order of magnitude. The storage usage is reduced by 25%.ConclusionWe demonstrate that Blockchain can be used to build a time and space efficient log and query genomic dataset audit trail. Therefore, it provides a promising solution for sharing genomic data with accountability requirement across multiple sites.

Design of a Near Field Communication-Enabled Points Collection Application System

Nowadays, users collect points by stickers after they purchase products. However, to issue stickers and to exchange points are inconvenient to store staffs and users. In addition, the traditional way for points collection increases the business cost because of additional cards and stickers. In this paper, we propose a novel, practical and integrated application system to resolve above issues namely NFC-enabled Point Collection Application System (NPCAS). NPCAS mainly consists of three subsystems including user-side APP, store-side APP and backend management system. The user-side APP let users to quickly complete points management, for instance, they can accumulate points by communicating with the store-side APP through Near Field Communication (NFC) technique. The store-side APP calculates the points of each transaction and updates them to the backend management system. Then, the users can inquire their current points, exchange details, exchangeable commodities and so on. Android operating system is the mainstream on smart phones so that the Android-based APPs attract a lot of attention. Accordingly, we develop both APPs based on Java, XML, Android Software Development Kit (SDK) and Android Studio. The backend management system is built by Apache server, PHP module, PHP files and MySQL database. The format that we used on information exchange between backend management system and both APPs is JavaScript Object Notation (JSON). In a word, NPCAS greatly simplifies the procedures of points collection and management.

A Novel Android Memory Forensics for Discovering Remnant Data

As recently updated on the vulnerability statistics shown in 2019, Android-driven smartphones, tablet PCs, and other Android devices are vulnerable, whether from internal or external threats. Most users store sensitive data like emails, photos, cloud storage access, and contact lists on Android smartphones. This information holds a growing-importance for the digital investigation process of mobile devices, e.g., internal memory or random-access memory (RAM) forensics, or external memory or read-only memory (ROM) forensics on Android smartphones. Internal memory retrieval is considered flawed and difficult by some researchers as it alters the digital evidence in an intrusive way. On the other hand, external memory retrieval also called logical acquisition that implies the image of logical storage items (e.g., files, database, directories, etc.) that locate on logical storage. This research provides a novel methodology that focuses only on internal memory forensic in a forensically sound manner. This research also contributes two algorithms, e.g., collect raw information (CRI) for parsing the raw data, and investigate raw information (IRI) for extracting the digital evidence to be more readable. This research conducted with fourteenth events to be analyzed, and each event was captured by SHA-1 as digital evidence. By using GDrive as the case study, the authors concluded that the proposed methodology could be used as guidance by forensics analyst(s), cyberlaw practitioner(s), and expert witness(es) in the court.

Fine-grained assured data deletion scheme based on attribute association

ABSTRACT With the rapid development of cloud computing technology, an increasing number of enterprises and users store their data in the cloud to achieve convenient storage and data sharing. However, once data is stored in a third-party cloud storage service provider, the privacy and integrity of the data can be compromised. This paper proposes a fine-grained assured data deletion scheme based on attribute association (ADAA), which aims to protect security and achieve assured deletion of outsourced data. The scheme uses standard encryption technology to ensure the privacy and integrity of the data and implements secure deletion based on attribute revocation. At the same time, it incorporates access policy graphs and policy combinations to achieve fine-grained access control and data sharing. In addition, it associates attributes to achieve access control by using a homomorphic hash function, and it establishes a deletion confirmation feedback mechanism to provide a reliable guarantee. By experimenting and comparing with related schemes, ADAA is proved to be safe and efficient, and it provides a practical method for the assured deletion of cloud storage data.

An Adaptive Data Placement Architecture in Multicloud Environments

Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.

헬스 & 뷰티스토어 이용자 태도의 인구통계학적 차이에 관한 연구

헬스 & 뷰티스토어 이용자 태도의 인구통계학적 차이에 관한 연구

Community-Based Recommendations to Improve Customer Turnout at a Non-Profit Grocery Store

Abstract Objectives Aimed at improving healthy food access in a low-income neighborhood, the Salvation Army's first non-profit grocery store in Baltimore City has so far failed to attract a large customer base. The store averages just 140 customers daily and serves 500 unique families weekly, with an average transaction of $14. This study explored reasons for low usage from the perspective of community members and suggests solutions. Methods Mixed-methods formative research was conducted at DMG Foods. In-depth interviews were conducted with store staff (n = 5), wholesalers (n = 3), local store non-users (n = 4), and store users (n = 9).Interviews focused on purchasing habits, observations, prices, marketing efforts, quality of food, location, and convenience. Direct observations of store operations (n = 8) and a short demographic survey of store users with loyalty cards (n = 119) and without loyalty cards (n = 33) was also completed. All in-depth interviews were coded and analyzed using ATLAS.ti v8.4.18. Results The survey results indicated that the average amount spent at DMG Foods per loyalty customer within the last 30 days was $82. Only 11% survey respondents reported using DMG for all their grocery shopping. Reasons given for low store usage, included: (a) prices at DMG Foods are not low; (b) Non-users do not perceive DMG Foods as a food store; and (c) food quality concerns. In light of these responses, we made several recommendations, including: (a) make displays showing price differences between DMG Foods and other competitors; (b) place rebranded signs outside the store to indicate that DMG Foods is a grocery store; and (c) improve and maintain the quality of produce and meat in store. Additionally, store management should negotiate with wholesalers and distributors to obtain competitive wholesale prices. Conclusions The results of this study will be used to inform DMG Foods and to help Salvation Army refine their non-profit grocery model as they expand it to other locations in Baltimore and across the United States. Funding Sources Johns Hopkins University Bloomberg School of Public Health Department of International Health.

JupyterLab at the Saint Petersburg State University

This paper describes our experience using and extending JupyterLab. We started with a copy of CERN SWAN environment, but now our project evolves independently. A major difference is that we switched from classic Jupyter Notebook to JupyterLab, because our users are more insterested in text editor plus terminal workflow rather than in Notebook workflow. However, like in SWAN, we are still using CVMFS to load Jupyter kernels and other software packages, and EOS to store user home directories.

Secure Data Storage in Cloud System Using Modern Cryptography

Cloud computing is the movement of enlisting computing services like organization servers, accumulating databases, arranging, programming examination over the Internet. Companies offering these computing services are known as cloud providers and typically charge for cloud computing services based on usage, similar to how you are billed for water or electricity at home. It enables the companies to consume a compute resource, such as a Virtual Machine (VM), storage or an application, as a utility—just like electricity—rather than building and maintaining large computing materials in the house. Though servers are greatly protected against unauthorized access, there are incidents where classified data stored on servers are accessed by the maintenance staffs. So, the security plays a major role in cloud storage as when the user stores the data in the cloud, it stays there and anybody accessing it cannot be known at all. Hence, this paper mainly deals with the idea of storing data securely in the cloud using Symmetric and Asymmetric Cryptography algorithms including AES, 3DES, Blowfish, RSA along with modern Steganography LSB algorithm for wireless communication which hides the key inside the cover image.

Design and Implementation of Virtual Private Storage Framework Using Internet of Things Local Networks

This paper presents a virtual private storage framework (VPSF) using Internet of Things (IoT) local networks. The VPSF uses the extra storage space of sensor devices in an IoT local network to store users’ private data, while guaranteeing expected network lifetime, by partitioning the storage space of a sensor device into data and system volumes and, if necessary, logically integrating the extra data volumes of the multiple sensor devices to virtually build a single storage space. When user data need to be stored, the VPSF gateway divides the original data into several blocks and selects the sensor devices in which the blocks will be stored based on their residual energy. The blocks are transmitted to the selected devices using the modified speedy block-wise transfer (BlockS) option of the constrained application protocol (CoAP), which reduces communication overhead by retransmitting lost blocks without a retransmission request message. To verify the feasibility of the VPSF, an experimental implementation was conducted using the open-source software libcoap. The results demonstrate that the VPSF is an energy-efficient solution for virtual private storage because it averages the residual energy amounts for sensor devices within an IoT local network and reduces their communication overhead.

A Security Transmission and Storage Solution about Sensing Image for Blockchain in the Internet of Things.

With the rapid development of the Internet of Things (IoT), the number of IoT devices has increased exponentially. Therefore, we have put forward higher security requirements for the management, transmission, and storage of massive IoT data. However, during the transmission process of IoT data, security issues, such as data theft and forgery, are prone to occur. In addition, most existing data storage solutions are centralized, i.e., data are stored and maintained by a centralized server. Once the server is maliciously attacked, the security of IoT data will be greatly threatened. In view of the above-mentioned security issues, a security transmission and storage solution is proposed about sensing image for blockchain in the IoT. Firstly, this solution intelligently senses user image information, and divides these sensed data into intelligent blocks. Secondly, different blocks of data are encrypted and transmitted securely through intelligent encryption algorithms. Finally, signature verification and storage are performed through an intelligent verification algorithm. Compared with the traditional IoT data transmission and centralized storage solution, our solution combines the IoT with the blockchain, making use of the advantages of blockchain decentralization, high reliability, and low cost to transfer and store users image information securely. Security analysis proves that the solution can resist theft attacks and ensure the security of user image information during transmission and storage.