Earticle

Many Palestinian higher education institutions had a successful experience in utilizing Electronic Services and Electronic learning, such as all the academic services for student and lecturer, schedule lectures, exams schedule, viewing the academic information, grades report, printing transcript, registering courses, library services, Email services, News and Announcements, but these services are supported by web based applications or desktop application which restrict the access of the users using computers or laptops, But there is a lack in supporting these services with mobile. Especially when dealing with sensitive data as inserting grades for courses. So in this paper we present a system called Secure Mobile Cloud Computing for Sensitive Data: Teacher Services for Palestinian Higher Education Institutions (MCCTSs) which is a mobile application to facilities access using RSA algorithm to encrypt the data which sent and received through Cloud computing application. MCCTSs serve the lecturers of Palestinian higher education institutions. Agile methodology was adapted to develop MCCTSs application. MCCTSs users successfully insert the grades securely and encrypted any time anywhere using RSA algorithm. It is hoped that the result of this study will encourage the universities to engage MCCTSs in their services. From the results we obtained, it is proved that RSA provides protection for the data, which is stored in Cloud. Only authorized user can read the encrypted data and decrypt it. Even if anyone happens to read the data accidentally, the original meaning of the data will not be understood.

Information entropy features are often used for radiation source signal recognition, but due to the information entropy is very sensitive to noise, so this method has greater recognition rate changes with the SNR. This paper putting forward a viable recognition based on Entropy and cloud model. using cloud model to extract secondary features of signals, build radiation source signal’s entropy and cloud feature vector. The method uses cloud model description and processing interval fuzzy and observation noise data, better solve the low SNR cases of radiation source signal feature extraction problem. At the same time, putting forward the similar cloud classification recognition algorithm based on cloud model. The simulation results show that Entropy and cloud model has better recognition effect under low SNR, which can improve the signals’ recognition rate under low SNR.

Social network analysis is an evolving field of research and link prediction problem shows a vital role for prediction of social network structure. This paper emphases on prevailing research on link prediction problem. Prevailing researches reveal that link prediction problem complexity, available solutions effective group communication management and social link consciousness. The link prediction problem across associated networks can include anchor link prediction problem and link transfer through associated heterogeneous networks. This paper summarizes recent growth about link prediction algorithms and survey of all the prevailing link prediction techniques.

In order to solve the problem of non-restoration of PVSS, in this paper, we propose a method to recover the secret image without using the Boolean operation of the matrix matrix. In the lossless recovery process, each column base matrix will be "and" and "exclusive or" operation to get the original image anti-color image, and then the image can be obtained by the lossless secret image, this method can be applied to the general ( k, n ) threshold lossless recovery. In the absence of a computing device, it can be decrypted by superimposing, in the XOR computing device, it can realize lossless recovery. Experimental results show that the proposed algorithm can guarantee the gradual visual secret sharing, complete the restoration of the secret image, the operation is simple and efficient, and has a strong practicability.

While the innovation and technology is modernizing the industry at rapid pace, the electrical power system is lagging behind the race and is being maintained in the same way for decades. Although the smart grid has ensured demand-response, reduction in transmission and distribution losses, and minimized theft, the inherent characteristic of enormous data generation has become a biggest concern. The absence of IT infrastructure in existing set up for handling unstructured data and huge capital investments are the major obstacles in complete success of smart grid in developing countries like India. However cloud computing has emerged as a solution to this problem. In this work, we have simulated cloud for smart grid on CloudSim and derived the cost for storing the smart grid data on cloud. The total cost of ownership (TCO) and return on investment (ROI) on building the datacenter with same configuration is also calculated using net present value (NPV), and compared with previous calculated cost. The results indicate that the initial cost of building a datacentre is $6673185 and ROI is 22.74 % in 7 years, which is low keeping in view the fact that during average design life of 10 years, a datacenter undergoes major equipment changes 3 to 4 times. On the contrary, outsourcing the same data on to cloud costs $2430720 annually, which is considered as an effective alternative. This analysis would be beneficial to developing countries like India in framing policies for storing the smart grid data on to the cloud which will further help in reaping the benefits of smart grid.

According to the power load has strong randomness and difficult to forecast, the introduction of the two types of fuzzy logic in order to improve the prediction accuracy. The interval type two non-single valued two type Mamdani fuzzy model for power load time series forecasting, and reverse the spread of a similarity of a singular value decomposition iterative blending algorithm to simplify the redundant rules in the model of fuzzy sets and redundant fuzzy rules, in order to eliminate the adverse effects. For ordinary type-2 fuzzy sets, uncertainty of the trace and once the membership function is the most important factor, therefore in the calculation formula for construction of two kinds of measure when considering these two factors; analysis of the ordinary type-2 fuzzy inclusion degree properties; discussed two kinds of conversion between the new measure of the relationship, revealing its internal relations; finally through an example to verify the performance of the new measure ordinary type-2, and the fuzzy similarity and Yang Shih clustering method combining cluster analysis used in Gauss plain type-2 fuzzy sets, obtained the reasonable clustering results, verify the rationality of the new measure and effectiveness.

The main contribution of this paper is: (i) proposing a new efficient and simple load flow method for balanced radial and meshed distribution systems, (ii) impact of load models, different X/R ratios, load growth and tolerance levels to check robustness of proposed load flow method, (iii) impact of number of loops on meshed distribution systems, (iv) Comparison of radial and mesh distribution for voltage profile, total power losses, and number of iterations. The convergence ability of the proposed load flow method is evaluated under these conditions. Computer program has been developed to implement the power flow solution scheme in MATLAB and successfully applied to several practical distribution networks with radial and mesh structure. The convergence ability is quantitatively evaluated for different loading conditions, load growth, R/X ratios, and tolerance levels. Moreover, the effects of static load modeling on the convergence characteristics of the proposed algorithm are also investigated. Effectiveness of the proposed load flow method has been tested on IEEE 33 bus and IEEE 69 bus radial and meshed distribution networks. The proposed load flow method is compared with existing load flow methods, Ghosh et al. [6], Aravindhababu et al. [7], and J.H.Teng [16] to demonstrate its effectiveness.

Recent years have witnessed an increasing interesting in data stream processing, such as network monitoring, the e-business, advertising system and etc. Join is applied to explore the correlation among the tuples from multiple streams. In this paper, we present a general method named Distributed Streams Join (DSJ) to process multi-way windowed streams θ-joins using a shared-nothing cluster. DSJ contains a distribution method named Time-Slice Distribution Method (TDM) and a join method named Transfer Join Method (TJM). Different from previous work, DSJ can (1) process multi-way θ-joins under arbitrary predicates; (2) preserve the integrity of results and load balance while distributing tuples to different nodes for parallel joining; (3) carry out the join operation in a local optimum order according to the histograms maintained in a real-time way. We have built DSJ on our own stream processing cluster to deal with multi-way streams joins and the experiments demonstrate that our DSJ can not only guarantee the load balance among all the computing nodes but also improve the throughput effectively.

The measurement matrix of compressed sensing has a significant impact for sampling and reconstruction algorithm of the original signal. At present, the majority of the measurement matrix is randomly constructed, and it is difficulty for hardware implementation in the practical applications. In this paper, we use the sparse characteristic of parity- check matrix of LDPC codes, construct measurement matrix based on QC-LDPC (Quasi-cyclic low-density parity-check) matrix, which is a structural and sparse deterministic measurement matrix. The simulation results show that, the measurement matrix is proposed in this paper not only can obtain a better reconstructed image quality, but also it can reduce the complexity of hardware implementation for quasi-cyclic.

Genetic algorithm (GA) is a population-based approach for heuristic search in optimi- zation problems based on the principle of biologic evolution and natural selection. In this paper, we present a hybrid adaptive genetic algorithm with chaos searching technique for numerical optimization. On the one hand, two sets of crossover and mutation rates are for- mulated to automatically maintain the balance between exploration and exploitation during the genetic search process. On the other hand, the chaos searching technique is introduced into the adaptive genetic algorithm based on the decision mechanism for premature conver- gence adopted in this paper, whose main goal is to avoid being trapped into the local opti- mum. In addition, half of the total evolutionary generation is utilized as one of the decision conditions so as to speed up the convergent process. To validate the effectiveness and efficiency of the proposed approach, we apply it to four benchmark functions obtained from the literature, and the experimental results show that the proposed algorithm can find global optimal or the closer-to-optimal solutions and have faster search speed as well as higher convergence rate.

Data layout is an important issue which aims at reducing data movements among data centers to improve the efficiency of the entire cloud system. This paper proposes a data-intensive application oriented data layout algorithm. It is based on hierarchical data correlation clustering and the PSO algorithm. The datasets with fixed location have been considered, and both the offline strategy and the online strategy for data layout have been given. As this proposed strategy is aimed at reducing the global amount of data transmissions, and the special permission of the datasets has been introduced, the cost of data transmission can be measured more reasonable. Simulation results show that compared with two classical strategies, our algorithm can reduce the amount of data transmission more effectively.

In wireless sensor networks, network connectivity problems and sensor coverage are mainly affected by the restricted communication and sensing range of the nodes. To solve all these problems, many studies have been undertaken till now but most of these works is limited to be applied to the real environments because these do not consider various environmental factors that affect wireless sensor network deployment. In this paper, we propose a node deployment strategy that considers environmental factors and the number of nodes for surveillance .Deployment of the sensor nodes, and relay nodes considering all features of the surveillance and reconnaissance sensor networks as well as environmental factors. Smarty deploying the sensor nodes is very important if we are considering the performance of wireless sensor nodes (wsn). This paper focuses on real time deployment of sensor nodes using unmanned aerial vehicle (UAV). Smart deployment has importance in battlefield surveillance, disaster monitoring, environmental and habitat monitoring etc. It plays a very important role mainly in disasters affecting areas like flood affected or fire affected forests for sensing different conditions and physical data out there. The objective of this paper is to deploy sensor nodes smartly only in terrain of interest not in fire or water so that it can conserve environment and can reduce the loss of sensor nodes from being deploy in fire or water. Image segmentation of the images captured by camera situated on a board of UAV.. Comparison of these optimization tools SA and GFA are presented here for different images. Simulation results show that GFA performs better than SA when compared with no. of individuals and SA is faster than GFA.

Mobile Ad hoc Networks (MANETs) are non-infrastructure networks consisting of mobile nodes. Since the mobile nodes have limited battery power, they are very important to using energy efficiently in MANETs. In order to maximize the lifetime of MANET, traffic should be sent via a route that can avoid nodes with low energy while minimizing the total transmission power. This paper proposes an Energy Entropy-based minimum Power cost Multipath routing algorithm in MANET (EEPMM). It is typically proposed to increase the reliability of data transmission or to provide load balancing. In simulation experiments, we compare EEPMM routing protocol with SHM and MEA-DSR routing protocol, in terms of the network lifetime, and the energy consumption when a packet is transmitted. The performance results indicate that the proposed scheme is quite adaptive for energy-efficient communication in MANETs.

WSNs consist of many low-cost autonomous sensors that are equipped with a small battery, data processing capabilities, and short-range wireless communication interfaces. They are often deployed for monitoring and surveillance of certain phenomena of interest. In this paper, we propose a fuzzy Cmeans-based clustering scheme which improves the network lifetime by creating symmetric clusters. It improves the random process of selecting the clusterhead nodes and by creating symmetric clusters reduces the total distances of intra-cluster communications. Thus, because, sensed data are transmitted to the closer clusterheads, our proposed clustering solution can prolong the network lifetime.

MapReduce, as a popular programming model for processing large data sets, has been widely applied. MapReduce 2.0 (MRV2) is a newly adopted one, which has a better performance. Those machines which have a lower performance in a cluster usually play a role who pull down the pace of job execution time. Speculative execution known as an approach for dealing with the above problems works by backing up those tasks running on a low performance machine to a higher one. Although multiple speculative execution strategies have been proposed, there are still a lot of pitfalls existing in the strategies. In this paper, Some pitfalls in proposed strategy have been modified and computer hardware has been taken into consideration (HWC-Speculation). In Hadoop-2.6, we have implemented it, called Hadoop-HWC. Experiment results show that our method can find a slow task correctly, also, the performance of MRV2 is improved.

The protocols of Wireless Sensor Networks (WSNs) can be categorized into two classes: proactive network protocol like CTP and reactive network protocol like TEEN. The former periodically collects data, working well in the applications of collecting data persistently. However, it can’t dynamically alter the transmission frequency and the routing selecting strategy. The latter can react to the environment change, so it is widely used in the applications requiring nodes to be reactive to some emergencies. But it only works in reactive networks without collecting data under normal circumstance. We find that dynamically altering the transmission frequency and the routing selecting strategy according to the environment change is very effective and significant. For the reasons above, this paper presents TDRHN protocol. It periodically collects data in normal situation and can not only take faster transmission frequency but also alter the routing selecting strategy when it detects some emergencies. So TDRHN can effectively work in hybrid networks combining proactive networks and reactive networks. Finally, we evaluate TDRHN on the testbed with TelosB motes in real deployment. The concluded results of the experiments demonstrate that TDRHN has some particular advantages.

This paper proposes a generalized cluster risk evaluation model by applying a data mining method to the cloud computing risk evaluation. The model maps data sets into a semantic space via singular value decomposition (SVD), uses a clustering algorithm to classify them and to extract the prototype vector of a particular category from clustering results, and assigns a definite weight to each category so as to set up an initial prototype vector model. The model is taken as the basis for risk evaluation of information system. After the data to be evaluated were mapped to the same semantic space, they are calculated with the prototype vector of each category, so as to obtain the similarity of the category, and the cumulative sum of the similarity with the weight of the corresponding category comes out. Finally, a mean value is calculated to obtain the risk value of the data to be evaluated, namely, the risk value of the occasion when the data is obtained. In this paper, the safety risk information is obtained from the operating system log and Web application server log of a virtual host; the Latent Semantic Analysis-based Generalized Cluster Classifier (LSA-GCC) is adopted and the MapReduce-based LSA-GCC and LSA-SAM parallel acceleration experiment is conducted. The experimental results show that in a cloud computing environment of large-scale parallel processing, the method used in this paper can identify the log events of a cloud computing system and conduct risk prompt rapidly.

Mobile Adhoc Networks (MANETs) are the self-organized networks in which connections are not established for exchange the information. In MANET, there are major problems like scalability, dynamic topology, high mobility and routing. The network can be damaged due to the high mobility. Topology based routing can fail when there is a dynamic change in the network topology. To avoid these problems, Geographic routing is used. The geographic routing protocols are more efficient and scalable when there is a dynamic change in the network topology and when the mobility is high. In this paper, we have surveyed on the Hybrid Routing and Geographic routing protocol. The hybrid routing can be done into two ways i.e. greedy routing and face-2 algorithm or perimeter routing.

Message transmission in opportunistic networks is accomplished via the encounters of mobile nodes while moving around. The distributing of nodes greatly impacts the performance of message delivery ratio due to their sparse encounter opportunities. Nodes with exhaust energy can’t participate in message transfer process. So it is very meaningful to make nodes energetic and balance the energy consumption between nodes. In this paper, a novel dynamic irregular cellular multiple learning automata (DICMLA) model and the corresponding routing algorithm are proposed to optimize the energy consumption of nodes. The proposed routing algorithm utilizes the characteristics of cellular learning automata to reduce the energy consumption of nodes and improve the delivery ratio of message transmission. The simulation results show that the proposed algorithm can obviously balance energy consumption of nodes and thus prolong the lifetime of the network.

Installing distributed generators can effectively reduce network power consumption and improve power quality in the distribution network, but it will also bring some technical problems. Therefore, we need to study how to efficiently and quickly determine the optimal investment capacity of distributed generators in the distribution network. In this paper, we set the minimization of the power loss as the objective and establish the most investment scale decision-making model of distributed generation access to power distribution system. In terms of optimization algorithm, optimization time of traditional sequential quadratic programming will dramatically increase with the increasing number of variables and restraints. In order to pursue higher efficiency optimization, we apply the improved sequential quadratic programming that is fast sequential quadratic programming method for solving nonlinear problems of distributed generators optimal investment size. We apply the constructed model to systems with different nodes, and the results show that the proposed new optimization method is not only scientific and effective, and can significantly improve decision-making efficiency.