iosr-JCE   Volume-16 ~ Issue-4 ~ July-August 2014

Abstract: Wireless Mesh Network (WMN) is a wireless network with mesh topology and is expected to be widespread due to the advantages such as low deployment cost, easy maintenance, robustness, scalability, reliable service coverage and high performance. It has self-configuring and self-healing ability and is compatible, interoperable with existing wireless networks. With these advantages, WMN inherits security issues that need to be considered before the deployment and proliferation of the network, as it is unappealing to subscribers to obtain services without security and privacy. It is difficult to achieve both, privacy and security together in a system but an attempt is made by designing a ticket-based security architecture achieving anonymity and traceability in WMN. Anonymity provides protection to the users to access network services without being traced i.e. preserving the identity of the user and has been extensively studied in payment-based systems such as e-cash and peer-to-peer systems. When anonymity is achieved, few entities tend to misbehave by imposing attacks as they remain anonymous. This affects network security and hence, misbehaving entities have to be traced. The clustering concept is included in the system to increase network performance and to reduce topology updating overhead in the network.

Keyword: Anonymity, traceability, pseudonym, misbehavior, clustering.

[1] I. F. Akyildiz, X. Wang and W.Wang, Wireless Mesh Networks: A Survey, Computer Networks, vol. 47, no. 4, pp.445-487, Mar. 2005.

[2] y. Zhang and Y. Fang, ARSA: An Attack-Resilient Security Architecture for Multihop Wireless Mesh Networks, IEEE J. Selected Areas Comm., vol. 24, no. 10, pp. 1916-1928, Oct. 2006.

[3] Jinyuan Sun, Chi Zhang, Yancho Zhang, Yuguang Fang, SAT: A Security Architecture Achieving Anonymity and Traceability in Wireless Mesh Network, IEEE Transaction on Dependable and Secure Computing, March-April 2011.

[4] X. Chen, F. Zang and S. Liu, ID-Based Restrictive Partially Blind Signatures and Applications, J. Systems and Software, vol. 80, no. 2, pp. 164-171, Feb. 2007.

[5] A. R. Beresford and F. Stajano, Location Privacy in Pervasive Computing, IEEE Pervasive Computing, vol. 2, no. 1, pp. 46-55, Jan.-Mar. 2003.

Abstract : Wireless mesh network is an emerging technology progressing in the field of wireless networking. The broadcast nature of wireless mesh network allows each router to search destination and forward the packet. Wireless mesh network is based on IEEE 802.11 standard according to which the received packet must be acknowledged and the routers unacknowledged continue broadcasting packet in their range. This leads to signaling overhead and decreases throughput of the network. The proposed work overcomes this signaling overhead in addition to the design of a routing metric, expected forwarded counter [EFW] which deals with the problem of selfish behavior of mesh routers in wireless mesh network. EFW considers forwarding behavior of node and wireless link quality to select the most reliable and high performance path. The proposed system is evaluated by performing comparative analysis with the existing system by considering performance metrics like packet delivery ratio, average throughput, bandwidth utilization. The proposed work shows significant increase in throughput with efficient bandwidth utilization and the metric EFW selects a path with highest delivery rate, considering both quality of wireless links and reliability of network node in wireless mesh network.

Keywords: Data dropping, metrics, broadcasting, wireless mesh network.

[1] I.F Akyildiz and Wang, A Survey on Wireless Mesh Networks (IEEE commun.Mag vol. 43, no.9, pp.s23-s30, sep.2005).

[2] Sakhapure S.S.,Channe H, A Cross Layer Routing Approach In Self Configurable Wireless Mesh Networks, International journal of engineering research and technology (IJRET) vol.1 Issues 8,October-2012 ISSN:2278 -0181.

[3] Stefano Paris,Cristina Nita –Rotaru, Fabio Martignon and Antonio Capone, Cross Layer Metrics For Reliable Routing In Wireless Mesh Network.

[4] MdAsri Bin NgadiSaquib Ali Abdul Hanan Abdullah and Rashid Hafeez Khokhar, A Taxanomy Of Cross Layer Routing Metrics for Wireless Mesh Network.

[5] Jaydip Sen. and Kaustav Goswami, An Algorithm for Detection of Selfish Nodes in Wireless Mesh Network,Innovations labs TCS. [6] Xiaolin Cheng, Prasant Mohapatra, Retransmission-Aware Queuing and Routing For Video Streaming In Wireless Mesh Networks

Abstract : Face detection is one of the challenging problems in the digital image processing. Digital images have an enormous information and characteristics measures. But until today, a complete capable mechanism to extract these characteristics in an automatic way is so far unknown. Referring to facial images, its detection in an image is a problem that requires a thorough investigation due to its high complexity. Face detection is an important application of visual object detection and it is one of the main components of face analysis and understanding with face localization and face recognition. Here the investigation aspects of genetic Algorithms (GA's) in face recognition are characterized as one of search technique. GA is efficient technique in reducing computational time for a huge stack space. Face recognition from a very huge stack space is a time consuming job hence GA based approach is used to recognize the unidentified image within a short duration of time. This work analyzes the work done by distinguished authors previously and throws light on what next is to be done. Though they don't give exact and accurate results but are very efficient in time bound recognition for very huge databases. For promptness on a random pickup base it gives fastest result. GA is used when user has no time or less time for giving results without going for check related to each database containing facial images. Feature extraction along with GA will prove better for quicker face recognition. The basic plan of face detection is to determine if there is any face in an image and then locate a position of face in an image. Human face detected in an image can represent the presence of a human in a place. Obviously, face detection is the first step towards creating an automated system, which may involve other face processing. A novel face detection system is presented in this work. Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that is either rotated along the axis from the face to the observer or rotated along the vertical or left-right axis or both. The newer algorithms take into account variations in the image by factors such as face appearance, lighting, and pose.

Keywords: ACOG, image, genetic, FRS, Performance

[1]. M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004.

[2]. Image Edge Detection Using Ant Colony Optimization Anna Veronica Baterina and Carlos Oppus International journal of circuits, systems &signal processing

[3]. Rafael C. Gonzalez and Richard E Woods, "Digital Image Processing", Person Education Asia.

[4]. M. D. Malkauthekar and S. D. Sapkal, "Experimental Analysis of Classification of Facial Images," IEEE International Advance Computing Conference, March 2009.

[5]. Praseeda Lekshmi.V and M. Sasikumar, "RBF Based Face Recognition and Expression Analysis," Proceedings of World Academy of Science, Engineering and Technology, vol. 32, pp. 589-592, August 2008.

[6]. Charles L. Karr and L. Michael Freeman, "Industrial Appl. of Genetic algorithms".

Abstract : Cloud Computing offers latest computing paradigm where application, data and IT services are provided online over the Internet. One of the significant concerns in Cloud Computing is security. Since data is exposed to many users, security and privacy have become the key issues of Cloud Computing. Intrusion Detection System (IDS) plays an important role to identify intrusions by monitoring the activity of the system and alert the user about malicious behaviours and detect attacks. To detect those attacks, several classification methods have been used till now. This paper deals with Intrusion Detection System by the method of classification. In this paper, KNN is applied as binary classifier for anomaly detection. Neural Network is applied for detecting abnormal classes after KNN classification. Before classification, feature selection has been used to select relevant features. For our experimental analysis, we have used NSL-KDD dataset where all samples of "KDDTrain+" used as training dataset and "KDDTest+" samples are used as testing dataset. We use Rough Set Theory and Information Gain to select relevant features. Experimental results show that, we get better accuracy with our proposed hybrid KNN_NN classifier model for Intrusion Detection.

Keywords: Information Gain (IG), Intrusion Detection System (IDS), K-Nearest Neighbor (KNN), Neural Network (NN), NSL-KDD dataset, Rough Set Theory (RST).

[1] Manthira M. S, Rajeswari M. Virtual Host based Intrusion Detection System for Cloud. International Journal of Engineering and Technology (IJET), Vol.5, No.6, pp.5023-5029(2013).
[2] Araujo J. D, Abdelouahab Z. Virtualization in Intrusion Detection System: A Study on Different Approaches for Cloud Computing Environments. International Journal of Computer Science and Network Security (IJCSNS), Vol.12, No.11, pp.9-16(2012).
[3] Modi C, Patel D, Borisaniya B, Patel H. A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, Vol.36, pp.42-57(2013).
[4] Majeed S. K, Hashem S. H, Gbashi I. K. Propose HMNIDS Hybrid Multilevel Network Intrusion Detection System. International Journal of Computer Science Issues (IJCSI), Vol.10, Issue.5, No.2, pp.200-208(2013).
[5] Garcia-Teodoro P, Diaz-Verdejo J, Macia-Fernandez G, Vazquez E. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, Vol.28, pp.18-28(2009).

Abstract : Non-restoring division method originally defined by Robertson in 1958. Restoring and non-restoring division processes are the algorithms conventionally used to program division method on microprocessors to minimize the hardware cost. A new hardware algorithm is to be proposed for non-restoring division algorithm for nonnegative integers. Restoring division algorithm maximizes the hardware cost where as non-restoring division algorithm minimizes the hardware cost.

Keywords: Register, Accumulator, ALU, Shifting Operation, Non-restoring division algorithm, Hardware minimization.

[1] J. E. Robertson, "A new class of digital division methods," IRE Trans, of Elec. Comp., Vol. EC-7, No.3 (Sept. 1958), pp. 218-222.
[2] Patterson, D.A and Hennessy, J.L, "Computer Organization and De- sign: The Hardware/Software Interface", 1993 San Mateo, CA: Morgan Kaufmann Publishers, Second Edition, 1998.

[3] Stallings, W, "Computer Organization and Architecture (Designing for Performance)", Upper Saddle River, New Jersey: Prentice Hall Publishers, Fifth Edition, 1999.

[4] Daniel E. Atkins, "HIGHER RADIX, NON-RESTORING DIVISION: HISTORY AND RECENT DEVELOPMENTS", Computer Arithmetic (ARITH), IEEE 3rd Symposium, pp. 158-160, 1975.

[5] Jack Bukholdt Andersen, Anders Fzrgemand Nielsen, Ole Olsen, "A Systolic ON-LINE Non-restoring Division Scheme", Proceed-ings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences, IEEE, 1994

Abstract : Data mining is gaining importance due to huge amount of data available. Retrieving information from the warehouse is not only tedious but also difficult in some cases. The most important usage of data mining is customer segmentation in marketing, shopping cart analyzes, management of customer relationship, campaign management, Web usage mining, text mining, player tracking and so on. In data mining, association rule mining is one of the important techniques for discovering meaningful patterns from large collection of data. Discovering frequent itemsets play an important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. This paper presents a literature review on different techniques for mining frequent itemsets. Keywords: Association rule, Data mining, Frequent itemsets

[1] Aggaraval R; Imielinski.t; Swami.A. 1993. Mining Association Rules between Sets of Items in Large Databases. ACM SIGMOD Conference. Washington DC, USA.
[2] S.Vijayarani el.al., "Mining Frequent Item Sets over Data Streams using Éclat Algorithm" International Conference on Research Trends in Computer Technologies (ICRTCT-2013).
[3] Bharat Gupta et.al., "A Better Approach to Mine Frequent Itemsets Using APRIORI AND FP-TREE Approach" .
[4] Jian Pei , Jiawei Han , Hongjun Lu , Shojiro Nishio , Shiwei Tang , Dongqing Yang "H-Mine: Hyper-StructureMining of Frequent Patterns in Large Databases".
[5] Margaret H. Dunham, Yongqiao Xiao, Le Gruenwald, Zahid Hossain "A Survey of Association Rules".
[6] Hassan Najadat , Amani Shatnawi and Ghadeer Obiedat "A New Perfect Hashing and Pruning Algorithm for Mining Association Rule" IBIMA Publishing.

Abstract : Traffic signs should be accurately identified in order to prevent vital road accidents and secure lives. The objective of this paper is to detect the warning traffic signs and recognize the message it is designed to convey. This system is based on extracting the warning sign from the traffic scene by Windowed Hough Transform. Next Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of triangular object and finally SVM classifier is applied to train the HOG features. To summarize, the system first detects the warning traffic sign in the first place, specifies whether the detected sign is a warning sign, and then determines the meaning of the symbol inside it. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 327 images which contain 292 warning traffic sign and 35 other types of traffic signs. It was found that the accuracy of recognition was approximately 94% which indicates clearly the high robustness targeted by this system.

Keywords: HOG, SVM, Traffic sign; Windowed Hough Transform.

[1] Fleyeh, "Color detection and segmentation for road and traffic signs", in 2004 IEEE Conf. on Cybernetics and Intelligent Systems, Singapore, 2004, pp. 808-813.
[2] L. Estevez and N. Kehtarnavaz, "A real-time histographic approach to road sign recognition," in IEEE Southwest Symposium on Image Analysis and Interpretation, San Antonio, Texas, 1996, pp. 95-100.
[3] A. de la Escalera, et al., "Neural traffic sign recognition for autonomous vehicles," in 20th Inter. Conf. on Industrial Electronics Control and Instrumentation, Bologna, Italy, 1994, pp. 841-846.
[4] Hsiu-Ming Yang, Chao-Lin Liu, Kun-Hao Liu, and Shang-Ming Huang, "Traffic Sign Recognition in Disturbing Environments", Department of Computer Science, National Chengchi University.
[5] Vincenzo Barrile, Giuseppe M. Meduri and Domenico Cuzzocrea,"Automatic Recognition of Road Signs by Hough Transform: Road-GIS", Journal of Earth Science and Engineering 2 (2012) 42-50

Abstract : Travel time prediction plays an important role with the development of ATIS (Advanced Travelers Information Systems).It provides useful information which may allow travelers to change their routes and to decide whether or not to make necessary changes to their routes or departure times. Here we proposed a new method ATA(Adjacent total average). The challenge of this paper is to provide accurate travel time to compare with others method .With the use of same set of historical travel time and comparing the previous results of other four methods like Successive Moving Average (SMA), Naïve Bayesian Classification (NBC) method, Switching method and K –means Clustering(MKC) Our algorithms exhibit high accuracy in predicting travel time.

Keywords: Intelligent transportation system, travel time prediction, Successive moving average, NBC method, Switching method.

[1]. Chen, M., Chien, S.: Dynamic freeway travel time prediction using probe vehicle data: Link-based vs. Path-based. J. of Transportation Research Record, TRB Paper No. 01-2887, Washington, D.C. (2001)
[2]. Wei, C.H., Lee, Y.: Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data. IEEE Transactions on Vehicular Technology 56(2007)
[3]. Kwon, J., Petty, K.: A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. In: Transportation Research Board 84th Annual Meeting, Washington, D.C (2005)
[4]. Park, D., Rilett, L.: Spectral basis neural networks for real-time travel time forecasting. J. of Transport Engineering 125(6), 515–523 (1999)
[5]. J. Kwon, B. Coifman and P. Bickel, Day-to-day trave time trends and travel time prediction from loop detector data, Journal of Transportation Research Record, No. 1717, TRB, National Research Council, Washington, D.C., 2000, 120–129.

Abstract : Cloud building means virtualization of whole system like hardware and software. That is accessible through network at any time anywhere in this world. The resources of cloud are allocated to the requested users in dynamic manner , the resources are not statically given to a particular user. In this paper we present a system that uses virtualization technology to balance the load on a physical machine. Overloading can be minimized using virtual machines to handle jobs. The green computing allows energy saving in a network. Errors while transferring data in cloud can be avoided by implementing fault tolerance.

Keywords: Cloud computing, fault tolerance, green computing, VMware workstation, virtualization.

[1]. Dynamic Resource Allocation Using VM for cloud computing enviornment, Zhen Xiao, Senior Member, IEEE, Weijia Song, and Qi Chen ,IEEE transactions on parallel and distributed system, VOL. 24, NO. 6, JUNE 2013.
[2]. Cloud Bulding on core-i3 using Vmware Workstation, International Conference on Pervasive Computing and Communication 2012

[3]. Live Migration Of Virtual machine, May 2005 Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hanseny,Eric July, Christian Limpach, Ian Pratt, Andrew Wareld
[4]. Memory resource management in Vmware ESX server, Carl A. Waldspurger, IEEE August 2002
[5]. Energy-Aware Server Provisioning and Load Dispatching for Connection intensive Internet services, Gong Chen, Wenbo He, Jie Liu, Suman Nath, Leonidas Rigas, Lin Xiao, Feng Zhao,August 2008

Abstract : The human is the most significant success factor in any organization, or project. The main purpose of this research is to introduce a proposed framework for enhancing the technical team management in Information Systems (IS) projects; the proposed framework is helpful for project managers, and leads to project success, and delivered within planned time, budget, and acceptable quality. The proposed framework contains a set of components which may help to enhance the human management in software projects. Most of published studies in the area of IS projects have shown that; the technical team can be one from the most causes of the project failures[1], so we are trying to contribute in this area of IS projects management. In this research we had started to survey in IS projects management, then focused on technical team problems, such as team selection, team evaluation, tasks allocation, and study how the published researches were tried to provide solutions. After that we have collected the most significant problems, and then we developed our proposed framework. The proposed framework has overcome some of these problems. The framework covered the project life cycle phases; because the management activates extended along and cover all life cycle phases. In order to evaluate our proposed framework the evaluation processes done through a real case studies on four live IS projects in Egypt; the results of the cases study conclude that the more applying of the proposed framework, the more leading to project success.
Keywords: Software Project Management,Technical Team Management (TTM), Software Projects (SW).

[1] MindTools.com. 10 Common Time Management Mistakes Avoiding Common Pitfalls Available: http://www.mindtools.com/pages/article/time-management-mistakes.htm#sthash.vmkpaYa6.dpuf
[2] I. m. article. (2012) "Why Software Fails". IEEE. Available: http://spectrum.ieee.org/computing/software/why-software-fails/3
[3] I. Sommerville. (2011). Software engineering (Ninth Edition ed.).

[4] A Guide To The Project Management Body Of Knowledge (PMBOK Guides): Project Management Institute, 2004.
[5] F. Heyworth and E. C. f. M. Languages, A Guide to Project Management: Council of Europe Publishing, 2002.

Abstract : In Mobile Agent Technology, interoperability between agents is indispensably to secure the data from malicious agents under Multi-Agent System. To protect data and agents from malicious attacks, the multi-agent system essentially needs to offer secure communication and access control mechanisms. Hence the digital signature and cryptosystem of asymmetric key based encryption and decryption provides secure communication and increases the confidentiality of accessing services designated only to a determined group of users. However, for the distribution of public key between agents we need to identify the trusted agent. The identification of trusted agent in a multi-agent platform is a challenging work. The technique of adapting USB Dongle is like a security device, which makes the identity of trusted agent, gives a robust mechanism for the identification of trusted agents in a Multi-Agent secured Distributed Computing System. In addition to that bio-metric based finger print sensor enables the owner's physical contribution to access the data. Keywords: Asymmetric Key Cryptosystem, Authentication, Authorization, Dynamic Key Distribution, Hardware Identity Module (HIM), Multi-Agent System (MAS).

[1] Fabio Bellifemine, Glovanni Caire, Dominic Greenwood, Developing Multi-Agent System with JADE (John Wiley & Sons Ltd, 2007).

[2] Rodolfo Carneiro Cavalcante, Ig Ibert Bittencourt, Alan Pedro da Silva, Marlos Silva, Evandro Costa, Roberio Santos, A survey of security in multi-agent systems, Expert Systems with Applications 39 (2012), 4835–4846.

[3] Vanderson Botelho, Fabricio Enembreck, Braulio Avila, Hilton de Azevedo, Edson Scalabrin, Using asysmmetric keys in a certified trust model for multiagent systems, Experts Systems with Applications 38(2011), 1233-1240.

[4] Kumaravelu R, Kasthuri N, Distribution of Shared Key (Secret Key) using USB Dongle based identity approach for authenticated access in Mobile Agent Security, Internation Conference on Communication and Computational Intelligence (2010), 558-562

[5] Salvatore Vitabile, Vincenzo Conti, Carmelo Militello, Filippo Sorbello, An extended JADE-S based framework for developing secure Multi-Agent Systems, Computer Standards & Interfaces 31 (2009), 913–930

Abstract : The study of Earth and its features with the help of Remote Sensing has lead to a fast growing and effective technology called Geographic Information System (GIS) where digital Maps and spatial data can be used for visualizing, analyzing and taking decision based on the analysis. Crime Analysis has become an integral part of GIS where the Analyst uses the spatial and temporal aspect of Crime information for analysis and forecasting. Using Hotspot analysis technique the Hotspot means the area where the concentration of Crime is more can be found. There are different classes of Hotspot detection such as Spatial Analysis, Interpolation and Spatial Autocorrelation for finding out the Crime Hotspot. In this paper Kernel Density Estimation, Inverse Distance Weighted and Getis-Ord Gi* methods from each classes respectively are discussed. These methods are applied on Aurangabad city of Maharashtra state, India for finding out the hotspots for crime incidence like Murder, Day House Break and Night House Break.

Keywords: Crime Mapping, Getis-Ord Gi*, GIS, Hotspot, KDE

[1] Kan-tsung Chang; Introduction to Geographic Information System (4th Edition, Tata McGraw-Hill, Eleventh Reprint 2012).
[2] Rachel Boba; Crime Analysis and Crime Mapping (Sage Publications, Inc., printed in United States of America, 2005).

[3] http://aurangabadcitypolice.gov.in , 19-09-2013.

[4] Nagne Ajay D., and Bharti W. Gawali; Transportation network analysis by using Remote Sensing and GIS a Review; IJERA, 2013.
[5] M. Ahmed and R. S. Salihu; Spatiotemporal Pattern of Crime Using Geographic Information System (GIS) Approach in Dala L.G.A of Kano State, Nigeria; AJER, 2013