Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Convolutional Neural Network (CNN) has recently become very popular in visual object tracking due to their strong feature representation capabilities. Almost all of the CNN based trackers currently use the features extracted from shallow convolutional layers of VGGNet architecture. This paper presents an investigation of the impact of deep convolutional layer features in an object tracking framework. In this study, we demonstrate for the first time, the viability of features extracted from deep layers of GoogLeNet CNN architecture for the purpose of object tracking. We integrated GoogLeNet features in a discriminative correlation filter based tracking framework. Our experimental results show that the GoogLeNet features provides significant computational advantages over the conventionally used VGGNet features, without much compromise on the tracking performance. It was observed that features obtained from inception modules of GoogLeNet have high depths. Further, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the extracted features. This greatly reduces the computational cost and thus improve the speed of the tracking process. Extensive evaluation have been performed on three benchmark datasets: OTB, ALOV300++ and VOT2016 datasets and its performances are measured in terms of metrics like F-score, One Pass Evaluation, robustness and accuracy. © 2018 IEEE.

World University root node

2018 13th International Conference on Industrial and Information Systems, ICIIS 2018 - Proceedings

Deep GoogLeNet Features for Visual Object Tracking

Cataract is one of the major ophthalmic diseases worldwide which can potentially affect the performance of iris-based biometric systems. While existing research has shown that cataract does not have a major impact on iris recognition, our observations suggest that iris segmentation algorithms are not well equipped to handle cataract or post cataract surgery cases, thereby affecting the overall iris recognition performance. This paper presents an efficient iris segmentation algorithm with variations due to cataract and post cataract surgery. The proposed algorithm, termed as SegDenseNet, is a deep learning algorithm based on DenseNet. The experiments on the IIITD Cataract Surgery Database show that improving iris segmentation enhances the recognition performance by up to 25% across different sensors and matchers. © 2018 IEEE.

Proceedings - International Conference on Pattern Recognition

SegDenseNet: Iris Segmentation for Pre-and-Post Cataract Surgery

A face image not only provides details about the identity of a subject but also reveals several attributes such as gender, race, sexual orientation, and age. Advancements in machine learning algorithms and popularity of sharing images on the World Wide Web, including social media websites, have increased the scope of data analytics and information profiling from photo collections. This poses a serious privacy threat for individuals who do not want to be profiled. This research presents a novel algorithm for anonymizing selective attributes which an individual does not want to share without affecting the visual quality of images. Using the proposed algorithm, a user can select single or multiple attributes to be surpassed while preserving identity information and visual content. The proposed adversarial perturbation based algorithm embeds imperceptible noise in an image such that attribute prediction algorithm for the selected attribute yields incorrect classification result, thereby preserving the information according to user's choice. Experiments on three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed algorithm not only anonymizes k-attributes, but also preserves image quality and identity information. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

IJCAI International Joint Conference on Artificial Intelligence

Anonymizing k-facial attributes via adversarial perturbations

Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle's lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, perhaps even alerting the driver in advance if it is not, or whether the vehicle's speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this paper we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle's current lane. We employ a deep learning based technique to classify the vehicle's lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real world datasets collected in developed and developing regions. DeepLane can detect vehicle's lane position with an accuracy of over 90% in both day and night conditions. We have implemented DeepLane as an Android-app that runs at 5 fps on CPU and upto 15 fps on smartphone's GPU and can also assist existing navigation applications with lane-level information. © 2018 Association for Computing Machinery.

BuildSys 2018 - Proceedings of the 5th Conference on Systems for Built Environments

DeepLane: Camera-assisted GPS for driving lane detection

Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has raised apprehensions about susceptibility of these systems to various attacks. In the past, researchers have examined the impact of various iris presentation attacks such as textured contact lenses and print attacks. In this research, we present a novel presentation attack using deep learning based synthetic iris generation. Utilizing the generative capability of deep con-volutional generative adversarial networks and iris quality metrics, we propose a new framework, named as iDCGAN (iris deep convolutional generative adversarial network) for generating realistic appearing synthetic iris images. We demonstrate the effect of these synthetically generated iris images as presentation attack on iris recognition by using a commercial system. The state-of-the-art presentation attack detection framework, DESIST is utilized to analyze if it can discriminate these synthetically generated iris images from real images. The experimental results illustrate that mitigating the proposed synthetic presentation attack is of paramount importance. © 2017 IEEE.

IEEE International Joint Conference on Biometrics, IJCB 2017

Synthetic iris presentation attack using iDCGAN

IIIT-Delhi

Unraveling representations for face recognition: from handcrafted to deep learning

Self-assemblies of N2O4 donor Schiff base ligands, that is, N,N′-Bis(3-ethoxysalicylidenimino) 1, 3-diaminopropane (H2LOEt) with cadmium acetate salts in presence of auxiliary co-ligands thiocyanato/azido/chloride allow the formation of three anion control complexes, specifically, [Cd2LOEt(μ1,3-SCN)(η1-SCN)]n (1), {Cd4(LOEt)2(μ1,1-N3)3(η1-N3)]2 (2) and [Cd2(LOEt)(Cl)2.CH3OH].CH3OH] (3). Crystal structures of complexes 1–3 were determined by single-crystal X-ray diffraction studies. The single-crystal X-ray diffraction analysis confirmed that complex 1 is an one-dimensional polymeric chain in which Cd(II) ions are bridged by one thiocyanate group bonding in an end-to-end fashion. Complex 2 is a centrosymmetric octanuclear complex with double end-on azide (μ1,1-N3 −) as linker. On the other hand, 3 is a dinuclear complex with terminally bound Cl− ions. Analysis of the structure revealed that all the complexes possess a 4-membered Cd2 (μ2-O)2 core fastened by the combined coordination action of a doubly deprotonated ligand. Complexes 1–3 have been successfully characterized by elemental analyses, IR and UV-Vis spectroscopy. The luminescent properties of complexes 1–3 have been studied in DMSO solution. The antibacterial and antibiofilm potential for complexes 1–3 were investigated against Gram-positive and Gram-negative bacterial strains. Intermolecular interactions with respect to percentages of hydrogen bonding of cadmium complexes were quantified by Hirshfeld surface. © 2018 Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim}, author_keywords={Antimicrobial; Cd(II) coordination compounds; Hirshfeld surface; Photoluminescence; Pseudohalides}, references={Jiang, P., Guo, Z., (2004) Coord. Chem. Rev., 248, pp. 205-229; Huang, W., Zhu, H.-B., Gou, S.-H., (2006) Coord. Chem. Rev., 250, pp. 414-423; Zhang, H., Wang, X., Zhang, K., Teo, B.K., (1999) Coord. Chem. Rev., 183, pp. 157-195; Deng, H., Qiu, Y.-C., Li, Y.-H., Liu, Z.-H., Zeng, R.-H., Zeller, M., Batten, S.R., (2008) Chem. Commun., pp. 2239-2241; Morsali, A., Masoomi, M.Y., (2009) Coord. Chem. Rev., 253, pp. 1882-1905; Mahmoudi, G., Morsali, A., Zeller, M., (2009) Inorg. Chim. Acta., 362, pp. 217-225; Steed, J.W., Atwood, J.L., (2009) Supramolecular Chemistry, , second ed; Zaworotko, M.J., (2007) Cryst. Grwoth. Des., 7, pp. 4-9; Escuer, A., Vicente, R., Goher, M.A.S., Mautner, F.A., (1998) Inorg. Chem., 37, pp. 782-787; Abu-Youssef, M.A.M., Escuer, A., Gatteschi, D., Goher, M.A.S., Mautner, F.A., Vicente, R., (1999) Inorg. Chem., 38, pp. 5716-5723; Abu-Youssef, M.A.M., Goher, M.A.S., Mautner, F.A., Vicente, R., Reiss, G., (2000) Angew. Chem. Int. Ed. Engl., 39, p. 1624; Cano, J., Yourmaux, Y., Abu-Youssef, M.A.M., Goher, A.E.M.A.S., Mautner, F.A., Vicente, R., Reiss, G.J., (2005) New. J. Chem., 29, pp. 306-314; Bin, L., Wen, H.-M., Zhou, W., Chen, B., (2014) J. Phys. Chem. Lett., 5, pp. 3468-3479; Sumida, K., Rogow, D.L., Mason, J.A., McDonald, T.M., Bloch, E.D., Herm, Z.R., Bae, T.-H., Long, J.R., (2012) Chem. Rev., 112, pp. 724-781.; Mautner, F.A., Scherzer, M., Berger, C., Fischer, R.C., Vicente, R., Massoud, S.S., (2015) Polyhedron, 85, pp. 20-26; Mautner, F.A., Louka, F.R., Hofer, J., Spell, M., Lefevre, A., Guilbeau, A.E., Massoud, S.S., (2013) Cryst. Growth. Des., 13, pp. 4518-4525; Kundu, P., Chakraborty, P., Adhikary, J., Chattopadhay, T., Fischer, R.C., Mautner, F.A., Das, D., (2015) Polyhedron, 85, pp. 320-328; Maiti, M., Thakurta, S., Sadhukhan, D., Pilet, G., Rosair, G.M., Nonat, A., Charbonniere, L.J., Mitra, S., (2013) Polyhedron, 65, pp. 6-15; Machura, B., Nawrot, I., Michalik, K., (2011) Polyhedron, 30, pp. 2294-2302; Machura, B., Nawrot, I., Michalik, K., (2012) Polyhedron, 31, pp. 548-557; Machura, B., Nawrot, I., Kruszynski, R., (2014) J. Luminescence, 156, pp. 240-254; Sun, P.-P., Chen, Q., Zhu, X., Li, B.-L., Li, H.-Y., (2013) Polyhedron, 52, pp. 1009-1015; Rogge, S.M.J., Bavykina, A., Hajek, J., Garcia, H., Olivos-Suarez, A.I., Sepúlveda-Escribano, A., Vimont, A., Gascon, J., (2017) Chem. Soc. Rev., 46, pp. 3134-3184; Chen, Y., Shengqian, M., (2016) Dalton Trans., 45, pp. 9744-9753; Mistry, S., Sutter, J.P., Natarajan, S., (2016) Dalton Trans., 45, pp. 5140-5150; Mondal, K.C., Mukherjee, P.S., (2008) Inorg. Chem., 47, pp. 4215-4225; Liu, T.-F., Fu, D., Gao, S., Zhang, Y.-Z., Sun, H.-L., Su, G., Liu, Y.-J., (2003) J. Am. Chem.Soc., 125, pp. 13976-13977.; Wang, Y.-Q., Sun, W.-W., Wang, Z.-D., Jia, Q.-X., Gao, E.-Q., Song, Y., (2011) Chem. Commun., 47, pp. 6386-6388.; Guo, Z., Panda, D.K., Maity, K., Lindsey, D., Parker, T.G., Albrecht-Schmitt, T.E., Barreda-Esparza, J.L., Saha, S., (2016) J. Mater. Chem. C, 4, pp. 894-899; Campbell, M.G., Sheberla, D., Liu, S.F., Swager, T.M., Dincă, M., (2015) Angew. Chem. Int. Ed., 54, pp. 4349-4352; Janiak, C., (2003) Dalton Trans., pp. 2781-2804; Chen, J., Zhang, Z., Bao, Z., Ye Su, Xing, H., Yang, Q., Ren, Q., (2017) ACS Appl. Mater. Interfaces, 9, pp. 9772-9797; Le, Z., Guo, X., Cheng He, Duan, C., (2016) ACS Catal., 6, pp. 7935-7947; Yoon, M., Srirambalaji, R., Kim, K., (2012) Chem. Rev., 112, pp. 1196-1231; Chowdhury, H., Ghosh, R., Rahaman, S.H., Ghosh, B.K., (2007) Polyhedron, 26, pp. 5023-5029; Massoud, S.S., Dubin, M., Guilbeau, A.F., Spell, M., Vicente, R., Wilfling, P., Fischer, R.C., Mautner, F.A., (2014) Polyhedron, 78, pp. 135-140; Boonmak, J., Nakano, M., Chaichit, N., Pakawatchai, C., Youngme, Y., (2011) Inorg. Chem., 50, p. 7324; Meng, Z.-S., Yun, I., Zhang, W.-X., Hong, C.-G., Herchel, R., Ou, Y.-C., Leng, J.-D., Tong, M.-L., (2009) Dalton Trans., pp. 10284-10295; Escuer, E., Vicente, R., Goher, M.A.S., Mautner, F.A., (1997) J. Chem. Soc. Dalton Trans., pp. 4431-4434; Tandon, S.S., Thompson, L.K., Miller, J.S., (1995) Chem. Commun., pp. 1907-1908; Shen, Z., Zuo, J.-L., Yu, Z., Zhang, Y., Bai, J.-F., Che, C.-M., Fun, H.-K., You, X.-Z., (1999) Dalton Trans., pp. 3393-3397; Cannas, M., Carta, G., Cristini, A., Marongiu, G., (1976) J. Chem. Soc. Dalton Trans., p. 210. , and references therein; Zhang, N., Jiang, M.H., Yuan, D.R., Xu, D., Tao, X.T., Shao, Z.S., (1990) Cryst. Growth., 102, pp. 581-584; Hou, W.B., Yuan, D.R., Xu, D., Zhang, N., Yu, W.T., Liu, M.G., Tao, X.T., Jiang, M.H., (1993) Mater. Res. Bull., 28, pp. 645-653; Xu, H., Huang, L.F., Gao, L.M., Zhang, Y.G., Ren, X.M., Song, Y., Xie, J., (2008) J. Lumin., 128, pp. 1665-1672; Machura, B., Switlicka, A., Nawrot, I., Mrozinski, J., Michalka, K., (2011) Polyhedron, 30, pp. 2815-2823; Zianna, A., Ristovic, M.S., Psomas, G., Hatzimitriou, A., Coutouli-Argyropoulin, E., Lalia-Kantouri, M., (2011) J. Coord. Chem., 64, pp. 2130-2156; Costamagna, J., Vargas, J., Latorre, R., Alvadarado, A., Mena, G., (1992) Coord. Chem. Rev., 119, pp. 67-88; Sharma, Y.S., Pandey, H.N., Mathur, P., (1994) Polyhedron, 13, pp. 3111-3117; Chakraborty, P., Guha, A., Das, S., Zangrando, E., Das, D., (2013) Polyhedron, 49, pp. 12-18; Sharif, M.A., Najafi, G.R., (2013) Acta. Chim. Slov., 60, pp. 138-143; Karmakar, R., Choudhury, C.R., Huges, D.L., Mitra, S., (2007) Inorg. Chim. Acta., 360, pp. 2631-2637. , references therein; Lane, T.W., Morel, F.M.M., (2000) Proc. Natl. Acad. Sci. USA, 97, pp. 4627-4631; Majumdar, D.J., Das, S., Biswas, J.K., Mondal, M., (2017) J. Mol. Struct., 1134, pp. 617-624; Majumdar, D.J., Babu, M.S.S., Das, S., Biswas, J.K., Mondal, M., (2017) J. Mol. Struct., 1138, pp. 161-171; Majumdar, D.J., Babu, M.S.S., Das, S., Mohapatra, C., Biswas, J.K., Mondal, M., (2017) Chemistry Select, 2, pp. 4811-4822; Majumdar, D.J., Biswas, J.K., Mondal, M., Babu, M.S.S., Metre, R.K., Das, S., Bankura, K., Mishra, D., (2018) J. Mol. Struct., 1155, pp. 745-757; Tarafder, M.T.H., Kasbollah, A., Crouse, K.A., Ali, A.M., Yamin, B.M., Fun, H.-K., (2001) Polyhedron, 20, pp. 2360-2370; Nakamoto, K., (1997) IR and Raman Spectra of Inorganic and Coordination Compounds, , 5th ed; Lever, A.B.P., (1984) Inorganic Spectroscopy, , second ed; Garcia, J., Sole, L.E., Bausa, D., (2005) An Introduction to the Optical Spectroscopy of Inorganic Solids; Addison, A.W., Rao, T.N., Reedijik, J., Vanrjin, J., Verschoor, G.C., (1984) J. Chem. Soc., Dalton Trans., p. 1348; Boudalis, A.K., Tangoulis, V., Raptopoulou, C.P., Terzis, A., Tuchagues, J.P., Perlepes, S.P., (2004) Inorg. Chim. Acta., 357, pp. 1345-1354; Wemple, M.W., Coggin, D.K., Vincent, J.B., McCusker, J.K., Streib, W.E., Huffman, J.C., Hendrickson, D.N., Christou, G., (1998) J. Chem. Soc.; Dalton Trans., pp. 719-726; Hirshfeld, L., (1977) Theor.Chim.Acta., 44, pp. 129-133; Mckinnon, J.J., Jayatilaka, D., Spackman, M.A., (2007) Chem. Commun., 27, pp. 3814-3816; Machura, B., Switlicka, A., Nawrot, I., Mrozinski, J., Michalik, K., (2011) Polyhedron, p. 30; Lakowicz, J.R., (1994) Topics in Flourescence Spectroscopy; Ray, S., Konar, S., Jana, A., Patra, S., Chatterjee, S., Golen, J.A., Rheingold, A.L., Kar, S.K., (2012) Polyhedron, 33, pp. 82-89; Gan, W., Jones, S.B., Reibenspies, J.H., Hancock, R.D., (2005) Inorg. Chim. Acta., 358, pp. 3958-3966; Li, Y., Shi, L., Qin, L.-X., Qu, L.-L., Jing, C., Lan, M., James, T.D., Long, Y.-T., (2011) Chem. Commun., 47, pp. 4361-4363; Ciesla, P., Kocot, P., Mytych, P., Stasicka, Z., (2004) J. Mol. Catal. A.Chem., 224, pp. 17-33; de Silva, A.P., Fox, D.P., Huxley, A.J., Moody, T.S., (2000) Coord. Chem. Rev., 205, pp. 41-57; Fu, D.-W., Cai, H.-L., Liu, Y., Ye, Q., Zhang, W., Zhang, Y., Chen, X.-Y., Xiong, R.-G., (2013) Science, 339, pp. 425-428; Fu, D.-W., Zhang, W., Cai, H.-L., Zhang, Y., Ge, J.-Z., Xiong, R.-G., Huang, S.D., (2011) J. Am. Chem. Soc., 133, pp. 12780-12786; Fu, D.-W., Zhang, W., Cai, H.-L., Ge, J.-Z., Zhang, Y., Xiong, R.-G., (2011) Adv. Mater., 23, pp. 5658-5662; Fu, D.-W., Cai, H.-L., Li, S.-H., Ye, Q., Zhou, L., Zhang, Y., Deng, F., Xiong, R.-G., (2013) Phys. Rev. Lett., 110, p. 257601; Zhang, W.-Y., Tang, Y.-Y., Li, P.F., Shi, P.-P., Liao, W.-Q., Fu, D.-W., Ye, H.-Y., Xiong, R.-G., (2017) J. Am. Chem. Soc., 139, pp. 10897-10902; Basak, S., Sen, S., Banerjee, S., Mitra, S., Rodriguez, G.R.M.T.G., (2007) Polyhedron, 26, pp. 5104-5112; Sadhukhan, D., Ray, A., Rosair, G., Charbonniere, L., Mitra, S., (2011) Bull. Chem. Soc. Jpn., 84, pp. 711-764; Maiti, M., Sadhukhan, D., Thakurta, S., Roy, S., Pilet, G., Butcher, R.J., Nonat, A., Mitra, S., (2012) Inorg. Chem., 51, pp. 12176-12187; Majumdar, D.J., Babu, M.S.S., Das, S., Mohapatra, C., Biswas, J.K., Mondal, M., (2017) ChemistrySelect, 2, pp. 4811-4822; Majumdar, D.J., Babu, M.S.S., Das, S., Mohapatra, C., Biswas, J.K., Mondal, M., (2017) Chemistry Select, 2, pp. 4811-4822; Roy, S., Chattopadhyay, S., (2015) Inorg.Chim.Acta., 433, pp. 72-77; Roy, S., Bauza, A., Frontera, A., Chattopadhyay, S., (2016) Inorg.Chim.Acta., 450, pp. 321-329; Banu, K.S., Mondal, S., Guha, A., Das, S., Chattapdhyay, T., Suresh, E., Zangrando, E., Das, D., (2011) Polyhedron, 30, pp. 163-168; Nawrot, I., Machura, B., Kriszynski, R., (2015) Cryst.Eng.Com., 17, pp. 830-835; Roy, S., Harms, K., Chattopdhyay, S., (2015) Polyhedron, 91, pp. 10-16; Høiby, N., Ciofu, O., Bjarnsholt, A., (2010) Future Microbiology, 5, pp. 1663-1674; Rasamiravaka, T., Labtani, Q., Duez, P., El Jaziri, M., (2015); Estrela, A.B., Hec, M.G., (2009) Curr. Med. Chem., 16, pp. 1512-1530. , WR. Abraham; Skamnioti, P., Gurr, S.J., (2009) Trends Biotechnol., 27, pp. 141-150; Giacomucci, L., Bertoncello, R., Salvadori, O., Martini, I., Favaro, M., Villa, F., Sorlini, C., Cappitelli, F., (2011) Microb. Ecol., 62, pp. 287-298; Cappitelli, F., Salvadori, O., Albanese, D., Villa, F., Sorlini, C., (2012) Biofouling., 28, pp. 257-266; Zhang, J., (2012) &gt;J. Memb. Sci., 8-14, pp. 403-404. , WLC. Loong, S. Chou, C. Tang, R. Wang, AG. Fane; Parai, D., Islam, E., Mitra, J., Mukherjee, S.K., (2017) Can. J. Microbiol, 63, pp. 169-178}, correspondence_address1={Majumdar, D.; Department of Chemistry, India; email: dmajumdar30@gmail.com}, publisher={Wiley-Blackwell}, issn={23656549}, language={English}, abbrev_source_title={ChemistrySelect}, document_type={Article}, source={Scopus},

Department of Chemistry

ChemistrySelect

Cd(II) Pseudohalide Complexes with N, N′-Bis(3-ethoxysalicylidenimino) 1,3-Diaminopropane: Crystal Structures, Hirshfeld Surface, Antibacterial and Anti-Biofilm Properties

In this digital era, it is very difficult to think of any situation in our daily life that is not using Information and Communication Technology (ICT). It is the main component and infrastructure that empower modern computing. These improvements offer many useful features in automobiles industry as well. It emphasis on the green environment, engines with high performance, secure design, etc. The concept of an intelligent transportation system (ITS) or smart transport includes a large range of methods and applications. The use of embedded intelligence to connect the vehicles, to the infrastructure and central sites make them smart. The smart transportation achieves smart policy goals in the urban environment such as increased mobility, better safety, reduced emissions, minimum fuel usage, and economic competitiveness. The VANET (vehicular ad-hoc network) has the ability to build better solutions for poor road conditions, traffic congestion, weather predictions, driving safety, passenger comfort, and other related applications by exchanging data on the road. However, it is not an easy task to ensure secure data transmission in VANETs due to different challenges such as dynamic topology, different direction, high mobility, etc. In our scheme, we propose a lightweight secure authentication and communication system for VANETs. Our protocol mainly focuses on one of the main application of VANETs, that is warning messages. Analysis and results demonstrate that our protocol outperforms well in different perspectives such as communication cost, storage overhead, and computation time. © 2018 IEEE.

International Symposium on Advanced Networks and Telecommunication Systems, ANTS

Secure Lightweight Data Transmission Scheme for Vehicular Ad hoc Networks

Geographic Opportunistic Routing (GOR) tries to avariciously send a message from source node to the closest neighboring node that's the highest priority toward the packet's destination at road Intersection in VANETs. Geographic Opportunistic Routing (GOR) may well be an advanced and efficient routing technique that was associated decent, economical and heightens routing scheme for VANETs. Vehicular Ad-Hoc Networks (VANETs) is a special type of wireless networks. In VANETs, the foremost challenge is that the extraordinarily dynamic property of mobile nodes (i.e., high mobility), therefore the extraordinarily dynamic topology suggests that topology changes over times. In this paper, we have a tendency to propose a new distributed geographical opportunistic routing protocol for vehicular ad-hoc situations referred to as Distributed Geographical Opportunistic Routing (DGOR) protocol. To handle the node mobility, direction, and speed, we've got propose a new replacement metric that considers the geographical location of the vehicles, the amount of the neighboring nodes at road Intersection, and therefore the expected position of nodes victimization the motion vector of the vehicular nodes at road Intersection. We have introduced DGOR problem in Vehicular Ad-Hoc Networks at road Intersection and this problem is NP-complete. A O(nlogn) approximation algorithm is designed to solve the DGOR problem for Vehicular Ad-Hoc Networks at road Intersection. © 2017 IEEE.

Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017

Distributed algorithm for geographic opportunistic routing in VANETs at road intersection

Smart vehicle plays a major role in today's technological universe and we have different vehicle applications with the help of the internet of things. To fulfil it, there is a requirement of expeditious communication along with integrity, availability, high delivery ratio and low overhead assets. Otherwise, specific information or data may be useless or there will be possibility of the serious problem(s). Vehicular communication is mostly preferable to transfer datagrams from one user to other applicants to accomplish these characteristics. However, the delivery of packets successfully is not a simple task and be familiar with the updated information due to various obstacles. Researchers suggested discrete protocols to procure different properties in this noisy vehicular ad-hoc network (VANET). Scientists designed systematic authentication models using user identity for vehicular technology. In this article, we investigate multiple vehicle communication algorithms. But, we find some vulnerabilities after performing analysis on the particular scheme. After that, we propose an enhanced authentication system focused on vehicle-to-vehicle data transmission to overcome the present flaws. In addition, the suggested protocol is protected against multiple attacks and efficient in terms of the execution. Copyright © 2018 Inderscience Enterprises Ltd.