iosr-JCE   Volume-16 ~ Issue-5 ~ Sep-Oct 2014

Abstract: The Wireless Sensor Network (WSN) is composed of sensors. These sensor nodes sense the physical parameters like temperature, pressure, humidity etc. In real time environment these sensors have different energies. In this paper we have assumed three types of sensor nodes. Now a day's most researchers are focusing on how to increase the network lifetime through efficient use of the energy. Generally the network is divided into a certain number of clusters and then a cluster head is selected from all these sensors randomly. There is a Base Station (BS) or Sink which receives aggregated data from the cluster heads and sends it to the end computer. In the existing protocols the sink is kept fixed which results the problem of data collection from the sensor node's situated at WSN boundary. In our proposed Extended Stable Election Protocol (ESEP) the sink is kept mobile. To increase the lifetime of the network the concept of reliable cluster head is used. The reliable cluster head will act as secondary cluster head in case the main cluster head would die. We have used a new approach to make the cluster head selection more deterministic i.e. sorting technique which sorts the nodes in the descending manner. The performance evaluation of the proposed ESEP is done on the basis of the First Node Dead (FND), packets sent to base station, packets sent to cluster head and total Network Lifetime. The network simulator MATLAB is used for the simulation. The simulation results show that our proposed ESEP gives better result than the existing ESEP.

Keywords: Base Station (BS) or Sink, Cluster Head (CH), Extended Stable Election Protocol (ESEP), First Node Dead (FND), Reliable Cluster Head, Wireless Sensor Network (WSN)

[1] A. Ahlawat and V. Malik, An extended vice-cluster selection approach to improve V LEACH protocol in WSN, Proc. IEEE Third International Conference on Advanced Computing & Communication Technologies (ACCT), Rohtak, 2013, 236-240.

[2] A. Kashaf, N. Javaid, Z. Khan and I. Khan, TSEP: Threshold-sensitive stable election protocol for WSNs, Proc. IEEE 10th International Conference on Frontiers of Information Technology, 2012, 164-168.

[3] A. Khan, N. Javaid, U. Qasim, Z. Lu and Z. Khan, HSEP: Heterogeneity-aware hierarchical stable election protocol for WSNs, Proc. IEEE Seventh International Conference on Broadband, Wireless Computing, Communication and Applications, 2012, 373-378.

[4] D. Melese, H. Xiong and Q. Gao, Consumed energy as a factor for cluster head selection in wireless sensor networks, Proc. IEEE 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 2010, 1-4.

[5] G. Chen, X. Zhang, J. Yu, and M. Wang, An improved LEACH algorithm based on heterogeneous energy of nodes in wireless sensor networks, Proc. IEEE International Conference on Computing, Measurement, Control and Sensor Network, 2012, 101-104.

Abstract : In Distributed Real Time System (DRTS), systematic allocation of the tasks among processors is one of the important parameter, which determine the optimal utilization of available resources. If this step is not performed properly, an increase in the number of processing nodes results in decreasing the total system throughput. The Inter-Task Communication (ITC) is always the most costly and the least reliable parameter in the loosely coupled DRTS. In this paper an efficient task allocation algorithm is discussed, which performs a static allocation of a set of "m" tasks T = {t1,t2,…tm} of a program to a set of "n" processors P = {p1,p2,….pn}, (where, m >> n) to minimize the application program's Parallel Processing Cost(PPC) with the goal to maximize the overall throughput of the system through and allocated load on all the processors should be approximately balanced. While designing the algorithm the Execution Cost (EC) and Inter Task Communication Cost (ITCC) have been taken into consideration.

Keywords: Distributed Real time System, Execution Cost, Inter Task Communication Cost, Task Allocation, Load Balancing

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Abstract : Wireless Sensor Network has several issues and challenges that are to be taken care while designing the techniques and algorithms to increase the Network lifetime of WSN. Network optimization is a critical component and optimization techniques are used to achieve the design goals in Networking. With several optimization algorithms existing to suit different problems, choosing a proper algorithm is very important in any optimization technique. In this paper we present the summary of different optimization techniques incorporating Evolutionary model and their approach in the different areas of Wireless sensor Network. These evolutionary techniques are studied from the Environment and are used to incorporate similar behavior in Wireless Sensor Networks. This Paper includes the discussion of few other papers which uses such Evolutionary model in different areas of optimization.

Keywords: Energy Efficiency, Evolutionary model, Network Lifetime, Optimization technique, Wireless sensor Network.

[1] Jennifer Yick, Biswanth Mukherjee, DipakGhosal," Wireless Sensor network survey", Computer networks (2008), 52, pp. 2292- 2330.

[2] Md. Akhtaruzzaman Adnan, MohammdAbdurRazzaque, Ishtiaque Ahmed, Ismail Fauzi Is nin," Bio-mimic Optimization Strategies in Wireless Sensor Networks: A Survey", Sensors 2014, 14, pp. 299-235.

[3] E.M. Saad, M.H. Awadalla, and R.R. Darwish, "Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks", International journal of computers, Issue 2, volume 2, 2008, pp. 148-157.

[4] A. Moussa and N El-Sheimy," Localization of Wireless Sensor Networking using Bees Optimization Algorithm", IEEE International Symposium on ISSPIT, 2010, 15th -18thDec., pp. 478-481.

[5] Akira Mutazono, Masashi Sugano, Masayuki Murata," Frog Call-Inspired Self-Organizing Anti-Phase Synchronization for Wireless Sensor Network",ISATTranscations on Computers and IntelligentSystems,vol 1, no. 2, pp. 86-93, Dec. 2009.

Abstract : For making the decision of data mining process some expertise are required, some organization have their own expertise, but many organization doesn't have their own expertise, so the organization helps with some external advisor for the process of data mining. But risk is occurred at the time of getting advice from the external advisor; the question arises regarding the privacy of the customer data and loss of business intelligence. The Security and Privacy of the data are main challenging issues. The owner of the data has some private property like the outsourced database which contains the association rules. However, if the service provider is not trustworthy then integrity of mining results can affect badly. The proposed scheme for privacy preserving mining on databases to protect association rule means the corporate privacy. As per our study, in our paper we are proposing the heuristic based algorithm for hiding the sensitive association rules the algorithm is named as MDSRRC , owner hide sensitive association rule and place transform rules to the server for outsourcing purpose. In this algorithm we are providing an incremental association rule for mining. The recent study concludes that the problem of the incremental association rule mining task's importance was observed, when data is updated. The Matrix Apriori algorithm is proposed which is based on analysis of two association algorithm named as Apriori algorithm and FP-growth algorithm. The matrix Apriori algorithm has a simple structure similar as a matrices and vectors, the algorithm generates frequent patterns and minimizes the number of sets, as compared to previous algorithm. The matrix algorithm is simple and efficient way to generate association rule than the previous algorithm. For hiding the sensitive information of the database proposed algorithm MDSSRC selects the transactions and items by using certain criteria which transform. As per comparing with the previous algorithm the proposed algorithm is much better in performance which can be concluded with the results of the implementation.

Keywords: MDSRRC, AES, Hiding association rule, privacy preserving policy

[1]. X. Sun and P.S. Yu "A Border-Based approach for hiding the frequent item sets" In Proc. Fifth IEEE Int‟I conf. data mining (ICDM "05), pp. 426-433 Nov 2005.
[2]. V. Verkios and A. Gkoulalas- Divanis, A Survey of association rule hiding method for privacy, ser. Advance in database systems. Springer US, 2008, vol. 34.
[3]. Charu C. Aggrawal, Philip S. Yu, privacy preserving data mining models and algorithm. springer publishing company incorporated, 2008, pp. 267-286.
[4]. Y. Guo, "Reconstruction based association rule hiding‟, in proc. Of SIG<OD2007 Ph.D. Workshop on innovative database research 2007(IDA2007), 2007.
[5]. J. vaidya and C. Clifton, "privacy preserving association rule mining in vertically partitioned data‟, In proc. Int‟I Conf data mining pp. 639-644 july 2002.

Abstract : Cloud computing is an emerging trend in the field of IT providing scalable and flexible services to the end users on demand. Cloud offers services in three levels namely infrastructure, platform and software to meet the needs of different kinds of customers. The key cloud characteristics include multitenancy, location and device independence, elasticity, resource pooling and measured service. The IT companies especially the Small and Medium Scale Businesses are moving onto the cloud which enables them to perform high end computational tasks in a cost effective manner. As more and more IT capabilities can be provided as a service in cloud, security becomes a major concern. Among the numerous attacks that can target the cloud environment, DoS or DDoS attacks can cause a major breach in security. This paper discusses the various DDOS attacks and the defense mechanisms that can be employed to secure the cloud. Keywords: cloud computing, counter methods, DoS, DDOS attack, flooding

[1]. National Institute of Standards and Technology-Computer Security Resource Center. www.csrc.nist.gov
[2]. Nikhil Nischal and Peeyush Mathur, Cloud Computing: New challenge to the entire computer industry,IEEE 1st International Conference on Parallel, Distributed and Grid Computing, 2010.
[3]. The information week website.http://www.informationweek.com/cloud/infrastructure-as-a-service/9-worst-cloud-security-threats/d/d-id/1114085
[4]. K.Shanti, A Defense Mechanism to Protect Cloud Computing Against Distributed Denial of Service Attacks, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, May 2013.
[5]. S.S. Chopade, K.U. Pandey, D.S. Bhade, Securing Cloud Servers against Flooding Based DDOS Attacks, in Proc. International Conference on Communication Systems and Network Technologies,2013

Abstract : As more and more information on individuals and companies are placed in the cloud, concerns are beginning to grow about just how safe an environment it is. It is better to prevent security threats before they enter into the systems and there is no way how this can be prevented without knowing where they come from. The issue of resource allocation and revenue maximization is also equally important especially when it comes to cloud security. This brings about the necessity of different modelling techniques including but not limited; security threat, resource allocation and revenue maximization models. This survey paper will try to analyse security threats and risk mitigation in cloud computing. It gives introduction of how viral attack can invade the virtual machines on the cloud, discusses the top security threats and countermeasures by providing the viral threat modelling in virtual machines and risk mitigation. Resource allocation models and revenue maximization techniques are also discussed.
Keywords: Cloud computing, STRIDE, VMs, APIs, DREAD

[1] Yashaswi Singh, Farah Kandah, Weiyi Zhang (2011); A Secured Cost-effective Multi-Cloud Storage in Cloud Computing Computer Communications Workshops (INFOCOM WKSHPS), IEEE Conference on, pp.619-624, 10-15 April 2011, doi: 10.1109/INFCOMW.2011.5928887(ISBN: 978-1-4577-0248-8/11).

[2] Paulo Shakarian, Sean Eyre , Damon Paulo(2013), A Scalable Heuristic for Viral Marketing Under the Tipping Model. Social Network Analysis and Mining, Springer, Vol. 3, No. 4, Dec, 2013.

[3] Brent Lagesse (March, 2011): Challenges in Securing the Interface Between the Cloud and Pervasive Systems. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on Pages 106-110

[4] Fei Teng, Fr´ed´eric Magoul`es(2010), Resource Pricing and Equilibrium Allocation Policy in Cloud Computing," cit, pp.195-202, 2010 10th IEEE International Conference on Computer and Information Technology, 2010(ISBN: 978-0-7695-4108-2).

[5] Hong Xu, Member, IEEE, and Baochun Li, Senior Member, IEEE (2013), Dynamic Cloud Pricing for Revenue Maximization. IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 1, NO. 2, JULY-DECEMBER 2013(ISBN: 2168-7161).

Abstract : This Paper presents an automated method to identify the moving objects. The proposed method is a combination of Corner Detectors, Area Based Tracking, K – Means clustering. The Corner Detectors used in this are Moravec, Trajkovec 4 and 8 neighbour, Harris Plessey. NACM as a area based matching, K – means clustering for identifying the location of the object.

Keywords: Area Based Tracking, Automatic tracking, Corner Detectors, K mean clustering, Moravec.

[1] Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, "An Efficient k-Means Clustering Algorithm : Analysis and Implementation", IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 7, july 2002
[2] Ant´onio Almeida, Jorge Almeida and Rui Ara´ujo, "Real-Time Tracking of Moving Objects Using Particle Filters" ISR - Institute for Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, P-3030-290 Coimbra, Portugal
[3] Chris Harris & Mike Stephens," A combined corner and edge detector" (Plessey Research Roke Manor, United Kingdom © The Plessey Company pic. 1988).
[4] S. Lekshmi, Deendayal Kushwaha, Jharna Majumdar1, "Application Of Correlation Methods For Effective Computation Of Affine Parameters", Aerial Image Exploitation Division, Aeronautical Development Establishment (DRDO), C V Raman Nagar, Bangalore – 560 093
[5] Leila M.G. Fonseca and B.S. Manjunath, "Registration Techniques for Multisensor Remotely Sensed Imagery".
[6] D.Parks, J.P. Gravel, "Corner Detection"

Abstract : Video summarization is a process of removing the redundant frames and generating the most informative key frames of the videos. In this paper we have explained two efficient methods for video summarization and given the comparison between these two methods. The first method is Video summarization using CLD feature extraction and adaptive threshold technique for shot boundary detection. Second method is video summarization using aggregation function where three different features such as Histogram difference, correlation difference and moment of inertia difference are combined and that difference value is compared with the predefined threshold value.

Keywords: video summary, adaptive threshold ,key frames, aggregation function.

[1] "Efficient use of MPEG-7 Color Layout and EdgeHistogram Descriptors in CBI Systems"; Balasubramani R Dr.V.Kannan; GJCST , 2011.

[2] "Multimedia Content Filtering, Browsing, and Matching using MPEG-7 Compact Color Descriptors"; Santhana Krishnamachari , Akio Yamada , Mohamed Abdel-Mottaleb;In Proceedings of the Fourth Intl' Conf. On Visual Information Systems, Nov. 2000, France. [3] "Image Retrieval System Based on Color Layout Descriptor and Gabor Filters"; Hamid A. Jalab;2011 IEEE Conference on Open Systems (ICOS2011).

[4] "An Algorithm for Shot Boundary Detection and Key Frame Extraction Using Histogram Difference";Ganesh. I. Rathod , Dipali. A. Nikam;International Journal of Emerging Technology and Advanced Engineering-2013.

[5] "Key frame extraction using color histogram method.";Miss.A.V.Kumthekar, ;Prof.Mrs.J.K.Patil; IJSRET-2013

Abstract : Cloud computing has brought about a paradigm shift in Information Technology services globally most especially in developed countries for mid-sized to large scale enterprises. It has emerged as one of the major IT trends of the 21st century. Cloud computing simply refers to the process of storing and accessing data and applications over the internet in a remote location instead of the localhard drive storage. This has brought about so many advantages in business and has helped improved productivity in the organization; cost-efficiency, scalability, easy access to information, unlimited storage and flexibility are also some of the benefits. However, several challenges abound with the use of cloud computing. Despite the numerous benefits it offers, Security threats and risk stands out as a major constraint for organizations. This paper examines some of the benefits of cloud computing and the challenges in the enterprise environment and factors militating against its full adoption.It focuses more on the security challenges that includes data protection, privacy, security standardsas well as network attacks.

Keywords: Cloud Computing, Enterprise, Information and Communications Technology (ICT), Security threats, Cloud Service Providers (CSPs), Enterprise Cloud Computing

[1]. Wikipedia. (2012, December 5). Mobile Cloud Storage.[Online].Available: http://en.wikipedia.org/wiki/Mobile_Cloud_Storage
[2]. Cloud computing(2014) [Online].Available: http://www.contrib.andrew.cmu.edu/~madhurim/cloud%20computing.html
[3]. P. Mell and T. Grance, "The NIST Definition of Cloud Computing," IT Laboratory NIST., Gaithersburg, MD, Tech. Rep. 800-145, 2011.pp.1-3.
[4]. Enterprise Cloud Computing: Transforming IT (2009, July).[Online]. Available:http://www.inst-informatica.pt/servicos/informacao-e-documentacao/dossiers-tematicos/teste-dossier-tematico-no-7-cloud-computing/tendencias/enterprise-cloud-computing-transforming-it
[5]. N. Majadi. "Cloud Computing: Security Issues and Challenges" The International Journal of Scientific & Engineering Research, vol.4(7), pp.1515-1520, Jul. 2012.

Abstract : Contrast enhancement technique is used to enhance the perception of the image or scene. This enhancement controls the brightness difference between objects and their backgrounds. It also used as brightness preservation of image. Numerous contrast enhancement techniques are offered by various researchers for the betterment of the quality of images like contrast enhancement of HDR, DCT, DWT, Filtering etc. the common problem with image enhancement is difficult to achieve such images. Noise is an unwanted element of system that creates problem or interference. Contrast enhancement uses scaling to remove internal noise of dark images using DCT technique. Here in this paper we are presenting some techniques used for contrast enhancement of image.

Keywords: Contrast Enhancement, Internal Noise, Stochastic Resonance, Noise Reduction, Discrete Cosine Transform.

[1] Jha, Rajib Kumar, Rajlaxmi Chouhan, Prabir Kumar Biswas, and Kiyoharu Aizawa, Internal noise-induced contrast enhancement of dark images, In 19th IEEE International Conference on Image Processing (ICIP-2012), pp. 973-976, 2012.
[2] Chouhan, Rajlaxmi, C. Pradeep Kumar, Rawnak Kumar, and Rajib Kumar Jha. Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain. International Journal of Machine Learning and Computing, vol. 2, no. 5, 2012.
[3] Arici, Tarik, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, vol. 18, no. 9, pp. 1921-1935, 2009.
[4] Z. Y. Chen, B. R. Abidi, D. L. Page, and M. A. Abidi, Gray-level grouping (GLG): An automatic method for optimized image contrast enhancement—Part I: The basic method, IEEE Trans. Image Process., vol. 15, no. 8, pp. 2290–2302, Aug. 2006.
[5] Z. Y. Chen, B. R. Abidi, D. L. Page, and M. A. Abidi, Gray-level grouping (GLG): An automatic method for optimized image contrast enhancement—Part II: The variations IEEE Trans. Image Process., vol. 15, no. 8, pp. 2303–2314, Aug. 2006.