A Verifiable Secure Sharing and Dynamic Auditing Method for Ownership Transfer of Cloud Data

Effective data management across large-scale shared-use materials-science facility centers is crucial, as high-end facilities available at such centers are indispensable for cutting-edge research in modern materials science. However, establishing network connectivity for these facilities is often not practically feasible due to its uniqueness, forcing users to resort to the traditional manual method of data acquisition and transfer, such as the use of DVD disks. In this paper, we introduce an IoT device designed to facilitate direct data transfer from these non-networked and shared-use facilities to a cloud system. We propose a novel data transfer algorithm that integrates user management with data transfer processes for safely assigning each user's data to its private storage space on the cloud. The proposed technology serves as a fundamental building block in shaping a smarter laboratory information management system for the advancement of material researches and developments.

SummaryThis paper deals with data transfer between two meshes as it happens in a finite element context when a remeshing has to be performed. We propose a finite‐volume‐based data transfer method for an efficient remeshing of three‐dimensional solid mechanics problems. The originality of this transfer method stems from a linear reconstruction of the fields to be transferred on an auxiliary finite volume mesh, a fast computation of the transfer operator and the application to the complete remeshing of 3D problems. This procedure is applicable to both nodal values and discrete fields defined at quadrature points. In addition, a data transfer method using mortar elements is presented. The main improvement made to this second method comes from a fast computation of mortar elements. These two data transfer methods are compared with the simplest transfer method, which consists of a classical interpolation. After some academic examples, we present 2D forging and 3D friction stir welding applications. Copyright © 2014 John Wiley & Sons, Ltd.

Data transfer is an indispensable step that is widely involved in the maintenance and processing of Cloud data. Due to rapid growth in Cloud data, methods of reducing the huge energy consumption of data transfer in the Cloud have become a challenge. In this paper, we propose a novel energy-efficient data transfer strategy called link rate controlled data transfer LRCDT. By scheduling bandwidth in a link rate controlled fashion, LRCDT intends to reduce the energy consumption specifically for data transfer that does not require the maximum transfer speed, which is referred to as 'lazy' data transfer, so to achieve the energy-efficient data transfer goal in the overall term. The result in our simulation indicates that LRCDT is able to reduce energy consumption by up to 63% when compared to existing data transfer strategies.

In designing query processing primitives, a crucial design choice is the method for data transfer between two operators in a query plan. As we were considering this critical design mechanism for an in-memory database system that we are building, we quickly realized that (surprisingly) there isn't a clear definition of this concept. Papers are full of ad hoc use of terms like pipelining and blocking, but these terms are not crisply defined, making it hard to fully understand the results attributed to these concepts. To address this limitation, we introduce a clear terminology for how to think about data transfer between operators in a query pipeline. We argue that there isn't a clear definition of pipelining and blocking, and that there is a full spectrum of techniques based on a simple concept called unit-of-transfer. Next, we develop an analytical model for inter-operator communication, and highlight the key parameters that impact performance (for in-memory database settings). Armed with this model, we then apply it to the system we are designing and highlight the insights that we gathered from this exercise. We find that the gap between the traditional “pipelining” and “non-pipelining” methods of query processing, w.r.t. key factors such as performance and memory footprint is quite narrow, and thus system designers should likely rethink the notion of “pipelining” vs. “blocking” for in-memory database systems.

O.382 Mandibular prognathism: how and why to avoid mandibular setback

ABSTRACT: As cloud computing continues to play a pivotal role in data storage and processing, ensuring the security and integrity of transferred data remains a critical concern. This project introduces a novel approach for secure data transfer and detection leveraging the Counting Bloom Filter in cloud computing environments. The proposed system addresses the vulnerabilities associated with traditional data transfer methods by incorporating the Counting Bloom Filter, a probabilistic data structure. This technology aids in optimizing storage efficiency and enhances the accuracy of data detection, crucial for maintaining data integrity during transmission. The project focuses on these cure transfer of sensitive information within cloud computing environments, providing an added layer of protection against unauthorized access and potential data corruption. The Counting Bloom Filter is employed to detect anomalies and ensure the integrity of the transferred data, contributing to a robust and reliable cloud-based data transfer mechanism. Key objectives of the project include the implementation of advanced encryption techniques for secure data transfer, the integration of the Counting Bloom Filter for efficient data detection, and the development of a comprehensive system ensuring data integrity in the cloud. The project's innovation lies in its ability to enhance the security and reliability of data transfer in cloud computing, addressing the evolving challenges associated with data protection and privacy. In conclusion, the Secure Data Transfer and Detection from Counting Bloom Filter in Cloud Computing project represents a significant advancement in the realm of secure data transmission. By combining encryption methods with the unique capabilities of the Counting Bloom Filter, the project offers an effective solution for securing data during transfer, ensuring the integrity of information in cloud computing environments. Keywords:- Cloud computing, Data storage, Data processing, Data transfer, Security, Integrity, Counting Bloom Filter, Probabilistic, data structure, Storage efficiency, Data detection, Sensitive information, Encryption techniques, Unauthorized access, Data corruption, Anomaly detection, Data protection, Privacy, Secure data transfer, Cloud-based data transfer, System development, Data transmission.

With the tremendous development of remote sensing technologies, large-scale data sets are collected from environmental and meteorological sensors. Edge computing for large-scale data sets is hard to be conducted in real time on the sensors, thus data transfer to cloud system and data processing play an important role. One of the barriers in such system is high-speed data transfer on long-distance networks. Even low packet loss condition leads to decrease in throughput by conventional transmission control protocol (TCP)-based tools. In this paper, we overcome this issue by proposing a high-performance data transfer tool to improve real-time data processing of phased array weather radar on long-distance networks. The proposed tool, called high-performance copy (hcp), is designed based on an open source tool for file copy operations and implemented using our socket library specialized for long-distance networks. We evaluate our tool with simulations and real data transfer scenarios. The results show that the hcp enables concurrent data transfer from radar sites to cloud system (data processing site) in real time. The throughput of data transfer from a radar site to a data processing site increases from 40 Mbps using the conventional TCP-based tool to 489.2 Mbps using the hcp.

Aiming toward a compact high- throughput reconfigurable architecture, we propose the reconfigurable processor DS-HIE. In order to achieve the characteristics of compactness and high-throughput, the DS-HIE architecture executes operations following a bit-serial computation scheme and adopts a Beneš network as its routing resource. Implementing bit-serial computation brings the advantage of small chip area and high throughput to the DS-HIE architecture, and the Beneš network ensures the high availability of the routing paths within a compact chip area. In this paper, we explain several methods, namely two data transfer methods and three feedback path methods, and provide an evaluation of the architecture. The evaluation results showed that the structure which allows for the smallest chip area comprises the dedicated wiring method for data transfer and the area effort method for routing. Further, the transistor count of the DS-HIE processor is notably smaller than that of the Core 2 Duo processor.

Prompt and reliable communication between vehicular nodes are essential as its limited coverage and dynamic mobility rate introduces frequent change of network topology. The key feature of vehicular communication that establishes direct connectivity or Road Side Unit-based data transfer among vehicular nodes is responsible for sharing emergency information during critical situations. Multicast routing data dissemination among vehicular nodes is considered to be the potential method of parallel data transfer as they facilitate the option of determining an optimal multicast tree from feasible number of multicast trees established between the source and destinations. This estimation of optimal multicast tree using meta-heuristic techniques is confirmed to improve the throughput and reliability of the network when QoS-based constraints are imposed during multicast routing. An Improved Shuffled Frog-Leaping Algorithm-Based QoS Constrained Multicast Routing (ISFLABMR) is proposed for estimating an optimal multicast tree that confirms effective multi-constrained applied multicast routing between vehicular nodes. ISFLABMR minimizes the cost of transmission to 22% by reducing the number of multicast clusters formed during multicasting through the utilization of local and global-based optimizations. The simulation results of ISFLABMR proveits predominant reduction rate of 24% and 21% in average packet latency and energy consumptions incurred under multicast routing.

Autonomous underwater vehicles (AUVs) are increasingly used for undersea exploration. The endurance of AUVs is limited by the onboard energy storage among which the battery systems dominate. Various underwater recharging methods are employed to increase the AUV range and autonomy. Currently, contact-based underwater recharging utilizes the wet-mate connector technology that requires a high-precision AUV docking, and is prone to electrical safety issues. To overcome these limitations, underwater wireless recharging techniques for AUVs have been explored in recent years. Wireless charging offers a safe and reliable method for autonomous power transfer between a charging station and a vehicle. This article reviews the state-of-the-art inductive wireless power transfer (IWPT) solutions for underwater applications and discusses the engineering challenges of the IWPT system design. Underwater environmental factors, such as seawater conductivity, temperature, pressure, water currents, and biofouling phenomenon, impose constraints on IWPT systems. A comprehensive review of AUV energy storage systems, docking methods, IWPT system control methods, and compensation networks is presented in this article. Based on the main operational and constructional principles, the AUV IWPT systems are categorized as loosely coupled transformers and resonant IWPT systems. Each of the categories is illustrated through their main design principles and implementations reported in the literature so far. Technical challenges, such as integration of IWPT system into an AUV hull, interoperability, alignment and retention issues, docking station sinking and stability, the design of pressure-tolerant charging electronics, data transfer, and the battery operation in the underwater environment are discussed in this article too. The article is concluded with the best practice overview of designing an IWPT system for AUVs.

Processing of the multiple data streams demand highperformance multiple transfers overburden the Processor System on Chips (SoC) in real time multimedia processing applications. High performance direct memory access (DMA) controller eases the processor as it performs bulk data transfer without the intervention of processor. This is true even in most artificial intelligence (AI) based systems and interleaving functions in communication systems where high-speed bulk data transfers are required. This is achieved by the design of Enhanced Direct Memory Access (EDMA) Controller, for high speed bulk data transfers. Paper presents the design of enhanced DMA core which is synthesizable ready to integrate for high performance AI based Digital Signal Processing SoC. The EDMA core is used for flexible Memory Access and bulk data transfers. EDMA core support several methods for data transfer between an input or output (I/O) device and the core processing unit. The processor in the SoC is used to program the Direct Memory Access (DMA) transfer instructions and actual transfers are performed by the EDMA core without the interference of processor. The EDMA design supports flexible addressing modes like linear, circular, step for bulk data transfers. The EDMA core is planned to be verified with test cases as in realistic application scenarios of interleaving, real time video processing.

Optical packet-switching (OPS) networks remain an emerging technology, as exemplified by the feasibility challenges of optical RAM. Nevertheless, OPS networks possess significant potential advantages, including a high data transfer rate and low power consumption due to the elimination of optical–electrical–optical conversion, compared to conventional electronic packet-switching networks, flexibility in handling traffic fluctuations due to packet-switching principles, and low latency as they do not require overhead for communication path establishment. However, effective data recovery from non-negligible packet loss due to optical packet collisions remains a critical challenge in OPS networks. Packet-level forward error correction (FEC) is regarded as an effective data recovery method for lossy networks, such as OPS ones, because it does not rely on packet-by-packet feedback, which could be lost, and avoids the delays associated with feedback and retransmission. On the other hand, FEC requires the transmission of additional packets beyond the original data, increasing the computational load associated with encoding and decoding. In this paper, we propose a data transfer method based on erasure coding, which is useful not only in OPS networks but also in other network environments suffering from severe packet loss due to collisions or signal degradation, such as optical circuit switching networks and wireless networks. This method improves effective data transfer throughput by limiting the number of transmitted packets and reducing the computational overhead of the encoding and decoding processes. This improvement is achieved through the appropriate configuration of the distribution of XOR operation target packets that constitute XOR packets, thereby restricting the number of symbols encoded in each XOR operation and introducing cumulative feedback of which symbols were already recovered at the receiver. To validate the proposed method, we conducted not only simulation evaluations but also what we believe to be the world’s first packet transmission demonstration experiment using packet-level erasure coding over an actual OPS network testbed, taking actual computational overhead into account. The experimental results show that, in high transmission rate environments, the proposed method achieves up to a 188.7% increase in effective data transfer throughput compared to the benchmark LT-Codes-based packet-level FEC transmission method while reducing the processing time on the sender side by up to 15.6% and on the receiver side by up to 39.4%. In addition, the number of packets required for data decoding can be reduced by up to 63.3%.

The transfer of scientific data has emerged as a significant challenge, as datasets continue to grow in size and demand for open access sharing increases. Current methods for file transfer do not scale well for large files and can cause long transfer times. In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. BioTorrents allows files to be transferred rapidly due to the sharing of bandwidth across multiple institutions and provides more reliable file transfers due to the built-in error checking of the file sharing technology. BioTorrents contains multiple features, including keyword searching, category browsing, RSS feeds, torrent comments, and a discussion forum. BioTorrents is available at http://www.biotorrents.net.

The use of computerized medical records for complex medical patients will depend upon the transfer of previous medical data from the manual record into the automated version. The complexity of these patients precludes the complete transfer of medical data into the automated record because of time requirements. This paper describes a method of transfer of medical data from the manual record to the computerized record that is efficient, accurate, and useful.

Heterogeneous systems with multiple different compute devices have come into common use recently, and the heterogeneity of the compute device is mainly reflected in three aspects: hardware architecture, instruction set architecture, and processing capability. Heterogeneous CPU-accelerator systems have attracted increasing attention especially. To make full use of multiple CPUs and accelerators to execute data-parallel applications, programmers may need to manually map computation and data to all available compute devices, which is tedious, error-prone, and difficult. Especially for some data-parallel applications, the inter-device communication could easily become the performance bottleneck of multi-device co-execution. Therefore, firstly, a runtime system is designed for supporting heterogeneous cooperative execution (HCE) of data-parallel applications, which can help programmers to automatically and efficiently map computation and data to multiple compute devices. Secondly, an incremental data transfer method is designed to avoid redundant data transfers between devices, and a three-way overlapping communication optimization method based on software pipelining is designed to effectively hide the inter-device communication overhead. Based on our previously proposed feedback-based dynamic and elastic task scheduling (FDETS) scheme and asynchronous-based dynamic and elastic task scheduling (ADETS) scheme, the modified FDETS that supports incremental data transfer and the modified ADETS that supports three-way overlapping communication optimization are proposed, which not only can effectively partition and balance the workload among multiple compute devices but also can significantly reduce data transfer overhead between devices. Thirdly, a prototype of the proposed runtime system is implemented, which provides a set of runtime APIs for task scheduling, device management, memory management, and transfer optimization. Our experimental results show that the communication overhead between devices is greatly reduced using the proposed inter-device communication optimization methods and the multi-device co-execution significantly outperforms the best single-device execution.