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With the advance of mobile edge computing (MEC), the number of edge services running on mobile devices grows explosively. In this situation, it is becoming a necessity to recommend the most suitable edge services to a mobile user from massive candidates, based on the historical quality of service (QoS) data. However, historical QoS is a kind of private data for users, which needs to be protected from privacy disclosure. Currently, researchers often use the Locality-Sensitive Hashing (LSH) technique to achieve the goal of privacy-aware recommendations. However, existing LSH-based methods are only applied to the recommendation scenarios with a single QoS dimension (e.g., response time or throughput), without considering the multi-dimensional QoS (e.g., response time and throughput) ensemble, which narrow the application scope of LSH in privacy-preserving recommendations significantly. Considering this drawback, this paper proposes a multi-dimensional quality ensemble-driven recommendation approach named RecLSH-TOPSIS based on LSH and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) techniques. First, the traditional single-dimensional LSH recommendation approach is extended to be a multi-dimensional one, through which we can obtain a set of candidate services that a user may prefer. Second, we use TOPSIS technique to rank the derived multiple candidate services and return the user an optimal one. At last, a case study is presented to illustrate the feasibility of our proposal to make privacy-preserving edge service recommendations with multiple QoS dimensions.

The ever-increasing popularity of web service sharing communities have produced a considerable amount of web services that share similar functionalities but vary in Quality of Services (QoS) performances. To alleviate the heavy service selection burden on users, lightweight recommendation ideas, e.g., Collaborative Filtering (CF) have been developed to aid users to select their preferred services. However, existing CF methods often face two challenges. First, service QoS is often context-aware and hence depends on the spatial and temporal information of service invocations heavily. While it requires challenging efforts to integrate both spatial and temporal information into service recommendation decision-making process simultaneously. Second, the location-aware and time-aware QoS data often contain partial sensitive information of users, which raise an emergent privacy-preservation requirement when performing service recommendations. In view of above two challenges, in this paper, we integrate the spatial-temporal information of QoS data and Locality-Sensitive Hashing (LSH) into recommendation domain and bring forth a location-aware and time-aware recommendation approach considering privacy concerns. At last, a set of experiments conducted on well-known WS-DREAM dataset show the feasibility of our approach.

IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method.

Attributed-based access control (ABAC) is widely used in systems with large resources and users such as the Industrial Internet of Things (IIoT), Industrial information integration system, and so on. Attribute-based security policy is highly flexible and expressive, but conflicts between policies occur frequently, affecting the security and availability of the system. Based on analyzing the ABAC security policies represented by the eXtensible Access Control Markup Language (XACML), this study proposes a formal definition of explicit conflicting rules, probable-conflicting rules, and never-conflicting rules. Also, we found that conflicts occur on a pair of rules in which attribute expressions have overlapping values and that be applied to the same request. A new conflict detection method is proposed in which implicit conflicting rules are converted to explicit conflicting rules by completing the absent attribute expressions and then compare all the rules in pairs to detect all the probable conflicting rules in a rule set. In this way, we can analyze the conflicting probability of each pair of policy rules. Furthermore, we define two metrics to evaluate the conflict level of a rule set. Experiment results show that implicit conflicting rules are more numerous than explicit conflicting rules in the policy set. Also, with an increase in the number of attribute expressions in each rule, the conflicting level of a rule set is significantly reduced, which provides a reference for policymaking. With this method, administrators can formulate more robust and efficient security policies, improve the security and availability of systems.

Due to the complexity of mechanical system, multiple faults may co-exist in a rotating machinery, where vibration is commonly used for diagnosis. The measured vibration signal could be considered as a result of convolution process of malfunction induced periodic impact signal and resonant response of the mechanical component, and deconvolution is an effective way to restore impulses. The minimum entropy deconvolution (MED) has been shown to be an effective deconvolution method and has been employed in rotating machinery fault diagnosis. Nevertheless, the simulation in this paper shows that the MED is unable to identify multi-faults of rotating machinery fully when different faults excite different resonance frequencies. To overcome this shortcoming, a new multi-faults detection method based on Spectral kurtosis (SK) and MED is proposed. The effectiveness of the proposed method is validated by simulation data and field signals from a vacuum pump.

The threshold voltage distribution after ideal programming in NAND flash memory cells is usually distorted by a combination of the random telegraph noise (RTN), cell-to-cell interference (CCI), and the retention process. To decide the original bits more accurately in this scenario, a precise channel model shall be utilized on the basis of the measured threshold voltages. This paper aims to characterize these various distortions occurring in multi-level cell (MLC) flash memories. A mathematical description of the overall distribution for the total flash channel distortion is presented. The final threshold voltage distribution for each symbol of MLC flash is also characterized, which is important for calculating the exact soft decisions of cell bits and the application of advanced flash error correction. The results of the theoretical analysis have been validated through Monte Carlo simulations of the flash channel.

Compared with the traditional sequential design method, concurrent engineering is a systematic approach to integrate concurrent design of products and their related processes. One of the key factors to successfully implement concurrent engineering is information technology. In order to design a product and its manufacturing process simultaneously, information on product features, manufacturing requirements, and customer demands must be processed while the design is concurrently going on. There is an increased understanding of the importance of the correct decisions being made at the conceptual design and development stages that involve many complex evaluation and decision-making tasks. In order to promote the efficiency in concurrent product development, appropriate evaluation and decision tools need to be provided. In this paper, the characteristics of fuzzy, multi-stage evaluation and decision making in concurrent product development process are analyzed and a decision support system for product design in concurrent engineering is presented. An example is given to illustrate the application of the system.