iosr-JCE International Conference on RECENT TRENDS IN ENGINEERING AND MANAGEMENT

Abstract:Users can remotely store their data in the cloud storage and can use the applications and services from a collective pool of configurable computing resources, without the trouble of local data storage and protection. Additionally, users should be able to just use the cloud storage as if it is local, without worrying about the need to authenticate its integrity. Hence, enabling public auditability for cloud storage is of critical importance so that users can resort to a Third Party Auditor (TPA) to prove the reliability of outsourced data and be burden free. To strongly initiate a valuable TPA, the auditing process should bring in no new vulnerabilities toward user data confidentiality, and bring in no other online trouble to user. In this paper, we propose a protected cloud storage system supporting privacy-preserving public auditing for active data. To allow the TPA to carry out audits for multiple users simultaneously and also prove the accuracy of remotely stored data efficiently. The audit outcome would also be useful for the cloud service providers to progress the cloud service platform. Extensive protection and performance analysis show the proposed schemes are provably protected and highly efficient. The test accomplished on Amazon EC2 instance further demonstrates the fast performance of the design.
Keywords- Data Storage, privacy-preserving, public auditability, cloud computing, batch verification

[1] C. Erway, A. Kupcu, C. Papamanthou, and R. Tamassia, Dynamic Provable Data Possession, Proc. ACM Conf. Computer and Comm. Security (CCS '09), 2009, 213-222.
[2] Y. Dodis, S.P. Vadhan, and D. Wichs, Proofs of Retrievability via Hardness Amplification, Proc. Theory of Cryptography Conf. Theory of Cryptography (TCC), 2009, 109-127.
[3] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, Above the Clouds: A Berkeley View of Cloud Computing, Technical Report UCB-EECS, (Univ. of California: Berkeley, Feb. 2009), 2009-28.
[4] Cloud Security Alliance, Top Threats to Cloud Computing, http://www.cloudsecurityalliance.org, 2010.
[5] M. Arrington, Gmail Disaster: Reports of Mass Email Deletions,http://www.techcrunch.com/2006/12/28/gmail-disasterreportsof-mass-email-deletions/, 2006.
[6] J. Kincaid, MediaMax/TheLinkup Closes Its Doors, http://www.techcrunch.com/2008/07/10/mediamaxthelinkup-closesits-doors/, July 2008.
[7] Amazon.com, Amazon s3 Availability Event: July 20, 2008,http://status.aws.amazon.com/s3-20080720.html, July 2008.
[8] Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing, IEEE Trans. Parallel and Distributed Systems, 22(5), 2011, 847-859.
[9] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, Provable Data Possession at Untrusted Stores, Proc. 14th ACM Conf. Computer and Comm. Security(CCS '07), 2007, 598-609.
[10] M.A. Shah, R. Swaminathan, and M. Baker, Privacy-Preserving Audit and Extraction of Digital Contents, Cryptology ePrint Archive, Report 2008/186, 2008.

Abstract:建设高质量的云应用程序变成了n immediately required research problem in cloud computing technology. Non-functional performance of cloud services is normally described by Quality-of-Service (QoS). To acquire QoS values, real-world usage of services candidates are generally required. At this time, there is no framework that can allow users to estimate cloud services and rank them based on their QoS values. This paper intends to framework and a mechanism that measures the quality and ranks cloud services for the users. Cloud Rank framework by taking the advantage of past service usage experiences of other users. So it can avoid the time consuming and expensive real life service invocation. This tactic determines the QoS ranking directly using the two personalized QoS ranking prediction approach i.e., CloudRank1 and CloudRank2. These algorithms make sure that the active services are accurately ranked. The interior purpose is ranking prediction of client side QoS properties, which likely have different values for dissimilar users of the similar cloud service. It estimates each and every one the applicant services at the user-side and rank the services based on the observed QoS values.
Keywords -Cloud Services, Cloud rank Framework, Quality-of-Service, Ranking Prediction, And Personalized Framework.

[1] J.S.Breese, D.Heckerman, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Ann.Conf. Uncertainty in Artificial Intelligence (UAI ‟98), pp. 43-52, 1998.
[2] B.sarwar, g.Karypis, J.Konstan, and J.Riedl,"Item-Based Collaborative Filtering Recommendation Algorithms", Proc.World Wide Web (WWW "0), pp.285-295, 2001.
[3] M.Desponded and G.Karypis,"Item-Based Top-n Recommendation", ACM Trans.Information System, vol.22, no.1, pp.143-177, 2004.
[4] R.Jin, J.Y.Chai, and L.Si,"An Automatic Weighting Scheme for Collaborative Filtering", Research and Development in Information Retrieval (SIGIR ‟04), pp.337-344, 2004.
[5] N.N.Liu and Q.Yang,"Eigen Rank: A Ranking-Oriented Approach to Collaborative Filtering", Proc.31 st Int‟l ACM SIGIR Conf.Ressearch, pp.83-90, 2008.
[6] C.Yang,B.Wei,"Cares:A Ranking-Oriented Cadal Recommender System", Joint Conf.Digital Libraries(JCDL ‟09),pp.203-212,2009
[7] Z.Zheng, Y.Zhang,"CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing", Proc.Reliable Distributed Systems (SRDS ‟10), pp.184-193, 2010.
[8] J.Wu, L.Chen, Z.Wu,"Predicting QoS for Service Selection by Neighborhood-Based Collaborative Filtering", IEEE Trans.System, March 2010.
[9] H.Khazaei, J.Misic, and V.B.MisiC,"Performance Analysis of Cloud Computing Centers Using Queuing Systems", IEEE Trans.Parallel Distributed System, vol.23, no.5, May 2012.
[10] Mr.K.Saravanan,"An Enhanced Qos Architecture based Framework for Ranking of Cloud Services", (IJETT), Volume 4 Issue 4 April 2013.

Abstract:Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. Facial activities are characterized by three levels. First, in the bottom level, facial feature points around each facial component, i.e., eyebrow, mouth, capture the detailed face shape information. Second, in the middle level, facial action units, represent the contraction of a specific set of facial muscles, i.e., lid tightener,eyebrow raiser, etc.Finally,in the top level, six prototypical facial expressions represent facial muscle movement. A unified probabilistic framework based on the dynamic Bayesian network to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge.
Keywords:-Bayesiannetwork,expression recognition, facial action unit recognition, facial feature tracking, simultaneous tracking and recognition

[1]. P. Ekman and W. V. Friesen, Facial Action Coding System (FACS): Manual. Palo Alto, CA, USA: Consulting Psychologists Press, 1978.
[2]. J. J. Lien, T. Kanade, J. F. Cohn, "Detection, tracking, and classification of action units in facial expression," J. Robot. Auto. Syst., vol. 31, no. 3, pp. 131–146, 2000.
[3]. G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski, "Classifying facial actions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 10, pp. 974–989, Oct. 1999.
[4]. M. S. Bartlett, G. Littlewort, M. G. Frank, C. Lainscsek, I. Fasel, and J. R. Movellan, "Recognizing facial expression: Machine learning and application to spontaneous behavior," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2. Jun. 2005, pp. 568–573.
[5]. Y. Tian, T. Kanade, and J. F. Cohn, "Recognizing action units for facial expression analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 2, pp. 97–115, Feb. 2001.
[6]. F. Dornaika and F. Davoine, "Simultaneous facial action tracking and expression recognition in the presence of head motion," Int. J. Comput. Vis., vol. 76, no. 3, pp. 257–281, 2008.

Abstract:The current image retrieval systems are successful in retrieving images, using keyword based approaches. However, they are incapable to retrieve the images which are context sensitive and annotated inappropriately. Content-Based Image Retrieval (CBIR) aims at developing techniques that support effective searching and browsing of large image repositories, based on automatically derived image features. The current CBIR systems suffer from the semantic gap. Though a user feedback is suggested as a remedy to this problem, it often leads to distraction in the search. To overcome these disadvantages, novel interactive keyword based image retrieval and integrating text with image content are proposed to enhance the retrieval accuracy. Also GOOGLE search engine is used as a back end to search and retrieved images with their link. The robustness of the result obtained by the proposed method is shown by various performance analyses like different web browsers, different internet service providers and etc.
Keywords:-Context sensitive, Annotation, Content-Based Image Retrieval, Google search engine

[1] S Edward Chang., Kingshy Goh., Gerard Sychay., Gang Wu., CBSA: Content-based soft annotation for multimodal image retrieval using Bayes Point Machines, IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, 13, 2003, p. 26.
[2] Feng, H., Shi, R., chua, T.S., " A Bootstraping Framework For annotating and retrieving WWW images," In :Proceedings of the ACM International Conference on Multimedia, 2004.
[3] Liu, Y., Zhang, D.S., Feng, D.D., A survey of content-based image retrieval with high-level semantics, Pattern Recognition, 40 (1), 2007, p. 262-282.
[4] Smith, J.R., Chang, S.F., "Visual Seek: A Fully automated content-based query system", ACM Multimedia, 1996, p. 87.
[5] Hauptman, A., Baron, R.V., Chen, M. Y., Christel, M., Duygulu, P., Huang, C., Jin, R., Lin, W. H., Ng, T., Moraveji, N., Papernick, N., Snoek, C. G. M., Tzanetakis, G., Yang, J., Wactlar, H. D., 2003. Informedia at TRECVID: analyzing and searching broadcast news http://www-nlpir.nist.gov/projects/tv.pubs.org.
[6] Hu, W., Wu, O., Chen, Z., Fu, Z., Maybank, S., Recognition of Pornographic WebPages by Classifying Texts and Images, IEEE Transactions on Pattern Analysis And Machine Intelligence 29(6), 2007, p.1019.
[7] Barnard. K., Forsyth, D. A., Learning the semantics of words and pictures, IEEE International Conference on Computer Vision 2, 2001, p. 408-415.
[8] Yang, H. C., Lee, C. H., Image Semantics Discovery from Web Pages for Semantic-based Image Retrieval using Self-organizing maps, Expert Systems with Applications 34, 2008, p. 266 - 279.
[9] Vadivel, A., Sural, S., Majumdar, A. K., Robust Histogram Generation from the HSV Color Space based on Visual Perception, International Journal on Signals and Imaging Systems Engineering 1(3/4), 2008, p. 245.
[10] Vadivel, A., Sural, S., Majumdar, A. K., Image Retrieval from Web using Multiple Features, Online Information Review, Emerald 33(6), 2009, p. 1169.

Abstract:Motivated by the needs of semantic search and retrieval of multimedia contents, operating directly on the video based annotations can be thought as a reasonable way for meeting these needs as video is a common standard providing a wide multimedia content description schema. Raw data and low-level features alone are not satisfactory to fulfil the user's requirements; that means, a deeper understanding of the content at the semantic level is necessary. A semantic content extraction system that allows the user to query and regain objects, events, and concepts that are extracted automatically is proposed. In automatic extraction process, starts with object and define class for each process in video data. A new ontology based fuzzy video data semantic model uses spatial/temporal relation in event and concept definition is declared. Objects extracted from consecutive representative frames are processed to extract temporal relations. In addition to that, additional rule to lower spatial relation computation cost and to be able to define some difficult situations more successfully is used. Event extraction process uses objects, spatial relations between objects and temporal relations between events. Similarly, objects and events are used in concept extraction process
Keywords:-Content-based retrieval, fuzziness, ontology, Semantic content extraction, video content modeling.

[1] T. Yilmaz, Object Extraction from Images/Videos Using a Genetic Algorithm Based Approach, master's thesis, Computer Eng. Dept., METU, Turkey
[2] C. Xu, J. Wang, K. Wan, Y. Li, and L. Duan, Live Sports Event Detection Based on Broadcast Video and Web-Casting Text, MULTIMEDIA '06: Proc. 14th Ann. ACM Int'l Conf. Multimedia, pp. 221-230
[3] Y. Zhang, C. Xu, Y. Rui, J. Wang, and H. Lu, Semantic Event Extraction from Basketball Games Using Multi-Modal Analysis, Proc. IEEE Int'l Conf. Multimedia and Expo (ICME '07), pp. 2190- 2193
[4] D. Song, H.T. Liu, M. Cho, H. Kim, and P. Kim, Domain Knowledge Ontology Building for Semantic Video Event Description, Proc. Int'l Conf. Image and Video Retrieval (CIVR), pp. 267-275
[5] Y. Yildirim, T. Yilmaz, and A. Yazici, Ontology-Supported Object and Event Extraction with a Genetic Algorithms Approach for Object Classification, Proc. Sixth ACM Int'l Conf. Image and Video Retrieval (CIVR '07), pp. 202-209
[6] Y. Yildirim and A. Yazici, Ontology-Supported Video Modeling and Retrieval, Proc. Fourth Int'l Conf. Adaptive Multimedia Retrieval: User, Context, and Feedback (AMR), pp. 28- 41
[7] Y. Yildirim, Automatic Semantic Content Extraction in Video Using a Spatio-Temporal Ontology Model, PhD dissertation, Computer Eng. Dept., METU, Turkey
[8]公元Bagdanov, m·贝尔蒂尼a . Del女人crniai, and G. Serra, Semantic Annotation and Retrieval of Video Events Using Multimedia Ontologies, Proc. IEEE Int'l Conf. Semantic Computing (ICSC)
[9] l .白林亭汝老挝,g·琼斯,和自动跟踪Smeaton,Video Semantic Content Analysis Based on Ontology, IMVIP '07: Proc. 11th Int'l Machine Vision and Image Processing Conf.
[10] T. Sevilmis M. Bastan, U. Gu¨du¨ kbay, and O ¨. Ulusoy, Automatic Detection of Salient Objects and Spatial Relations in Videos for a Video Dat abase System, Image Vision Computing, vol. 26, no. 10, pp. 1384-1396

Abstract:Grid computing is an important and developing computing initiative that involves the collection of network connected computers to form a distributed system for coordinated problem solving and resource sharing. This paper presents a state-of-the-art review of grid computing
. Keywords:-Computing, Distributed System, Grid Computing, Resource Sharing, Review.

[1] D. Abramson, R. Buyya, and J. Giddy, (2002) "A Computational Economy for Grid Computing and Its Implementation in the Nimrod-G Resource Broker," Future Generation Computer Systems, Vol. 18, No. 8,1061-1074.
[2] Henri Casanova (1998) "Distributed Computing Research Issues In Grid Computing", Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093-0114.
[3] j .曹S.A.贾维斯,美国赛妮D.J. Kerbysonand G.R. Nudd,(2002) "ARMS: An Agent-Based Resource Management System for Grid Computing," Scientific Programming, Vol. 10, No. 2, 135-148.
[4] Jasma Balasangameshwara and Nedunchezhian Raju (2013),"Performance-Driven Load Balancing With A Primary Backup Approach For Computational Grids With Low Communication Cost And Replication Cost, IEEE Transactions on Computers, Vol.62, No.5, 990-1003.
[5] K. Krauter, R. Buyya, and M. Maheswaran, (2002) "A Taxonomy and Survey of Grid Resource Management Systems for Distributed Computing," Software—Practice and Experience, Vol. 32, No. 2, 135-164.
[6] Klaus Krauter and Muthucumaru Maheswaran (1997) "Towards A High Performance Extensible Grid Architecture", Advanced Networking Research Laboratory Department of Computer Science University of Manitoba, Canada.
[7] Lei Liu, Hongxiang Guo and Jintong Lin (2012), "Dynamic Provisioning Of Self Organized Consumer Grid Services Over Integrated OBS/WSO Networks", JournalOf LightWave Technology, Vol.30, No.5, 734-753.
[8] Lizhe Wang, Gregor von Laszewski, Jie Tao, and Marcel Kunze (2009) "Grid Virtualization Engine: Design, Implementation, and Evaluation", IEEE Systems Journal, Vol. 3, No. 4, 477-488.
[9] M. Livny and R. Raman,(1998) "High-Throughput Resource Management," The Grid: Blueprint for a New Computing Infrastructure, 311-338.
[10] Mario Cannataro, Andrea Pugliese, Paolo Trunfio, (2004) "Distributed Data Mining On Grids: Services, Tools, And Applications", IEEE Transactions on Systems, Man and Cybernetics, Vol. 34, NO. 6, 2451-2465.

Abstract:An extensive helplessness of wireless networks in exacting the Mobile Ad-Hoc Network (MANET) is their vulnerability to node compromise and physical capture attacks. Detecting replication attacks is a nontrivial problem in MANETs due to the challenges resulted from node mobility, cloned and compromised node collusion, and the outsized number and extensive of the replicas. It has two replication detection schemes Time Domain Detection (TDD) and Space Domain Detection (SDD). The theoretical analysis indicates that TDD and SDD provide high detection accuracy and excellent resilience against smart and colluding replicas. It has no restriction on the number and distribution of replicas, and incurs low communication and computation overhead. According to theoretical analysis, for validating the path to occur any interference before transmitting the data from source to destination the RIP Protocol, TDD and SDD are the only approaches that support mobile networks that places no restrictions on the number and distribution of the cloned frauds and on whether the replicas collude or not.
Keywords:-TDD (Time Domain Detection), SDD (Space Domain Detection), RIP (Routing Information Protocol), MANET.

[1]. D.Sheela, Priyadarshini, Dr. G.Mahadevan, (2011) 'Efficient Approach to Detect Clone Attacks in Wireless Sensor Networks,' IEEE Transactions on wireless Networks.
[2]. H. Wen, J. Luo L, Zhou., (2011), 'Lightweight and effective detection scheme for node clone attack in wireless sensor networks', IET Wireless Sensors.
[3]. Heesook Choi, Sencun Zhu, Thomas F. La Portal., (2007) 'Detecting node clones in Sensor Networks,' The Pennsylvania State University.
[4]. K. Xing, F. Liu, X. Cheng, and D. H. C. Du, (2008) 'Real-time Detection of Clone Attacks in Wireless Sensor Networks', IEEE ICDCS'08.
[5]. Luigi Vincenzo, Mauro Conti, Roberto Di Pietro, Mancini, and Alessandro Mei., (2011) 'Distributed Detection of Clone Attacks in Wireless Sensor Networks,' IEEE Transactions.
[6]. Ryo Murakami, Nariyoshi Yamai, Kiyohiko Okayama., (2010) 'A MAC-address Relaying NAT Router for PC Identification from Outside of a LAN', 10th Annual International Symposium on Applications and the Internet.
[7]. Saurabh Ganeriwal and Mani B. Srivastava, (2004) 'Reputation-based Framework for High Integrity Sensor Networks', University of California.
[8]. W. Ho, M. Wright, and S. Das. (2008) 'Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis', IEEE Infocom.
[9]. Yi Yang, Xinran Wang, Sencun Zhu†, and Guohong Cao, (2004) 'SDAP: A Secure Hop by Hop Data Aggregation Protocol for Sensor Networks', University of California.
[10]. Zeng, Jiannong Cao, Shigeng Zhang, Shanqing Guo and Li Xie, (2010) 'Random-Walk Based Approach to Detect Clone Attacks in Wireless Sensor Networks', IEEE Journals on selected Areas in Communications, Vol 28, No 5.

Abstract:本文提出了新的策略对象tracking initialization using automatic moving object detection based background subtraction. The new strategies are integrated into the real-time object tracking system. The proposed background model updating technique and adaptive thresholding are used to produce a foreground object mask for object tracking initialization. Traditional background subtraction technique detects moving objects by subtracting the background model from the current image. Compare to various common moving object detection technique, background subtraction segments foreground objects more accurately and detects foreground objects even if they are non moving. However, one drawback of traditional background subtraction is that it is vulnerable environmental changes, for instance, gradual or fast illumination changes. The reason of this disadvantage is that it assumes a static background, and therefore a background model update is needed for dynamic backgrounds. The most important challenges are how to update the background model, and how to find out the threshold for classification of foreground and background pixels. The proposed technique is to determine automatically and dynamically depending on the intensities of the pixels within the current frame and a technique to update the background model with learning rate depending on the variations of the pixels within the background model and also the previous frame. This paper additionally represented a shape tracking technique to track the moving multiple objects in surveillance video.
Keywords:-moving object detection, background model, shape tracking.

[1]. Albert Torrent, Xavier Lladó, Jordi Freixenet, Antonio Torralba. A boosting approach for the simultaneous detection and segmentation of generic objects. Pattern Recognition Letters 34 (2013) 1490–1498.
[2]. Feng-Yang Hsieha, Chin-Chuan Hanb, Nai-Shen Wua, Thomas C. Chuangc, Kuo-Chin Fana. A novel approach to the detection of small objects with low contrast. Signal Processing 86 (2006) 71–83.
[3]. ChengEn Lu, Nagesh Adluru , Haibin Ling , Guangxi Zhu , Longin Jan Latecki. Contour based object detection using part bundles. Computer Vision and Image Understanding 114 (2010) 827–834.
[4]. Xingwei Yang, HairongLiu, LonginJanLatecki. Contour-based object detection as dominant set computation. Pattern Recognition 45 (2012) 1927–1936.
[5]. Carlos Cuevas , Narciso García. Improved background modeling for real-time spatio-temporal non-parametric moving object detection strategies. Image and Vision Computing 31 (2013) 616–630.
[6]. Alessandro Leone, Cosimo Distante , Francesco Buccolieri. A shadow elimination approach in video-surveillance context. Pattern Recognition Letters 27 (2006) 345–355.
[7]. Faisal Bashir, Fatih Porikli. Performance Evaluation of Object Detection and Tracking Systems,june2006.
[8]. Junda Zhu,Yuanwei Lao,Yuan F.Zheng. Object tracking in structured environments for video surveillance applications.IEEE Transactions on circuits and systems for video technology,Vol.20,No.2,February 2010.
[9]. Jong Sun Kim,Hae Yeom,and Young Hoon Joo. Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems.IEEE Transactions on consumer electronics,Vol.57, No.3, August 2011.
[10]. Kinjal A Joshi, Darshak G.Thakore. A survey on moving object detection and tracking in video surveillance system.International Journal of Soft Computing and Engineering(IJSCE) ISSN:2231-2307, Volume-2, Issue-3, July 2012.

Abstract:Delay Tolerant Networks (DTNs), also called as intermittently connected mobile networks, are wireless networks in which a fully connected path from source to destination is unlikely to exist. However, effective forwarding based on a limited knowledge of contact behavior of nodes is challenging. When large files need to be transferred from source to destination make all the packets available at the source and transfer the file as small packets. We study the packets arrival at source and analysis their performance. We consider the linear blocks and rateless linear coding to generate redundancy and also for energy constraint .We scheduling the large file into small packets and delivering through multipath to destination, for this we use optimal user centric allocation and scheduling the packets in the receiver side.
Keywords:-Delay tolerant networks,network coding, rate less codes,.

[1] E. Altman, F. De Pellegrini, and L. Sassatelli, "Dynamic control of coding in delay tolerant networks," in Proc. 2010 IEEE INFOCOM
[2] E. Altman and F. De Pellegrini, "Forward correction and Fountain codes in delay tolerant networks," in Proc. 2009 IEEE INFOCOM
[3] T. Spyropoulos, K. Psounis, and C. Raghavendra, "Efficient routing in intermittently connected mobile networks: the multi-copy case," ACM/IEEE Trans. .
[4] E. Altman, T. Bas¸ar, and F. De Pellegrini, "Optimal monotone forwarding policies in delay tolerant mobile ad-hoc networks," in Proc. 2008ACM/ICST Inter-Perf.
[5] J. Metzner, "An improved broadcast retransmission protocol," IEEETrans. Commun.,
[6] J. Nonnenmacher, E. Biersack, and D. Towsley, "Parity-based loss recovery for reliable multicast transmission," IEEE/ACM Trans. Netw.,
[7] S. Jain, M. Demmer, R. Patra, and K. Fall, "Using redundancy to cope with failures in a delay tolerant network," SIGCOMM Comput.Commun.
[8] Y. Wang, S. Jain, M. Martonosi, and K. Fall, "Erasure-coding based routing for opportunistic networks," in Proc. 2000