Tin-Yu Wu

Improving Accessing Efficiency of Cloud Storage Using De-Duplication and Feedback Schemes

File distribution and storage in a cloud storage environment is usually handled by storage device providers or physical storage devices rented from third parties. Files can be integrated into useful resources that users are then able to access via centralized management and virtualization. Nevertheless, when the number of files continues to increase, the condition of every storage node cannot be guaranteed by the manager. High volumes of files will result in wasted hardware resources, increased control complexity of the data center, and a less efficient cloud storage system. Therefore, in order to reduce workloads due to duplicate files, we propose the index name servers (INS) to manage not only file storage, data de-duplication, optimized node selection, and server load balancing, but also file compression, chunk matching, real-time feedback control, IP information, and busy level index monitoring. To manage and optimize the storage nodes based on the client-side transmission status by our proposed INS, all nodes must elicit optimal performance and offer suitable resources to clients. In this way, not only can the performance of the storage system be improved, but the files can also be reasonably distributed, decreasing the workload of the storage nodes.

Multilayered Impact Evaluation Model for Attacking Missions

In practical application scenarios, direct attacking on a target system to test the impact of attack methods may expose an attackers intent and result in the difficulty in evaluating the attack method. Therefore, it is essential to design a controllable target range for testing and evaluating the attack impact. In this paper, we construct an attack test platform in order to evaluate the attack impact from different attack tools or the combinations of these attack tools. According to “vulnerability-asset-service-mission” (VASM) relationship, we design a multilayered evaluation model VASM, which includes a four-layer information structure: vulnerability layer, asset layer, service layer, and mission layer, from bottom to top. Considering that each asset may have one or more vulnerabilities, we score the attack impact on each asset based on attack probability and vulnerability and calculate the operational capacity of an asset after an attack. Since services may be provided jointly by one or more assets, we calculate the attack impact on services utilizing the dependencies among assets. The attack impact can be transmitted layer by layer from bottom to top through the dependencies among nodes. Finally, we can obtain the attack impact on missions. We use an actual logistics management and tracking system as the target range and verify the effectiveness and validity of our evaluation model, i.e., VASM, on goods delivery. Experimental results show that VASM cannot only assess the attack impact directly but also conform to the actual situations accurately.

A 3D neural network for moving microorganism extraction

Accurate detection and extraction of moving microorganisms from microscopic video streams is the first important step in biological wastewater treatment system. We propose a novel moving object extraction algorithm based on a 3D self-organizing neural network to overcome the prominent challenges in microorganism video sequences, such as error bootstrapping, dynamic background, variable motion, physical deformation and noise obscured. Firstly, we design a multilayer network topology instead of the traditional single-layer self-organizing map, which significantly improve the discrimination ability of moving objects. Secondly, new designed mechanisms related to background model initialization and adaptively update have effectively weakened the bootstrapping and ghost influences. Thirdly, we create buffer layers in neural network efficiently to resolve the dynamic background and variable motion problems. Finally, a simple Kalman predictor with constant coefficients has been constructed to tackle with the cases of microorganism being obscured or lost. Experimental results on real microscopic video sequences and comparisons with the state-of-the-art methods have demonstrated the accuracy of our proposed microorganism extraction algorithm.

Constructing data supply chain based on layered PROV

The inability to effectively construct data supply chain in distributed environments is becoming one of the top concerns in big data area. Aiming at this problem, a novel method of constructing data supply chain based on layered PROV is proposed. First, to abstractly describe the data transfer processes from creation to distribution, a data provenance specification presented by W3C is used to standardize the information records of data activities within and across data platforms. Then, a distributed PROV data generation algorithm for multi-platform is designed. Further, we propose a tiered storage management of provenance based on summarization technology, which reduces the provenance records by compressing mid versions so as to realize multi-level management of PROV. In specific, we propose a hierarchical visual technique based on a layered query mechanism, which allows users to visualize data supply chain from general to detail. The experimental results show that the proposed approach can effectively improve the construction performance for data supply chain.

Adaptive and safe presentation strategy of image information on social platform

With the development of social network, there are more and more people to share pictures on social platforms. Since the information contained in picture has the different requirements for confidentiality, it makes the selective presentation of secret information to be an urgent problem. Estimating users privilege of gaining some regions based on his/her attributes is a novel solution. But there are few perfect solutions aiming at the strategy of adaptive calculation for users privilege in the existing literatures, especially for the scenario in which the real values of some attributes have priorities. In this work, based on the cipher text-policy attribute-based encryption (CP-ABE), we propose an adaptive and safe presenting scheme for the information contained in a picture. This scheme firstly embeds the confidential data outside the secret region, and generates the image mosaic in the secret region; when someone requesting the original version of image, it adaptively calculates the recovery privilege of requestor with the strategy proposed in this paper, then precisely present some regions based on the privilege level of requestor. Moreover, we firstly propose the vote-attribute which facilitates the attribute revocation. The experiments demonstrate that, based on the privilege level, the proposed scheme can safely present the original version of the corresponding image region, and expediently achieve the attribute revocation. Compared with other algorithms, our scheme can restore the original version of image with only 1/2 secret data, and spend little time over the attribute revocation. Besides, the average of peak signal to noise ratio (PSNR) is 4dB more than the algorithms available, and the standard variance of PSNR is less than 0.4.

Monitoring the status of iBeacons with crowd sensing

Since Apple introduced the iBeacons in Worldwide Developers Conference (WWDC) 2013, the iBeacon has been rapidly accepted and generalized in the market. For the deployed iBeacons, it is necessary to monitor their status. In this paper, we design a crowd sensing based monitoring framework which combines the moving and static schemas of participants to monitor the real status of iBeacons. In such a system, the inaccuracy and conflict of the collected signal information, commonly caused by the error rate of participants or the differences of sensing context, have received more and more attention. Estimating the real status of iBeacons according to the uploaded signal information becomes a big challenge for our monitoring system. Towards this end, we propose a context-aware estimation approach in this paper. We first model the effects of sensing context, and then propose an iterative method to infer the error rate of participants and estimate the real status of iBeacons with high precision. Our method is tested via extensive simulations, and verified by our monitoring system which has been applied in the teaching building. The results demonstrate that the proposed estimation approach outperforms recent popular three-estimates algorithm and OtO EM algorithm. At last, we develop the review mechanism, which ensures the efficiency of our monitoring system.

QoS prediction method for data supply chain based on context

Due to the execution paradigm may be different at different invocation time, users obtain different QoS when interacting with the same Data Supply Chain (DSC). However, existing QoS prediction methods seldom took this observation into consideration, which shall decrease the prediction accuracy. In this paper, we propose a context-based QoS prediction method for data supply chain. First, a QoS mathematical model is developed for considering the mass data transmission across elementary sub-chains. Then, two execution paradigms of data supply chain are discussed. Besides, we explored several special context factors of data supply chain (such as invocation time, data source update period and execution paradigm) which influence QoS. By processing such context information, we can obtain the part of data supply chain which is need to execute when the user query occurs and leverage them to predict QoS. Experimental results indicate that our approach improves the prediction accuracy and efficiency of QoS when compared to previous methods.