Earticle

Cloud computing offers numerous benefits including scalability, availability and many services. But with its wide acceptance all over the globe, new risks and vulnerabilities have appeared too. Cloud computing provides facility of storing and accessing information and programs over the web without bothering the storage space on system. Storing the data on cloud eliminates one’s worries about space considerations, buying new storage equipment or managing their data, rather they are able to access their data any time from any place provided they have internet access. But the rising security problems have resisted the organizations from connecting with cloud computing completely. Hence security risks have appeared as the main disadvantage of cloud computing. This paper involves the efforts to analyze the security issues and then proposes a framework to address these security issues at the authentication and storage level in cloud computing. While addressing the security issues the first and the foremost thing is to classify what data needs security and what data needn't bother with security and hence data gets classified into two classes sensitive and non-sensitive. To achieve data classification, a data classification approach based on the confidentiality of data is proposed in this paper. Following that an efficient security mechanism has to be deployed by means of encryption, authentication, and authorization or by some other method to ensure the privacy of consumer’s data on cloud storage.

The single-instance deployment mode can't meet the needs of the increasing user access, and multiple-instance deployment is a feasible scheme to improve the scalability of the service system. The traditional reliability analysis methods are aimed at the single-instance deployment and they cannot be used to analyze the reliability of the service system of multi-instance deployment. To address this problem a new approach is proposed in this paper to analyze the reliability of service that a services system provided in the case of multiple-instance deployment. This approach extends the traditional user-oriented system reliability analysis method, adopting a vector representing the reliabilities of the multiple instances of a component service to settle down the problem. To bring this method to fruition, a prototype framework is constructed and realized in Java, and a case study demonstrates this approach.

LEO satellites are nearer to the earth’s surface than that of the other satellite systems. LEO satellites revolve round the earth is in the range from 500-1500km from the surface of the earth. The satellite is placed closer to the surface of the earth. If the height of the satellite is increased by increasing the power of the equipment used in the satellite system, the beam of the satellite cover a wide area in the surface of the earth. The radius of the foot print will increase as the height will increase and as the radius will increase the area of coverage will also increase. Height (h) is directly proportional to the radius(r) and area (A) is directly proportional to the square of the radius. Thus, the number of satellite required to cover the whole earth will decrease. The handoff failure will be decreased.

Route maintenance and repair process plays an important role in mobile self-organizing network. On the basis of considering route stability and energy aware routing protocol, a new scheme is proposed in this paper. Firstly, signal strength, rest energy and port queue length are periodically and alternately detected; secondly, cross-layer method is adopted for route maintenance, and the alternate route is actively established for data transmission according to the routing repair mechanism “Make-before-Break”; finally, relevant experiment is carried out in NS2 to simulate and compare the proposed protocol and other similar routing protocols such as RSEA and LAER. The experiment result shows that the new scheme has good performance in the aspects of data packet delivery ratio, overhead control and end-to-end delay.

More and more DG is connected with the grid through the low voltage distribution network, how to choose a protection scheme without depending on the nature of DG, and with reliable operation mode is a big issue encountered by the engineers. The rationally allocating the protection scheme of grid connection coupling point, which not only can judge the system fault in time, and judge it in DG island, and can still remain the protection features in the dynamic process of the island formation and in the operation mode of dividing island, can always have enough selectivity of the island fault is the problem to solve in this study. Through the selection of the grounding methods of DG grid connection transformer, the relationship between zero sequence current and zero sequence voltage is analyzed; the optimization of the protection scheme can inhibit DG interconnection and zero sequence over-voltage in network running-off, and quantity of zero sequence can be used as a stable criterion when the fault occurs, so as to work out a set of protection optimization schemes for DG interconnection transformer grounding system. Finally, through the COBASE system, a protection scheme of the wind power plant low-voltage grid is generated in this study, which verifies its excellent protection features in the DG connected to the grid system.

Wireless body area network (WBAN) is a communication standard optimized for low power devices and operation on, in or around the human body but not limited to humans used to serve a variety of applications including medical, consumer electronics, entertainment and other[1]. The challenge of WBAN is that the energy of the sensors is limited by the battery life that’s why the transmitting of data is not continuous for long time. This leads to poor efficiency of the WBAN because the continuous power decreasing is due to network operations. An algorithms such as MAC zigbee802.15.4 and adaptive reliable MAC are important to reduce the replacement of batteries many times and to save power consumption. In this paper we concentrated our study on some type of sensors, they are telosB, mica2, micaz, and imote2 depends on power consumption calculations using mat lab program and based on adaptive reliable MAC. We analyzed the sensors with several nodes and packet sizes, and calculate the lifetimes for all the sensors in WBAN. The results we obtained show a good performance of using Adaptive reliable MAC in Wireless Body Area Networks.

This paper introduces a kind of research and application of group-type of high voltage electric field measurement and safety distance caution apparatus based on wireless communication technology, the group-type caution apparatus is combined with a master and multiple slaves, in which the slaves are used to measure electric field and the master is used to monitor the slaves. The master keeps the real-time wireless communication with the slaves, realizing the safety dual monitoring of the high voltage working environment and guaranteeing the personnel safety of live working man. Conduct a working performance test on the site of Chongqing Rongchang 500kV Hongban transmission line I about the stability and accuracy. The test results show that the designer of group-type of HV electric field measurement and safety distance caution apparatus can work stably in the strong electric field environment, besides, it has a good performance in both safety distance exceeding alarm and wireless communication.

Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT) and fuzzy Petri nets (FPN). Based on the concepts of FT and FPN, a FT based FPN model for reliability analysis is obtained. In addition, the reliability calculation method is introduced and this work designs a hybrid intelligent algorithm integrating fuzzy simulation and NN, namely NN based simulation algorithm, to solve it. Finally, taking an electric vehicle motor as an example, its reliability modeling and evaluation issues are analyzed. The results illustrate the proposed models and the effectiveness of proposed algorithms. Moreover, the results reported in this work could be useful for the designers of electric vehicle motor, particularly, in the process of redesigning the electric vehicle motor and scheduling its reliability growth plan.

The aim of this study is to classify critical security issues in cloud banking organizations, according to the cloud service models (CSM), and the cloud deployment models (CDM). We classified and ranked the critical cloud security issues for banking organizations based on secondary and primary data. In this regard the cloud Delphi study is modified into three phases like identifying, analysis, and evaluating security issues. In this study, the samples of 40 panelists were selected from inside and outside Malaysian banking organizations based on their experienced in the banking environment to set the insight of the cloud issues. The study starts with a list of cloud security issues based on secondary data and interview. In addition, we illustrated the level of risk for all the cloud security issues in banking organizations as small, medium, and large risk. We also indicated that the highest risk of “Trusted cloud” in 3rd party (providers) and policies security issues and the highest risk of “Availability and Mobility” in application and program (software) security, etc. The study has been conducted on groups of cloud IT managers and cloud banking developers. As a future work, we will control and mitigate the critical security issues by using artificial intelligence techniques (ANN). A successful classification of critical cloud security issues will greatly improve the probability of cloud banking success rate.

Spectral clustering is a clustering method based on algebraic graph theory. It has solid theoretical foundation and good performance of clustering. However, during the process of nonlinear low rank approximation, the traditional spectral clustering algorithm can’t effectively remove redundant features leading to the phenomenon that the local area can not distinguish. It also suffers from the high computational complexity of eigen-decomposition when dealing with the high dimensional data. In order to resolve the aforementioned problems, in this paper a novel Spectral clustering algorithm called LF-SC is proposed. Firstly, based on the nonlinear low dimensional embedding feature selection, we realize dimension reduction. The multi clustering structure of the data is captured, the potential manifold structure is fully discovered, and the geometry structure of the low dimensional manifold clustering is well maintained. Secondly, utilizing the SVD instead of EVD to obtain the eigenvectors reduces the computational complexity and maintain the local structure of the data as well as low dimensional manifold. Extensive experiments show the effectiveness and efficiency of our approach.

Service composition is considered as a promising approach to generate composite services for satisfying services Consumers’ needs using their preferences. Literature review shows that previous studies ignored issues that affect Service composition such as increasing composition time and no ranking for produced composite services. To address these issues, we propose a semantic-aware approach for automatic cloud services composition. Firstly, a semantic network of service is built using an efficient input-output matching algorithm. Secondly, a Forward-Chaining based algorithm is adopted to create composite services, according to the request of the Cloud Service Consumer. Thirdly, a hybrid ranking method based on similarity quality and QoS Criteria is implemented to select “the most suitable” composite service. The results of experiments provide evidence that our approach can effectively produce composite services for service consumer’s request, and show benefits of consuming less composition time comparing to similar composition algorithms.

As a widely used programming model for the purposes of processing large data sets, MapReduce (MR) becomes inevitable in data clusters or grids, e.g. a Hadoop environment. Load balancing as a key factor affecting the performance of map resource distribution, has recently gained high concerns to optimize. Current MR processes in the realization of distributed tasks to clusters use hashing with random modulo operations, which can lead to uneven data distribution and inclined loads, thereby obstruct the performance of the entire distribution system. In this paper, a virtual partition consistent hashing (VPCH) algorithm is proposed for the reduce stage of MR processes, in order to achieve such a trade-off on job allocation. Besides, experienced programmers are needed to decide the number of reducers used during the reduce phase of the MR, which makes the quality of MR scripts differ. So, an extreme learning method is employed to recommend potential number of reducer a mapped task needs. Execution time is also predicted for user to better arrange their tasks. According to the results, VPCH can lead to load balancing and our prediction model can provide fast prediction than SVM with similar accuracy maintained.

Traditional Photovoltaic (PV) mathematical models have advantage of high accuracy, but its characteristics are multi-parameters and non-linear, which makes it difficult to be solved. Obviously, these are not suitable for engineering design since it requires both complexity and accuracy. In this paper, a simplified precision engineering PV mathematical model is proposed based on analysis of double diode PV cells mathematical model, and also gives a parameter calculation method based on Newton-Raphson method. Four different kinds of PV materials and variable resistance parameter characteristics are separately tested under different irradiance and temperature conditions. The results show that the proposed model can quickly and accurately reflect the PV model I-V and P-V characteristics, which has good versatility and sensitivity, and provides strong support for PV system related research.

This paper presents a review on recent trends and challenges in the development of power dividing components. Over these years, power dividers have been changing their structures and technology in order to be compatible with the newer changes adopted in designing of communication systems. From being operated as a single-frequency component to being flexible with dual-frequency bands, Wilkinson power dividers have come a long way to become an important and integral part of various RF/microwave devices. The recent technologies for cellular communication have motivated researchers to work on miniaturization and harmonic suppression of power dividers to make them more compact and efficient. Both equal and unequal power dividers are largely employed for L and S-Band applications (GSM, CDMA, Personnel communication systems). S-parameters such a return loss, insertion loss and isolation between output ports plays an important role in their designing.

According to the color difference fabric dyeing measurement in production, partial color existed bad real-time problems. Color difference and color of RGB fabric images put forward by using the method of color distribution measurement of general, which needed rapid measurement of large area color. According to RGB distribution histogram of the image, the measured color with sample color similarity and distribution curve of characteristic value was calculated, obtained the RGB color model, and gives a method of a partial color judging. The experiment proves the feasibility of this measurement fabric color difference and the judgment method and effectiveness of partial color. Color cast detection method has overcome the limitations of the traditional methods of detecting image color cast. In order to partial color image detection is performed after correction, using a combination of gray world and a perfect reflection of the color correction method, to make up for the shortcomings of traditional method. The experimental results show that, the color deviation correction method of image analysis based on detection improved the reliability of color cast detection. The method uses characteristics of image analysis, it has universal applicability.

A large number of distributed photovoltaic power grid, islanding problems become more prominent. This paper first analyzes some existing anti-islanding scheme, and analyzes its advantages and disadvantages. Then, this paper proposes adaptive anti-islanding scheme by using the load, i.e. load automatic switching, changing the characteristics of the whole load, make the passive anti-islanding without dead-zone. Then using PSCAD simulation of the program, to verify the usefulness of the proposed scheme. To minimize the distributed photovoltaic power grid investment, saving the scheme relies on a new generation of distributed photovoltaic (pv) grid interface device. Then the paper introduces the overall structure and function of the device configuration, the device not only can solve the problem of area of backup protection, also save on investment, convenient for on-site equipment installation and maintenance, for the widespread promotion of distributed photovoltaic power grid to lay a solid foundation.

The virtualized systems are today a very popular topic and their using plays the great role in current datacenters. The virtualization efficiency is a very important aspect in the real system deployment. Some studies have been published about this topic [1], mainly are based on various benchmarking techniques and are integrated into the specialized testing tools. Such a benchmark tool, which is able to simulate the behavior of a real computing system under the stress of different virtualization configurations, can e.g. well answer the question how many virtual machines could be simultaneously executed on it and how big the virtualization overhead is [2]. We developed and applied a new benchmark tool, which is able to measure the virtualization efficiency and overhead in the virtualization environment.

With the wide popularity of mobile devices, e-commerce software in the mobile client forms a larger consumer market, which makes the volume of e-commerce transactions having a wide range of growth. As the consumers have a frequent contact with mobile devices, which makes consumers pay more attention to security and entertainment of the e-commerce software. Past mobile e-business research typically focus on the related factors about transaction process, like interface design, services and user experience, but seldom study the factors about consumers. On the basis of the TAM, this paper not only added perceived security and perceived entertainment, but also the consumers’ personality factors like subjective norm in TRA, into the model. The study shows that PU, PEU, PS and PE all will have direct or indirect effects on use attitude and use intention, and subjective norms and use attitude will directly affect the consumers' use intention. Through the empirical analysis we found that e-commerce operators not only make the consumers experience more of usefulness and ease of use, security, and entertainment in the technology and services, but also improve the popularity with the aid of the mainstream media propaganda and star power to enhance the public's normative beliefs, which will ultimately affect the user behavior of the consumers.

Microblog has been a dominant social-network platform. Many social events spread fast on microblogging platforms, because of the high interactivity and large amounts of users of microblogging platforms. However, users can only search for interested events on microblogging platforms by keywords, yielding high redundancy of search results as well as the lack of complete description on events (owing to the limitation on the length of microblogs). Aiming to solve these problems, we propose a new approach to automatically generate event abstracts for microblog events. We summarize an event via two aspects, namely an event-description set and a user-opinion set. For extracting the event description for an event, we first partition microblogs into a set of short sentences. These sentences are further ranked by computing the relevance to the event based on a graph model and the most relevant sentences are selected as the set of event description. For extracting user opinions on an event, we propose a supervised learning model that is based on the set of long sentences extracted from microblogs. We conduct experiments on a data set consisting of 14 events crawled from the Sina Weibo. The experiment results suggest the effectiveness of the proposed approach.

The Flexible AC Transmission System (FACTS) controllers have been resolved many technical issues which emerged with deregulated environment. One of such issues is transmission system performance improvement under open access. In this paper, two FACTS controllers, Thyristor-Switched Series Capacitor (TSSC) and Unified Power Flow Controller (UPFC) are selected to improve the system performance. At the first stage, the generation schedule under competitive electricity market dispatch model is obtained and at the second stage, the impact of the selected FACTS controllers is analyzed while executing various power transactions. A two-stage hybrid optimization approach using Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) algorithm is adopted to find out the optimal location and parameters of FACTS controller to achieve the required objectives like voltage stability index maximization as well as transmission loss minimization. For case studies, standard IEEE 30 bus test system is used and the simulation results obtained have been validating for real-time adaptability of this proposed method.

Hybrid cloud can help companies to maximize the potentiality of IT infrastructure, so the hybrid cloud is increasingly popular in the market. Hybrid cloud scheduling has become one of the important issues which cloud providers face in the current. How to effectively and safely make hybrid cloud scheduling is one of the main research topics in the field of hybrid cloud. This paper introduces an effective hybrid cloud resource management model, and proposes a hybrid cloud resource scheduling strategy based on the particle swarm leapfrog algorithm and trust mechanism in order to solve the problems. The experimental results show that the hybrid cloud scheduling strategy is effective in terms of security and throughput.

Cloud computing has based on on-demand access to potentially significant amounts of remote datacenter capabilities in distributed systems environment. By propagation data centers in clouds, computers should have a lower energy consumption. Hence, a good prediction about the amount of needed resources is very helpful for correct decision on energy consumption management. In this paper the Fuzzy Logic approach has been used for decision making about the performance of processors to obtain the estimated amounts of energy consumed by them and have been applied Auto Regressive and Neural Network approaches to predict the future workload. In prediction operation, three modes On, Off and Idle are considered for each server. At times of peak demand all servers must be turned on but when consumption is low, an estimated number of servers that must be turned on is obtained, 10% of remaining servers are idle and others are off. In this case a significant amount of energy is saved compared to the case that all servers are clear. The result show that Neural Network approach has more accurate prediction and better performance than Auto Regressive model.

Mobile Internet can enable the reader to obtain a digital library resources from anywhere, but because of the huge amount of data in large data quickly retrieve the required information, the problem faced by mobile cloud library, aiming at this problem, we propose a big data mobile cloud Books and memory retrieval system with associated services, according to this system, it is possible to quickly search and develop reading programs for the reader's query and search time.

This paper presents a novel approach for dynamic economic dispatch in a MicroGrid by using the improved ant colony optimization (ACO). In order to satisfy the power balance and load demand, the mathematical model of economic dispatch is given to support the MicroGrid in both grid-connected and islanded mode. By means of ACO, the dynamic economic dispatch solution is proposed in combination with sequential quadratic programming (SQP), and the essential procedure is introduced. Simulation results demonstrate the feasibility and benefits of the proposed scheme.