A Framework for Secure and Survivable Wireless Sensor Networks

Wireless sensor networks increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future. A wireless sensor network (WSN) is vulnerable to security attacks due to the insecure communication channels, limited computational and communication capabilities and unattended nature of sensor node devices, limited energy resources and memory. Security and survivability of these systems are receiving increasing attention, particularly critical infrastructure protection. So we need to design a framework that provide both security and survivability for WSNs. To meet this goals, we propose a framework for secure and survivable WSNs and we present a key management scheme as a case study to prevent the sensor networks being compromised by an adversary. This paper also considers survivability strategies for the sensor network against a variety of threats that can lead to the failure of the base station, which represents a central point of failure.key management scheme, security, survivability, WSN

Developing Word-aligned Myanmar-English Parallel Corpus based on the IBM Models

Word alignment in bilingual corpora has been an active research topic in the Machine Translation research groups. Corpus is the body of text collections, which are useful for Language Processing (NLP). Parallel text alignment is the identification of the corresponding sentences in the parallel text. Large collections of parallel level are prerequisite for many areas of linguistic research. Parallel corpus helps in making statistical bilingual dictionary, in supporting statistical machine translation and in supporting as training data for word sense disambiguation and translation disambiguation. Nowadays, the world is a global network and everybody will be learned more than one language. So, multilingual corpora are more processing. Thus, the main purpose of this system is to construct word-aligned parallel corpus to be able in Myanmar-English machine translation. One useful concept is to identify correspondences between words in one language and in other language. The proposed approach is based on the first three IBM models and EM algorithm. It also shows that the approach can also be improved by using a list of cognates and morphological analysis

Distributed Energy Efficient Cluster Formation for Wireless Sensor Networks

In large scale wireless sensor networksclustering is often used for improving energyefficiency and achieving scalable performance. In theclustered environment, data gathered by the nodes istransmitted to the base station through cluster-heads(CHs). As the nodes will communicate data overshorter distances in such an environment, the energyspent in the network is likely to be substantiallylower compared to when every sensor communicatesdirectly to the base station. The essential operationin sensor node clustering is to select a set of clusterheadsfrom the set of nodes in the network, and thencluster the remaining nodes with these heads. In thispaper, we propose a distributed energy efficientcluster formation (DEECF) algorithm for wirelesssensor networks. The cluster-head selectionalgorithm of DEECF is extended the LEACH’sstochastic cluster-head selection algorithm byconsidering the additional parameters, the residualenergy of the node relative to the residual energy ofthe network. We also compare our DEECF withLEACH in terms of network lifetime. The simulationresults demonstrate that DEECF can achieve highenergy efficiency and prolong network lifetime

A Framework for Secure and Survivable Wireless Sensor Networks

Wireless sensor networks increasingly become viable solutions tomany challenging problems and will successively be deployed in many areas inthe future. A wireless sensor network (WSN) is vulnerable to security attacksdue to the insecure communication channels, limited computational andcommunication capabilities and unattended nature of sensor node devices,limited energy resources and memory. Security and survivability of thesesystems are receiving increasing attention, particularly critical infrastructureprotection. So we need to design a framework that provide both security andsurvivability for WSNs. To meet this goals, we propose a framework for secureand survivable WSNs and we present a key management scheme as a case studyto prevent the sensor networks being compromised by an adversary. This paperalso considers survivability strategies for the sensor network against a variety ofthreats that can lead to the failure of the base station, which represents a centralpoint of failure

CPU Usage Prediction Models for Virtualized Data Center

Resource allocation plays an important role inVirtualized Data Center (VDC). The applicationsrunning in VDC are mostly business criticalapplications with Quality-of-Service (QoS)requirements. Moreover, dynamic resource allocationand real time monitoring of the resource usage of VMsare also needed to reduce under resource utilization andover resource utilization. Therefore, resource usageprediction is required for dynamic resource allocationsystems. In efficient dynamic resource allocation, theresources are allocated to a VM while meeting theirService Level Agreement (SLA). The main contributionof this work is two-fold. The first is the generation ofCPU usage prediction models by applying differentpowerful machine learning techniques. The second isSLA evaluation on predicted value by using proposedSLA metric. To evaluate the efficiency of these models,experiments are carried out by using CPU profiles fromreal world data centre. According to the experiments,proposed resource prediction models have promisingaccuracy

Enhancing NameNode Fault Tolerance in Hadoop Distributed File System

In today’s cloud computing environment, Hadoop is applied for handling huge data, tens of terabytes to petabytes, with commodity hardware (HDFS) for storage and software (MapReduce) for parallel data processing. In Hadoop version 1.0.3, there is a single metadata server called NameNode which stores the entire file system metadata in main memory and most of I/O operations are associated with those credential metadata. Hadoop is out of commission if NameNode is crashed because it works on memory which becomes exhausted due to multiple concurrent accesses [3]. Therefore, NameNode is a single point of failure (SPOF) in Hadoop and it has to tolerate faults. To solve this issue, a proactive predictive solution is proposed for enhancing NameNode fault tolerance. The solution is designed to proactively calculate the predicted time to crash of NameNode due to resource exhaustion by evaluating the use of powerful Back Propagation Algorithm Neural Network. The proposed approach can give prediction accuracy with minimal error compared to the actual result. Therefore, NameNode’s single point of failure can overcome through proposed proactively predicting the time to crash of NameNode caused by memory resource exhaustion

Local Aggregation with Modified B+ tree in Map Reduce Data Processing

MapReduce is well-applied in high performance computing for large scale data processing. However, as long as the clusters grow, handling with huge amount of intermediate data produced in the shuffle and reduce phases (middle step of Map Reduce) have impacts heavily upon the performance. With local aggregation (either combiners or in-mapper), shuffling large amounts of data can be reduced which alleviates the reduce straggler problem. The proposed modified B+ tree based indexing algorithm is applied to reduce intermediate data amount for output retrieval fast as well as scalable data storage

Elastic Resource Prediction for Cloud Data Center

Cloud data centers offer utility-oriented ITservices to users worldwide. The nature ofresource demand of cloud data centers is elasticas the overall workloads are always changing.For handling dynamic workload nature ahead ofthe needs, elastic resource demand prediction isthe key issue in cloud data centers. If the cloudprovider does not ensure they have enoughresources to meet demand which will lead tounder or over provisioning of resources. In thispaper, integrated elastic resource predictionsystem is proposed by combining signaturebasedprediction and state-based predictionapproahces. The workload nature of the clouddata centers are both repeating pattern and nonrepeatingpattern workload. Signature-basedprediction is used to predict the repeatingpattern workload and state-based prediction isused to predict the non-repeating patternworkload. Integrated Elastic ResourcePrediction (IERP) system is used to predict themixed workload pattern. Feature selection isconducted first to reduce processing overheadswhile achieving high prediction accuracy. Theproposed predictors are implemented andevaluated with real world workload traces whichshow that they achieve high resource prediction accuracy with above 95%

Ensuring Reliability in Deduplicated Data by Erasure Coded Replication

As computer systems are taking more and more responsibilities in critical processes, the demand for storage is increasing due to widespread applications. Saving the digital information in a large disk is expensive and unreliable. As a result, if the disk fails all the data is lost. Therefore, the yearning for a better understanding of the system’s reliability is ever increasing. In greatest hit storage environments, deduplication is applied as an effective technique to optimize the storage space utilization. Usually, the data deduplication impacts the bad result for the reliability of the storage system because of the information sharing.In this paper, reliability guaranteed deduplication algorithm is proposed by considering reliability during the deduplication process. The deduplicated data are distributed to the storage pool by applying the consistent hash ring as a replicas placement strategy. The proposed mechanism is evaluated and the result is compared with pure replication and erasure coded replication. The proposed mechanism can provide the better storage utilization and the one hundred percent of assurance for demanded reliability level in compared with the existing systems

Comparative Analysis of Site-to-Site Layer 2 Virtual Private Networks

Nowadays, many companies have branchoffices and connect those offices to the main office overthe Internet using a site-to-site Virtual Private Networkconnection. Most of these connections have alwaysoperated at Layer 3 of the OSI network model. In recentyears, there has been a growing requirement to extendlinks at Layer 2, which allows broadcast traffic to beforwarded between sites. Depending on inter-siteconnection medium, different technologies are utilized.This paper compares and analyses site-to-site Layer 2VPN technologies, which include layer 2 tunnelingprotocol (L2TP), and point to point tunneling protocol(PPTP), OpenVPN, Ethernet over IP (EoIP), andMPLS/VPLS to choose the right VPN for theorganization. This is done by means of performancemeasurement and packet analysis. In order to providefair comparable results, all technologies are tested inthe same manner