Earticle

This paper presents a novel range-free single chord localization algorithm with low energy consumption and high accuracy for wireless sensor networks. A mobile anchor node and a mobile reference node are employed to periodically broadcast beacon messages and reference messages, respectively. Each sensor node is equipped with a received signal strength indicator for comparing the signal strength. By using the edge function and the result of comparing signal strength, each sensor node only needs one chord on the communication circle for localization. Compared with previous approaches that require multiple chords, less beacon messages are required by the sensor nodes. In the proposed algorithm, the localization error mainly depends on the move distance interval. However, for the other approaches, large localization errors can be observed according to the angle between chords which may descend the localization accuracy dramatically. Analysis results indicate that the proposed algorithm is superior to other approaches in energy consumption and localization accuracy.

Delay Tolerant Network (DTN) is a wireless ad hoc network that intends to provide communication between pair of nodes in spite of having intermittent connectivity and long delays. The nodes are buffer and energy constrained. To provide communication in such scenario, a sturdy routing mechanism is needed. In this paper we propose a new routing technique based on fuzzy logic for intermittently connected network. Also an effort has been made to evaluate the performance of the proposed routing strategy in realistic environment (having obstacles) in MATLAB. The simulation results show that the performance of the network is improved using fuzzy logic based routing strategy and the results of realistic environment are quite different from idealistic environment.

A novel architecture design of large-scale distributed object storage system (called DOSS) is proposed. Our design takes several aspects effecting on the overall performance of DOSS into consideration, including an improved model of interaction based on the traditional interactive mode of object-based storage systems, MDS(Metadata Server) management scheme and a load balancing scheme combines the definition of the maximum load for OSD(Object Storage Device), hierarchical model and re-polling in order to solve access of heat objects issues. Our experimental results show that the architecture is an effective way to promote the performance of DOSS.

How to have a better mining and use of information in the cloud computing environment constitutes the direction of current research in the field of cloud computing; this paper introduces support vector machine (SVM) concept in the cloud computing data mining, introduces a penalty factor in the SVM, and improves SVM data mining algorithms. The constructed Map / Reduce model by the concept of featured multi-tree conducted the validation of the model. Simulation results show that data mining methods of this model have effectively improved the accuracy and the time of information mining, hence have some practical significance.

Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to minimize the program finish-time (makespan) by means of mapping the tasks to the processor elements in such a way that precedence constrains are preserved. This problem is shown to be NP-hard in general form and some restricted ones. Therefore, utilization of heuristic and meta-heuristic approaches to solve this problem is logical. Learning automata (LA) is an abstract model to interact with stochastic environment, which tries to reform itself based on the environment feedback. Although a learning automaton itself is a simple component, a group of them by cooperating each other can show complicated behavior, and can coverage to desired solutions under appropriate learning algorithm. In this paper, an ingenious graph-like learning automata in which each task in the task graph is represented by a learning automaton tries to solve the multiprocessor task-scheduling problem in a collective manner. Set of different experiments on various real-world task-graphs has been done and archived results are so promising compared to the traditional methods and genetic algorithm.

In this paper, a novel approach to multi-hop range-free localization algorithm in wireless sensor network is proposed using principal component regression. The localization problem in the wireless sensor network is formulated as a multiple regression problem, which is resolved by principal component regression. The proposed methods are simple and efficient that no additional hardware is required for the measurements, and only hop-counts information and location information of the beacons are used for the localization. The proposed method consists of two phases: the offline training phase and the online localization phase. In offline training phase, the real distances and the hop-counts among sensor nodes are collected to build localization model. In online localization phase, each unknown sensor node finds its own location using the localization model. The experimental results show that compared with previous localization methods, the proposed method exhibits excellent and robust performances not only in the isotropic sensor networks but also in the anisotropic sensor networks.

Recently wireless sensor Network (WSN) and wireless personal area network (WPAN) has gained lot of interest from the research community because of its diverse applications of controlling, monitoring and automation of home, offices or any rescue area. Zigbee based on IEEE 802.15.4 standard is key enabling technology for the success of both WSN and WPAN. Zigbee has some inherent powerful characteristics like very low power consumption, localization and low cost that has drawn the attention of research community recently. Along with merits come the challenges for the success of Zigbee based networks at various layers of the network model. Network topologies in Zigbee are to be selected based on applications and performance requirements. So this paper investigates the performance of Zigbee for tree and mesh topologies. In doing so parameters like MAC throughput, MAC load, MAC delay, end to end delay have been evaluated through extensive simulations using OPNET. The intricate behavior of these topologies in Zigbee shows optimum bounds of performance for these topologies have been computed in this work

Job scheduling under the cloud circumstance is the key techniques to impact on frequency of cloud calculation. Trust relationship is an important factor of cloud calculation and job scheduling. In order to satisfy the requirement of quality of cloud calculation service for job scheduling, this article introduced the term of trust mechanism and apply it to cloud calculation and job scheduling. It uses altered SFLA as scheduling strategy, and brings forward a model of job scheduling with cloud circumstance which is based on trust mechanism and SFLA. The simulation results showed that this algorithm with faster convergence speed can increase the trust benefit and accomplish job scheduling task well.

It has always been a key topic in the current research how to make rational resource scheduling in the cloud computing environment. In this paper, the status of cloud computing resources is first analyzed, to point out the existing problems, and then combined with the characteristics of resource scheduling in cloud computing, the Shuffled Frog Leaping Algorithm is introduced. First, in its stage of subgroups classification, the chaos strategy is introduced and in the internal search the positive learning strategy is introduced, which makes the improved frog leaping algorithm gain good convergence, reduces the time of global search and optimization. Through the CloudSim platform, it shows that this algorithm can improve the efficiency of task processing and make the resource scheduling in cloud computing rational and effective.

This paper designs and implements a high availability clusters and incorporated with load balance infrastructure of web servers. The paper described system can provide full facilities to the website hosting provider and large business organizations. This system can provide continuous service though any system components fail uncertainly with the help of Linux Virtual Server (LVS) load-balancing cluster technology and combined with virtualization as well as shared storage technology to achieve the three-tier architecture of Web server clusters. This technology not only improves availability, but also affects the security and performance of the application services being requested. Benefits of the system include node failover overcome; network failover overcome; storage limitation overcome and load distribution.

Service composition provides a promising solution to integrate service components and business process, enabling cooperation with new partners. It increasingly relies on multiple service providers to fulfill a given complex business goal. Despite significant progress, most approaches of service composition are still impractical and almost unusable, because the current methods of service composition lack consideration of interaction among service providers. With the increasement of providers and services, it becomes difficult to find the required services in the distributed computing environment. This work addresses such issue and suggests metrics to discover the relationships of service providers by accounting of recent service interactions. In this way, a social network of service providers is constructed, and modeled as a social graph. Service clients can navigate the network and understand the structure and the metrics of social graph by employing social network analysis. Most importantly, through the constructing social networks, the efficiently service composition approach is therefore presented to discover a group of collaboration service providers for satisfying the business requirements. In addition, a case study is conducted to demonstrate the socially aware composition approach in details.

In the virtualization based cloud computing environment, multiple computers are allowed to run as virtual machines (VM) in a single physical computer. Efficient scheduling of limited underlying resources has been a key issue. This paper presents a formal method of CPU resources scheduling (FRS). VMs are divided into three resources statuses according to resources requirements and their run information. FRS scheduling is formally scheduling CPU resources according to the resources statuses. The implementation of FRS is confined to the VMM layer, without VM dependency. The evaluation shows that idle CPU resources of VMs are be used by those VMs which need more CPU resources and the CPU resources overall utilization is improved.

Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. Data center of this cloud computing has huge number of resources and list of applications (with different architecture, configuration and requirement for deployment) want to use those resource. Cloud computing environment uses virtualization concept and provides resources to application by creating and allocating virtual machine to specific application. There for resource allocation policies and load balance policies play very vital role in allocating and managing the resources among various application in clod computing life cycle. CloudSim is an extensible simulation toolkit that enables modeling and simulation of Cloud computing environments. The model proposed by this paper for dynamic load balance policy with considering different attributes and different service level agreements in cloud computing environment helps this environment to utilize their resources and improves performance. The proposed model uses Hungarian algorithm and the result is verified by simulating this model using CloudSim.

A novel chaotic-neuron model is presented by introducing the non-monotonous activation function which is composed of the Legendre function and the Sigmoid function. The reversed bifurcation of the chaotic neuron model is given and analyzed, meanwhile, how do parameters influence the network convergence speed is discussed. Based on the neuron model, the piecewise simulated annealing SLF chaotic neural network was made by introducing the simulated annealing idea, the model improve the convergence speed, at the same time, the precision of this network have not being influenced. The simulation experiment of function optimization and TSP problem verify the effectiveness of the segmented simulated annealing strategy.

Delay and/or Disruption-Tolerant Networking (DTN) is a novel communication prototype that can span across multiple networks and deal with unpredicted conditions in the Internet model.Delay-tolerant networks (DTNs) are partitioned wireless ad hoc networks with intermittent connectivity. Additional terminology in this family of dynamic networks includes disruption-tolerant networks, intermittently connected networks, and opportunistic networks. Routing of the packets in DTN is based on store-carry-and forward paradigm. In this paper, we study and analyze performance of well known PROPHET and Spray and Wait routing protocol, under different human mobility models such as Truncated Levy Walk mobility model (TLW),Self-similar Least Action Walk (SLAW) and Random way point (RWP) model. The MATLAB simulator is used in order to analyze the performance of these routing protocols.Simulation results illustrate that Spray and Wait significantly outperforms the PROPHET on aspects of delivery ratio, average delay and communication overhead.

With the continuous increase in the size and complexity of a real-time computer system, the use of formal verification methods in software development is also on the rise. The traditional formal verification method is not fully applicable to the development of actual system life cycle. Therefore, this paper presents a new real-time system verification method, It takes the deadlock timed Büchi automata as the medium, and translates the timed temporal logic into timed communicating sequential process language. The tock event is also joined, which can be directly used for the detection of refinement tool FDR. The method verifies the situation of deadlock. To establish the link between the conventional model checking and refinement model checking can well combine the advantages of both and improves system security and reliability.

The traditional K-means clustering algorithm is difficult to initialize the number of clusters K, and the initial cluster centers are selected randomly, this makes the clustering results very unstable. Meanwhile, algorithms are susceptible to noise points. To solve the problems, the traditional K-means algorithm is improved. The improved method is divided into the same grid in space, according to the size of the data point property value and assigns it to the corresponding grid. And count the number of data points in each grid. Selecting M(M>K) grids, comprising the maximum number of data points, and calculate the central point. These M central points as input data, and then to determine the k value based on the clustering results. In the M points, find K points farthest from each other and those K center points as the initial cluster center of K-means clustering algorithm. At the same time, the maximum value in M must be included in K. If the number of data in the grid less than the threshold, then these points will be considered as noise points and be removed. In order to make the improved algorithm can adapt to handle large data. We will parallel the improved k-mean algorithm and combined with the MapReduce framework. Theoretical analysis and experimental results show that the improved algorithm compared to the traditional K-means clustering algorithm has high quality results, less iteration and has good stability. Parallelized algorithm has a very high efficiency in data processing, and has good scalability and speedup.

Medical information digitization makes the medical information storage and extraction more convenient. Medical image information security and copyright protection is also gradually being taken seriously, and some medical image watermarking has been applied. According to the characteristics of three-dimensional medical images, this paper proposes a robust zero-watermarking algorithm for medical volume data based on legendre chaotic neural network and perceptual hashing. The algorithm is based on three-dimensional discrete wavelet transform frequency analysis features, which uses perceptual hashing technique to extract medical volume data itself feature vector in order to structure robust zero watermarking. And using legendre chaotic neural network to generate chaotic sequence to enhance the security of the watermarking. The algorithm achieves a combination of legendre chaotic neural network encryption and zero-watermarking technology, which can improve the medical volume data watermarking algorithm security and robustness. The simulation results show the effectiveness of the algorithm.

Collaborative innovation is an important strategy choice of China nowadays, and also of the national universities. Using SOA architecture and cloud computing, we build an integration and optimizing allocation cloud platform of high quality teaching resources for universities-led collaborative innovation to meet the resource requirements of universities based on the analysis of basic concept, types, core process, and evolution characteristics of university-led collaborative innovation following the inherent laws of university-led collaborative innovation, and then analyze the construction concept, logical architecture and main functions of the platform,. Finally, we provide a series of suggestions to the relevant universities and government departments to effectively promote the smooth implementation of the university-led collaborative innovation activities and the integration and optimizing allocation cloud platform of high quality teaching resources for university-led collaborative innovation.

Study the problem of logistic scheduling problem with fuzzy time window, construct mathematical model, and propose double objective function method. In the solution process, for the feature of the double objective functions, use the phased. In the first phase, use the chaos PSO to get the optimal solution. In the second phase, use simulated annealing algorithm and the preliminary solutions got from the first phase to solute the objective function.

Cloud computing task scheduling is a multi-objective decision on how to perform a variety of tasks simultaneously reasonable sort is essential. Therefore, this article constructs a population of convergence non-dominated sorting method. This method is based on non-dominated sorting method; the use of distributed estimation method is improved by four steps to complete the task order scheduling. The experimental results show that this method has good convergence and distribution.

Virtualization-based cloud computing platforms allow multiple virtual machines (VMs) running on the same physical machine. Efficient allocation of limited underlying resources has been a key issue. In order to improve the CPU resources utilization, this paper presents a workload-aware CPU resources scheduling method (WARS). WARS uses the allocated credits and consumed credits to diagnose the CPU resources requirements of VMs and dynamically adjusts CPU resources according to the requirements of VMs. The adjustment of CPU resources is converted into increased or decreased weights of VMs. The implementation of WARS is confined to the VMM layer, without VM dependency. Our evaluation shows that WARS can improve the overall utilization of CPU resources.

With many engineering and science application problems, we must deal with a lot of high-dimensional data, such as videos, images, web documents, text, etc. In the areas of computer vision, image processing and machine learning, high-dimensional data are widespread. However, it is very hard for obtaining meaningful learning and inference from these high-dimensional data directly, the computational complexity of high-dimensional data is often exponential. However, under many conditions, high-dimensional data lie in low-dimensional data corresponding to some classes of the data. Thus, finding the low-dimensional structure from the high-dimensional data is very important. The aim of subspace segmentation is to cluster data that lie in a union of low-dimensional subspaces. In recent years, based on the research of representation methods, many subspace segmentation algorithms appeared. Although these methods are all effective for handling subspace segmentation problems, they all have advantages and disadvantages. This paper focuses on the performance comparison of different subspace segmentation algorithms currently used in handling subspace segmentation problems and views other conventional methods that can be applied in this field.

Cloud Computing Is an Important Transition That Makes Change In Service Oriented Computing Technology. With The Widespread Adoption Of Cloud Computing, The Ability To Record And Account For The Usage Of Cloud Resources In A Credible And Verifiable Way Has Become Critical For Cloud Service Providers And Users Alike. The Success of Such A Billing System Depends On Several Factors: The Billing Transactions Must Have Integrity and No Repudiation Capabilities; the Billing Transactions Must Be No Obstructive and Have A Minimal Computation Cost; And the Service Level Agreement (SLA) Monitoring Should Be Provided In A Trusted Manner. Existing Billing Systems Are Limited In Terms Of Security Capabilities or Computational Overhead. This Project Proposes A Secure And Non-Obstructive Billing System Called THEMIS As A Remedy For These Limitations. The System Uses A Novel Concept Of A Cloud Notary Authority For The Supervision Of Billing. The Cloud Notary Authority Generates Mutually Verifiable Binding Information That Can Be Used To Resolve Future Disputes Between A User And A Cloud Service Provider In A Computationally Efficient Way. Even Administrator of A Cloud System Cannot Modify or Falsify the Data.

Cloud computing is providing basic level of computing facility to meet the daily needs of general or public community.in this paper we have selected the topic cloud computing and its environmental effects. Global warming, greenhouse effect, increasing temperature of the whole environment are major concerns regarding to rise in temperature and pollution of the environment of the whole world. No doubt cloud computing is providing basic facility of computing to general community but at the same time it is playing major role in increasing temperature of the whole world and becoming the source of pollution for the environment. In this paper different strategies have been adopted to control this problem. Virtualization technique is being utilized to control the problem of energy consumption and emission of carbon dioxide gas. So many techniques regarding to virtualization like live migration of VM, Dynamic Resource Allocation of different resources, virtualization of network resources like routers, routing protocols, virtualization of hardware resources like server, storage, memory and other devices. An effort is being exercised to reduce the problem of carbon emission and energy consumption of cloud computing to large extent.

With the rapid increase in international roaming and highly growing number of cellular subscribers it is important to locate the subscriber both efficiently and accurately not only to reduce the cost but also to decrease the load on MSC and decrease the paging delay. The procedure used by the service provider for both collecting and analyzing the information regarding the subscribers’ location is called location management. Location management comprises of location update and paging schemes. This paper tried to highlight the different location management techniques and paging schemes that are used in CDMA network. This paper also compares their features, merits and demerits. Finally the paper discusses various techniques that are used in order to optimize the location update schemes by categorizing them into two broad groups’ i.e. static location update and dynamic location update. Each group enlists different techniques that are used to decide the update frequency of MS (Mobile System). This study has tried to summarize their underlying concepts and various pros and cons.

The most challenging field of concern these days is energy conservation in various applications. Energy crisis led a way to green computing and green computing needs an efficient redesign of algorithms and mechanisms to meet the desired energy efficiency. Green IT is a study and practice which can reduce energy consumption significantly. In this paper various efficient energy saving Green IT methods are discussed. As cloud computing has become a fast emerging technology this days, some energy saving strategy for cloud computing has also been discussed in the paper. Most of the IT sector energy consumption happens in data centers. Virtual Machine migration an emerging energy aware technology used in data centers is also discussed in the paper. Finally some power conserving matrix parameters are given importance.