Journal Vol – 14 No -1, February 2019

A Secure and Efficient Scheduling Mechanism for Emergency Data Transmission in IOT


D.Subba Rao, Dr. N.S. Murti Sarma



Internet of things (IOT) enables electronic gadgets to communicate with the server and each other, enabling them to share crucial information. With the advancement in the technology, more and more devices are added to the network of IOT every day. In the era of smart cities, the amount of data being transmitted is immense. While transferring such a huge amount of data, the system has to prioritize the data being sent based on the importance, such as medical and fire safety information. Lack of efficient scheduling algorithms leads to inappropriate delivery of emergency packets, thus rupturing the functionality of the system. Also, the data sent over the network has to guardagainst attacks over the channel. To overcome these drawbacks, a scheduling algorithm named Efficient data emergency aware packet scheduling scheme (EARS), enhanced with data security using Elliptic curve cryptography is proposed in this paper. In EARS, each packet has a description of its priority and the deadline before which it has to reach the sink. This enables easy identification of the emergency nodes. Further, in order to reduce the total number of transmissions in the network, the normal data packets can be network-coded and sent to the destination. This will reduce the congestion in the network. The proposed method is compared with the existing state of the art techniques and the results produced outperformed the exciting methods.


network of IOT,efficient scheduling algorithms, Elliptic curve cryptography,emergency nodes, transmissions in the network,


I.A. T Hashemet al., “The role of big data in smart city,” Int. J. Inf. Manage., vol. 36, no. 5, pp. 748–758, 2016.

II.F. Yang and I. Aug ́e-Blum, “Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSN under real-time constraints,” Comput.Netw., vol. 55, no. 3, pp. 497–513, 2011.

III.G. Lu and B. Krishnamachari, “Minimum latency joint scheduling and routing in wireless sensornetworks,” Ad Hoc Netw., vol. 5, no. 6, pp. 832–843, 2007.

IV.K.-H. Phung, B. Lemmens,M. Goossens, A. Nowe, L. Tran, and K. Steenhaut, “Schedule-based multi-channel communication in wireless sensor networks: A complete design and performance evaluation,” Ad Hoc Netw., vol. 26, pp. 88–102, 2015.

V.M. Nitti, R. Girau, and L. Atzori, “Trustworthiness management in the social internet of things,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 5, pp. 1253–1266, May 2014.

VI.M. V. Moreno et al., “Applicability of big data techniques to smart cities deployments,” IEEE Trans. Ind. Informat., vol. 13, no. 2, pp. 800–809, Apr. 2017.

VII.P. Guo, T. Jiang, Q. Zhang, and K. Zhang, “Sleep scheduling for critical event monitoring in wireless sensor networks,” IEEE Trans. ParallelDistrib. Syst., vol. 23, no. 2, pp. 345–352, Feb. 2012.

VIII.R. Gomathi and N. Mahendran, “An efficient data packet scheduling schemes in wireless sensor networks,” in Proc. Int. Conf. Electron. Commun.Syst., Feb. 26–27, 2015, pp. 542–547.

IX.T.Qiu,K. Zheng, H. Song, M. Han, and B.Kantarci, “A local-optimization emergency scheduling scheme with self-recovery for smart grid,” IEEETrans. Ind. Inf, doi: 10.1109/TII.2017.2715844.

X.T. Qiu, R. Qiao, and D. Wu, “EABS: An event-aware backpressure scheduling scheme for emergency internet of things,” IEEE Trans. MobileComput., doi: 10.1109/TMC.2017.2702670.

XI.U. Jang, S. Lee, and S. Yoo, “Optimal wake-up scheduling of data gathering trees for wireless sensor networks,” J. Parallel Distrib. Comput., vol. 72, no. 4,pp. 536–546, 2012.

XII.V. Chang, “Towards a big data system disaster recovery in a private cloud,” Ad Hoc Netw., vol. 35, pp. 65–82, 2015.

XIII.X. Shen, C. Bo, J. Zhang, S. Tang, X. Mao, and G. Dai, “EFCon: Energy flow control for sustainable wireless sensor networks,” Ad Hoc Netw., vol. 11, no. 4, pp. 1421–1431, 2013.

XIV.Xue, B. Ramamurthy, and M. C. Vuran, “SDRCS: A servicedifferentiated real-time communication scheme for event sensing in wireless sensor networks,” Comput. Netw., vol. 55, no. 15, pp. 3287–3302, 2011.

XV.X. Xu, X. Li, andM. Song, “Distributed scheduling for real-time data collection in wireless sensor networks,” in Proc. IEEE Global Telecommun.Conf., Dec. 9–13, 2013, pp. 426–431.


View Download

Two Step Verification technique For Detection of Malicious Nodes in Wireless Sensor Networks


Mandeep Kumar, Jahid Ali



The wireless sensor network is the application oriented network which performs task of monitoring and object tracking. The wireless sensor node has the architecture which involves wireless interface for the communication. The design of the wireless sensor network depends upon the significant of application, cost and type of hardware. The architecture of WSN is dynamic due to which security and energy consumption are the major constraints. The Sybil attack is the attack which is possible in wireless sensor networks and it affect network performance. The attacker node generates multiple identities to attract network traffic and leads to denial of service in the network. In this research work, two step verification technique is proposed for the detection of malicious nodes from the network. In the two step verification technique, the cluster heads detect the node as untrusted if its energy consumption is abnormal. The extra observer nodes are deployed in the network, which observe network traffic. On the basis of network traffic observations, the node is declared as trusted or untrusted. When the cluster head and observer node both declare on node as untrusted node, then that sensor node will be considered as malicious node. The experiment is conducted is NS2 by considering certain simulation parameters. It is analyzed that two step verification technique detect malicious nodes successfully and it also leads to improve network performance in terms of Delay, PDR and Packetloss.


wireless sensor network,sensor node,Sybil attack,malicious nodes,observer node,network performance,


I.Akyildiz, I.F., Su, W., Sankarasubramaniam, y., Cyirci, E., ‘Wireless sensor networks: a survey. Computer Networks’, Vol. 38 no.4: p. 393-422, 2002.

II.Abirami, K., Santhi, B. (2013). ‘ Sybil attack in wireless sensor network’, International Journal of Engineering and Technology, 5 (2), pp. 620-623.

III.Abirami, K., Santhi, B. , ‘Sybil attack in wireless sensor network’. International Journal of Engineering and Technology, 5 (2), pp. 620-623.

IV.Alsaedi N, Hashim F, and Sali A. ‘Energy Trust System for Detecting Sybil Attack in Clustered Wireless Sensor Networks’. IEEE 12th Malaysia International Conference on Communications (MICC), Kuching, Malaysia, Nov 2015.

V.Cheng, C., Qian, Y., & Zhang, D. , ‘An Approach Based on Chain Key Predistribution against Sybil Attack in Wireless Sensor Networks’. International Journal of Distributed Sensor Networks, 2013.

VI.Cheikhrouhou O., ‘Secure Group Communication in Wireless Sensor Networks: A survey’, Journal of Network and Computer Applications, Feb. 2016, vol. 61, pp. 115–132.

VII.Douceur J. R., ‘The sybil attack’, in Proc. 1st Int. Workshop Peerto-Peer Syst., London, UK, Mar., 2002, pp. 252−260.

VIII.Demirbas Murat, Song Youngwhan, ‘An RSSI-based Scheme for Sybil Attack Detection in Wireless Sensor Networks’, Proceedings of WoWMoM 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2006. 5 pp. –570.

IX.Di Pietro R., Mancini L. V., Soriente C., Spognardi A.,

X.Dhanalakshmi T.G., Bharathi Dr.N., Monisha M., ‘Safety concerns of Sybil attack in WSN’, IEEE 2014.

XI.Demirbas M. and Song Y., ‘An RSSI-based scheme for sybil attack detection in wireless sensor networks’, Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, 2006, pp.564-570.

XII.Eschenauer L., ‘On Trust Establishment in Mobile Ad-hoc Networks’, in Department of Electrical and Computer Engineering, vol. Master of Science: University of Maryland, College Park, 2002, pp. 45.

XIII.Ganeriwal S., Balzano L. K. and Srivastava M. B., ‘Reputation-based Framework for High Integrity Sensor Networks’, ACM Transactions on Sensor Networks, vol. v, 2007.

XIV.Hsu, K., Leung, M. K., & Su, B., ‘Security Analysis on Defenses against Sybil Attacks in Wireless Sensor Networks’. IEEE Journal.

XV.Karlof, C., Wagner, D., ‘Secure routing in wireless sensor networks: Attacks and Countermeasures’, Ad hoc Networks Journal (Elsevier) 1(2–3) (2003) 293–315.

XVI.Kavitha T.,Sridharan D., ‘Security vulnerabilities in wireless sensor networks: a survey’, J. Inform. Assurance Security , 2010, vol. 5, pp. 31–44.

XVII.Kaschel H., Mardones J., and Quezada G., ‘Safety in wireless sensor networks: types of attacks and solutions’, Stud. Informatics Control, Sept., 2013, vol. 22, no. 3, pp. 323−329.

XVIII.Liu Z., Joy A. W. and Thompson R. A., ‘A Dynamic Trust Model for Mobile Ad-hoc Networks’, in The 10th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS ’04), 2004.

XIX.Levine B. N., Shields C., and Margolin N. B., ‘A survey of solutions to the Sybil attack’,University of Massachusetts Amherst, Amherst, MA,2006.

XX.Leopold M., ‘Sensor network motes: portability and performance’,Ph.D. dissertation, Dept. Comput. Sci., Copenhagen Univ.,Denmark, 2008.

XXI.Muraleedharan R., Yan Y., and Osadciw L. A., ‘Detecting sybil attacks in image senor network using cognitive intelligence’, Proceedings of the First ACM workshop on Sensor and actor networks, 2007, pp. 59-60.

XXII.Prasanna S., Rao S., ‘An Overview of Wireless Sensor Networks Applications and Security’, IJSCE,vol-2(2), May 2012, ISSN: 2231–2307.

XXIII.Putra G.D., Sulistyo S, ‘Trust Based Approach in Adjacent Vehicles to Mitigate Sybil Attacks in VANET’, Proceedings of the 2017 International Conference on Software and e-Business, (ICSEB ‘17) 2017, pp. 117-122.

XXIV.PaulA, Sinha S, and Pal S. ‘An Efficient Method to Detect Sybil Attack using Trust based Model’. Proc. of Int. Conf. on Advances in Computer Science, AETACS, Elsevier, 2013.

XXV.Rathod V., Mehta M., ‘Security in wireless sensor network: a survey’, Ganpat University Journal of Engineering & Technology, vol. 1, pp. 35–44, 2011.

XXVI.Rakesh G.V., Rangaswamy S., Hegde V., Shoba G., ‘A Survey of techniques to defend against Sybil attacks in Social Networks’, IJARSCCE, 2014.

XXVII.Raghunathan V., Schurgers C., Park. S, and Srivastava M. B., ‘Energy-aware wireless microsensor networks’. IEEE Signal Processing Magazine 2002, Volume: 19 Issue: 2, Page(s): 40 –50.

XXVIII.Rashidibajgan S., ‘A trust structure for detection of sybil attacks in opportunistic networks’, 11th International Conference for Internet Technology and Secured Transactions (ICITST) 2016.

XXIX.Sujatha V., Mary Anita E.A., ‘An efficient trust based method for Sybil node detection in mobile wireless sensor network’, Proceedings of the 3rdInternational Conference on Applied Science and Technology (ICAST’18) AIP Conference Proceedings, 2018.

XXX.Singh, Kumar Shio, Singh M. P., and Singh D. K., ‘A survey on network security and attack defense mechanism for wireless sensor networks’, International journal of computer trends and technology 2011, Vol1, no. 2, pp. 9-17.

XXXI.Singh R., Singh J., and Singh R., ‘A novel sybil attack detection technique for wireless sensor networks’, Advances in Computational Sciences and Technology 2017, vol. 10, pp. 185−202.

XXXII.Tsudik G., ‘Data security in unattended wireless sensor networks’,IEEE Trans. Comput., Nov., 2009, vol. 58, no. 11, pp. 1500−1511.

XXXIII.Wang Q., Balasingham I., ‘Wireless Sensor Networks –An Introduction, Wireless Sensor Networks: Application-Centric Design’, 2010.

XXXIV.Wang G, Musau F, Guo S, and Abdullahi M B. ‘Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce’. IEEE Transactions o Parallel and Distributed Systems, December 2013.

XXXV.. Younis, O., &Fahmy, S. ‘HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks’. Mobile Computing, IEEE Transactions on 2004, Vol 3(4), pp. 366-379.

XXXVI.Zhang H, Xu C, and Zhang J. ‘Exploiting Trust and Distrust Information to Combat Sybil Attack in Online Social Networks’. 8th IFIP WG 11.11 International Conference, IFIPTM 2014 Singapore, July 7-10, 2014.

View Download

Performance Analysis of sub interleaver for turbo coded OFDM system


M Rajani Devi, K Ramanjaneyulu, B T Krishna



4G LTE / 5G is the high speed communication system developed for smart phones and other mobile devices in the recent era. The current level of mobile device usage and data exchange over the internet has raised the need for such a fast and secure communication system. One of the important feature in an LTE system is the use of OFDM technique, owing to its advantage namely robustness to multipath fading and interference. This paper proposes an improved OFDM based 4G LTE system fused with turbo code encoding technique to further reduce the bit error rate over noisy real-time channels. The proposed turbo codes system has a hybrid two stage interleaver which is a combination of 3GP interleaver and block interleaver. This interleaver reduces the time required for interleaving processing while maintaining the BER criteria up to the levels. The traditional decoder has been replaced with a threshold-log-MAP algorithm based interleaver for improved noise tolerance. The proposed system has been tested over various channels like Rayleigh, rician and nakagami channels. The experimental results prove that the performance of the stem has improved in comparison by the addition of turbo codes.


turbocode,interleaver,ofdm,decoder,performance of the stem,


I.Bahl, L., Cocke, J., Jelinek, F., Raviv, J.: Optimal decoding of linear codes for minimizing symbol error rate (Corresp.). IEEE Trans. Inf. Theory 20(2), 284–287 (1974).

II.Battail, Gerard: A Conceptual Framework for Understanding Turbo Codes. IEEE J. Sel. Areas Commun. 16(2), 245–254 (1998).

III.Benedetto, S.; Montorsi, G. Design of parallel concatenated convolutional codes. IEEE Trans. Commun. 1996, 44, 591–600.

IV.Berrou, C.; Glavieux,A.; Thitimajshima, P.Near Shannon limit error-correcting coding and decoding: Turbo-codes.In Proceedings of ICC ’93—IEEE International Conference on Communications, Geneva, Switzerland,23–26May 1993; pp. 1064–1070.

V.Bose, R.C.; Ray-Chaudhuri, D.K. On a class of error correcting binary group codes. Inf. Control 1960, 3, 68–79.

VI.Divsalar, D.; Jin, H.;McEliece, R.J. Coding theorems for ―turbo-like‖ codes. In Proceedings of the 36th Annual Allerton Conference on Communication Control and Computing,Monticello, IL, USA, 23–25 September 1998;pp. 201–210.

VII.Dolinar, S., Divsalar, D.: Weight distribution for turbo codes using random and nonrandom permutations. TDA Progress Report 42–122, pp. 56–65 (1995)

VIII.D. Wiegandt, Z. Wu, C. R. Nassar, “High Capacity HighPerformance Low PAPR OFDM via Carrier Interferometry”, IEEE Transactions on Communications, vol. 51, no. 7, pp. 1123-1134, July 2003.

IX.Gallager, R. Low-density parity-check codes. IRE Trans. Inf. Theory 1962, 8, 21–28.

X.Hamming, R.W. Error detecting and error correcting codes. Bell Labs Tech. J. 1950, 29, 147–160.XI.Hocquenghem, A. Codes correcteurs d’erreurs. Chiffres 1959, 2, 147–156.

XII.Houshou Chen, Kuo-Chen Chung, ―A Low Complexity PTS Technique Using Minimal Trellis in OFDM Systems‖, IEEE Trans. onVehicular Technology, vo. 67, no. 1, pp. 817 –821, Jan. 2018.

XIII.Jan, YH. Wireless Pers Commun, ―Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels‖, Wireless Personal Communications, vol. 99, no. 3, pp 1279–1294, Apr. 2018.

XIV.Jason B. Coder, Yao Ma, ―On the susceptibility of coded OFDM to interference: A simulation study‖,United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Jan. 2018.

XV.Khandani, A.K.: Design of turbo-code interleaver using hungarian method. Electron. Lett. 34(1), 63–65 (1998)

XVI.M. Rajani Devi, K. Ramanjaneyulu, B.T. Krishna, “Design of Cascaded Hybrid Interleaver for Fast Turbo Coding and Decoding”, Springer Nature Singapore Pte Ltd, 2017.

XVII.M. Rajani Devi, K. Ramanjaneyulu, B.T. Krishna, ―Performance analysis of sub-interleaver based turbo codes‖, B.T. Cluster Comput (2018).

XVIII.N. Michailow et al., “Generalized frequency division multiplexing for 5th generation cellular networks”, IEEE Trans. Commun., vol. 62, no. 9, pp. 3045-3061, Sep. 2014.

XIX.Peterson, L.L.; Davie, B.S. Computer Networks: A Systems Approach; Elsevier: Amsterdam, The Netherlands, 2007.

XX.Reed, I.S.; Solomon, G. Polynomial codes over certain finite fields. J. Soc. Ind. Appl. Math. 1960, 8, 300–304.

XXI.R. v. Nee and R. Prasad, ―OFDM for Wireless Multimedia Communications‖, Artech House, 2000.

XXII.R. van Nee, A. de Wild, “Reducing the peak-to-average power ratio of OFDM”, Proc. IEEE Vehicular Technology Conf. (VTC’98), pp. 2072-2076, 1998-May.

XXIII.Subramanium, P. & Raut, R.D. J Supercomput, ―AI-enabled turbo-coded OFDM system for improved BER performance‖, The Journal of Supercomputing, pp 1–11, April, 2018.

XXIV.Singh, N., Kaur, G.: Performance analysis of serially concatenated convolutional codes using different generator polynomial and constraint length. Adv. R. Electr. Electron. Eng. 2(1), 73–76 (2014).

XXV.Toshiaki Koike-Akino, Congzhe Cao, Ye Wang, ―Turbo Product Codes with Irregular Polar Coding for High-Throughput Parallel Decoding in Wireless OFDM Transmission‖, 2018 IEEE International Conference on Communications (ICC), May. 2018.

XXVI.Wael Abd-Alaziz, Martin Johnston and Stephane Le Goff, ―Non-binary trellis codes on the synthetic statistical MIMO power line channel‖, IEEE InternationalSymposium on Power Line Communications and its Applications (ISPLC), Apr. 2018.

XXVII.Wang, C.; Sklar, D.; Johnson, D. Forward error-correction coding. Crosslink 2001, 3, 26–29.

View Download

Flag Com: Energy Efficient Secure Routing Protocol


Alok Srivastava, Dr Rajeev Ratan



Mobile communication has become all pervasive in the present day scenario and has gained ubiquitous importance in everyday life. The radio spectrum, which form foundation of mobile communication is a physically limited resources, and is already reaching the threshold of saturation. Co-Operative communication is expected to be the next big change in mobile communication systems. The radio spectrum scarcity, which is prescribed to be reality sooner than we may realize, needs immediate addressing and cooperative communication provides hope of offering solution towards resolution. While looking at radio spectrum scarcity co-operative communication is a hope to resolve this problem. There are however, lot of issues like security, energy consumption, instability of nodes etc, which should be resolved before execution of co-operative communication. In this paper we suggest a protocol Flag-com, which may take care of all these issues. This protocol has been designed in such a way that the major portion of packet processing is done only on source and destination node. This will resolve not only security issue but will also reduce consumption of power at the relay nodes. It will also keep a tab on the movement of relay nodes so that proactive measures like searching and selection of new relay nodes can be done before the relay node moves out of the range. Path selection is another major issue in co-operative communication. Since the transmitted power from any node is very low so effect any type of attenuation will affect the communication. Attenuation in a wireless or mobile network can be divided in two parts (i) attenuation due to nature (ii) attenuation due to interference. Attenuation due to nature cannot be reduced. In this paper we have dealt with both, Markov model has been used to predict the effect of nature on transmitted data packets and tabu search is used to find the path having lowest interference.


Flag Com,Energy consumption, Markovmodel,Tabu search,


I.A. Srivastava, Dr R. Ratan,“Development of a Fresh Approach To Use Cooperative Diversity for Efficient & Effective Communication in Modern Wireless Systems”, IJET, Volume: 7,No3.24(2018): Special issue 24 Pages: 430 -433, Dec. 2018.

II.A. K. Grewal and S. Chakravorty“ Determination of MIMO Channel Capacity and Enhancement of MIMO System Performance Using Tomlinson Harashima Precoding” International Journal of Signal Processing Systems Vol. 3, No. 1, June 2015.

III.Babich and G. Lombardi, “A Markov model for the mobile propagation channel”, IEEE Trans. On Vehicular Technology, vol. 49, No.1 Jan. 2000.

IV.E. Ansari, M.H. Sadreddini, B. S. Bighamb, F. Alimardania, “A combinatorial cooperative-tabu search feature reduction approach”, Scientia Iranica D (2013) 20 (3), 657–662.

V.Elias Z. Tragos, Alexandros Fragkiadakis, Ioannis Askoxylakis and Vasilios. A. Siris “The Impact of Interference on the Performance of a Multi-path Metropolitan Wireless Mesh Network” IEEE Symposium on Computers and Communications (ISCC)Greece, Aug.2011.

VI.E.Ansari,M.H.Sadreddini,B.Sadeghi Bigham and F.Alimardani, “A combinatorial cooperative-tabu search feature reduction approach” Scientia IranicaVolume 20, Issue 3, June 2013, Pages 657-662.

VII.G. Kramer, M. Gastpar, and P. Gupta. “Cooperative strategies and capacity theorems for relay networks”. IEEE Trans. Info. Theory, IT-51:3037–3063, Sept. 2005.

VIII.G.Hua1, D.Song, D.Hench, and C. W.Chen“H.264 Scalable Video over Finite-State MarkovChain Wireless Channels” Proc. of SPIE Vol. 6777, 677702, (2007) · 0277-786X/07/$18 · doi: 10.1117/12.748662.

IX.JNLaneman, DTse, GWWornell,“Cooperative diversity inwireless networks: efficient protocols and outage behavior”,IEEETrans Inform Theory 2004;50(12):3062–80, June 2004.

X.J.Hoydis, R. Couillet, and M.Debbah“Random Beamforming over Correlated FadingChannels”arXiv:1105.0569v1 [cs.IT] 3 May 2011s.

XI.K. Guo, J. Chen, and Y. Huang, “Outage analysis of cooperativecommunication network with hardware impairments,”Frequenz, vol. 69,no. 10, pp. 443-449, Sep. 2015.

XII.K. Xie, X Wang, X Liu, J. Wen, J. Cao “Interference-Aware Cooperative Communication in Multi-radio Multi-channel Wireless Networks” IEEE Transactions on Computers ,Volume: 65, Issue: 5, May 1 2016.

XIII.Murat Uysal, “ Cooperative Communication for improved Wireless Network Transmission”Information Science Reference, New York,2010.

XIV.T. W. Rondeau, C. J. Rieser, T. M. Gallagher, and C. W. Bostian,“Online Modeling of Wireless Channels with Hidden Markov Models and Channel Impulse Responses for Cognitive Radios”, 2004 BEE MTT-S Digest.

XV.W. P.Siriwongpairat, T. Himsoon, W. Su and K. J. R. Liu, “Optimum Threshold-Selection Relaying for Decode-and-Forward Cooperation Protocol”,Proc. Of IEEE WCNC, Las Vegas, USA, Apr. 2006.

View Download

Color and Edge Oriented Histogram for Real-Time Costume Image Retrieval


Dr. Raja Murali Prasad



Costume based real-time image retrieval is challenging task to accomplish. Such an applications is used in many fields like digital photography, multimedia analysis etc. the proposed method uses color and shape features for image feature extraction. The color features are extracted using histogram and shape features are extracted using edge oriented histogram. The proposed algorithm extracts the features accurately and classification is done using SVM classifier. The results prove that the proposed algorithm works well on real-time images.


image retrieval,color histogram,edge oriented histogram,SVM,


I.Chen Jia-Lin, Chen Wan-Yu, Chen I-Kuei, Interactive Clothing Retrieval System, IEEE International Conference on Consumer Electronics (ICCE), 2014:349-350. M. J. Swain, D. H. Ballard, Color Indexing, International Journal of Computer Vision, Vol.7, No.1, 1991:11-32.

II.Choi Yoo-Joo, Kim Ku-Jin, Nam Yunyoung, Retrieval of Identical Clothing Images based on Local Color Histograms, 3rd International Conference on Convergence and Hybrid Information Technology, 2008, 1(1):818-823.

III.Eric Persoon, King-Sun Fu, Shape Discrimination Using Fourier Descrptors, IEEE Transactions on pattern analysis and machine intelligence, VOL. PAMI-8. NO. 3, MAY 1986.

IV.G. Pass, R. Zabin, J. Miller, Comparing Images Using Color Coherence Vectors, In ACM International Conference on Multimedia, Boston, MA, 1996: 65-73.

V.Guang-Hai Liu, Jing-YuYang, Content-based image retrieval using color difference histogram, Pattern Recognition, 46 (2013):188–198.

VI.Guang-Hai Liu, Lei Zhang, Ying-Kun Hou, Zuo-Yong Li, JingYuYang, Image retrieval based on multi-texton histogram, Pattern Recognition, 44(2011):2123-2133.

VII.Guang-Hai Liu, Zuo-Yong li, Lei Zhang, Yong Xu, Image retrieval based on micro-structure descriptor, Pattern Recognition, 43(2010):2380-2389.

VIII.I. Sobel, G. Feldman, “A 3×3 Isotropic Gradient Operator for Image Processing”, presented at the Stanford Artificial Intelligence Project (SAIL) in 1968.

IX.J. HuangS. R. KumarM. MitraW. J. ZhuR. Zabih, Image Indexing Using Color Correlograms, Conference on Computer Vision & Pattern Recognition, 1997:762 –768.

X.Ju-xiang Zhou, Xiao-dong Liu, Tian-wei Xu, Jian-hougan and Wanquan Liu. A new fusion approach for content based image retrieval with color histogram and local directional pattern, Pattern Recognition Letters.

XI.J. Z. Wang, J. Li, G. Wiederholdy, SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.23, no.9, pp.947-963, 2001.

XII.M. J. Swain, D. H. Ballard, Color Indexing, International Journal of Computer Vision, Vol.7, No.1,1991:11-32.

XIII.M. K. Hu, Visual pattern recognition by moment invariant, IEEE Transactions on Information Theory, 1962, 8(2):179-187.

XIV.M. Stricker, and M. Orengo, “Similarity of color images”, In SPIE Conference on Storage and Retrieval for Image and Video Databases , volume 2420, 1995, pp. 381-392, San Jose, USA.

XV.NavneetDalal, Bill Triggs, Histograms of Oriented Gradients for Human Detection, Proceedings of IEEE Computer Society Conference On Computer Vision and Pattern Recognition, IEEE Press, 2005: 886-893.

XVI.OAB Penatti, RD Silva Torres. Color descriptors for web image retrieval: a comparative study. The XXI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI „08), Campo Grande, Brazil, 12–15 Oct. 2008: 163–170.

XVII.R. Haralick, K. Shanmugam and I. Dinstein, Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, VOL. SMC-3, No. 6, 1973(12).

XVIII.Tai Sing Lee, Image Representation using 2D Gabor Wavelets, IEEE Transactions on pattern analysis and machine intelligence, VOL. 18, NO. 10, OCTOBER 1996.

XIX.T. Jabid, M. H. Kabir, O. Chae, Local Directional Pattern for Face Recognition, International Journal of Innovative Computing, Information and Control, Vol.8, No.4, April 2012.

XX.T. Ojala, M. Pietikäinen, T. Mäenpää,Multi-resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.

XXI.Wang Hai-long, Du Jun-li, Research on Clothing Image Retrieval Technology and System, 1st International Symposium on Computer Network and Multimedia Technology, 2009.

XXII.Wang Yatong, Fu Wenlong, Wang Yongbin, Retrieval of Clothing Images Based on Color Feature, International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE), location: Jinan, PEOPLES R CHINA, Date: APR 24-26, 2015, Advances in Intelligent Systems Research, Vol.124, p143-149, 2015.

View Download