iosr-JCE   Volume-12 ~ Issue-6

Abstract: In mobile ad hoc networks (MANETs), the network topology changes frequently and unpredictably due to the arbitrary mobility of nodes. This feature leads to frequent path failures and route reconstructions, which causes an increase in the routing control overhead. The overhead of a route discovery cannot be neglected. Thus, it is imperative to reduce the overhead of route discovery in the design of routing protocols of MANETs. One of the fundamental challenges of MANETs is the design of dynamic routing protocols with good performance and less overhead. In a route discovery, broadcasting is a fundamental and effective data dissemination mechanism, where a mobile node blindly rebroadcasts the first received route request packets unless it has a route to the destination, and thus it causes the broadcast storm problem. This paper focuses on a probabilistic rebroadcast protocol based on neighbor coverage to reduce the routing overhead in MANETs.

Keywords - Mobile Ad Hoc Networks, Neighbor Coverage, and Network Connectivity, Probabilistic Rebroadcast, Routing Overhead, AODV.

[1] C. Perkins, E. Belding-Royer, and S. Das, "Ad hoc On-Demand Distance Vector (AODV) Routing," RFC 3561, 2003.
[2] D. Johnson, Y. Hu, and D. Maltz, "The Dynamic Source Routing Protocol for Mobile Ad hoc Networks (DSR) for IPv4," RFC 4728, 2007.
[3] H. AlAamri, M. Abolhasan, and T. Wysocki, "On Optimising Route Discovery in Absence of Previous Route Information in MANETs," Proc. of IEEE VTC 2009-Spring, pp. 1-5, 2009.
[4] X. Wu, H. R. Sadjadpour, and J. J. Garcia-Luna-Aceves, "Routing Overhead as A Function of Node Mobility: Modeling Framework and Implications on Proactive Routing," Proc. of IEEE MASS'07, pp. 1-9, 2007.
[5] S. Y. Ni, Y. C. Tseng, Y. S. Chen, and J. P. Sheu. "The Broadcast Storm Problem in a Mobile Ad hoc Network," Proc. of ACM/IEEE MobiCom'99, pp. 151-162, 1999.
[6] Mohammed, M. Ould-Khaoua, L.M. Mackenzie, C. Perkins, and J. D. Abdulai, "Probabilistic Counter-Based Route Discovery for Mobile Ad Hoc Networks," Proc. of WCMC'09, pp. 1335-1339, 2009.
[7] Williams and T. Camp, "Comparison of Broadcasting Techniques for Mobile Ad Hoc Networks," Proc. ACM MobiHoc'02, pp. 194-205, 2002.
[8] J. Kim, Q, Zhang, and D. P. Agrawal, "Probabilistic Broadcasting Based on Coverage Area and Neighbor Confirmation in Mobile Ad hoc Networks," Proc. of IEEE GLOBECOM'04, 2004.
[9] J. D. Abdulai, M. Ould-Khaoua, and L. M. Mackenzie, "Improving Probabilistic Route Discovery in Mobile Ad Hoc Networks," Proc. Of IEEE Conference on Local Computer Networks, pp. 739-746, 2007.
[10] Network Simulator - ns-2 http://www.isi.edu/nsnam/ns/.

Abstract: Motion tracking is an important task in image processing applications. To track moving objects and their interaction in a complex environment is a difficult task, this work basically explains the technique of tracking moving objects. Moving object detection can be accomplished by image capturing, background subtraction and Prewitt edge detection operator. The main idea of our approach, called the background subtraction technique, is to subtract directly between two consecutive frames to extract the difference image. The difference image marks the areas where a moving object was in frame N and where the object is in frame N+1, respectively. Prewitt operator is more suitable in case of moving object analysis.

Keywords - Background subtraction, Canny edge detection operator, Edge detection, Motion Tracking, Moving object detection, Noise reduction, Prewitt edge detection operator.

[1] Rafael C. Gonzalez, Richard E.Woods, Digital Image Processing, Prentice Hall
[2] J.Canny, "A Computational Approach to Edge Detection", IEEE Trans.1986.
[3] Arnab Roy, Sanket Shinde and Kyoung-Don Kang, International Journal of Image Processing(IJIP), Voloume (2).
[4] A.Mukherjee, "Edge detection based motion tracking in video survellance", Proc. International Conference on Signal and Image Processing,2013.(Conference Proceedings)
[5] Merin Antony A,JAnitha, "A Survay of moving object segmentation methods".
[6] M.Piccardi,"Background subtraction technique: a review", IEEE Conference on System,Man and Cybernetics.

Abstract: In the past few years sharing photos, within social networks has become very popular .In order to make these huge collection easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order o speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context .In this paper, daily and continuous communication implies the exchange of several types of content, including free text, image, audio and video data. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments On an image database containing various landmarks. Tagging in online social networks is very popular these days as it facilitates search and retrieval of multimedia content .However, noisy and spam annotations often make it.

Keywords – annotation process, audio and video data, trust modeling, tagging, spams.

[1] kyuoo, and D. Su, "Towards the semantic Web: Collaborative tag suggestions," in Proc. ACM WWW, May 2006, pp. 1–8.
[2] ive sources in a hyperlinked environment," JACM, vol. 46, no. 5, pp. 604–632, Sept. 1999
[3] page, s.brin, r. motwani, and t.winograd. the pagerank citation ranking:bringing order to the web. technical report, stanford university, 1998.
[4] m. richardson, r..agrawal, and p. domingos. trust management for the semantic web. in web, proceedings of the second international semantic web conference,2003.
[5] K, J.-S. Lee, L. Goldmann, and T. Ebrahimi, "Geotag propaga- tion in social networks based on user trust model," Multimedia Tools Applicat., pp. 1–23, July 2010.
[6] ML. von Ahn, B. Maurer, C. Mcmillen, D. Abraham, and M. Blum, "reCAPTCHA: Human-based character recognition via Web security measures," Science, vol. 321, no. 5895, pp. 1465–1468, Aug. 2008.
[7] petrusel, r.. stanciu, p.l.: making recommendations for decision processes based on aggregated decision data models. in abramowicz, w. et al.(eds.): bis 2012, lnbip 117, pp. 272–283. springer-verlag berlin heidelberg (2012)