ORIGINAL_ARTICLE
A Decentralized Polling System Using Ethereum Technology
Polling system is not trusted everywhere around the world it is very important in this modern world to replace the traditional polling system with the new technology. Some countries like United States, Japan, and India suffer from corrupted polling system. Major issues are faced by current polling systems like system hacking, vote rigging, vote manipulation, distributed denial of service attack, and online polling booth capturing. This paper will lead to the problems faced by the traditional polling system and how the new technology will provide the solution to that problem. Also, our purpose is to check the feasibility of the system by recording the transaction fees and evaluate the right way to spend the amount of gas in the transaction. This will highlight blockchain frameworks including blockchain as a service and polling system which is on blockchain that addresses all constraint introducing ethereum which is a blockchain-based distributed computing platform. Ethereum is open source, and publicly available with a system featuring smart contracts. It provides the cryptocurrency wallets that let you make cheap, instant payments with gas in the form of ethers. The ethereum community is the most active and largest blockchain community in the world. There is no centralized organization that controls ethereum.
https://jitm.ut.ac.ir/article_85645_c45e9b410c55c1316780a26e86bb6d6e.pdf
2022-03-01
1
8
10.22059/jitm.2022.85645
Blockchain
Ethereum
Decentralization
Gas
Distributed System
Metamask
Samarth
Shakya
samarthshakya@gmail.com
1
MSc in Information Security, Department of Information Technology, Institute of Engineering Technology Devi Ahilya University, Indore 452017, India.
LEAD_AUTHOR
Vivek
Kapoor
vkapoor@ietdavv.edu.in
2
Assistant Professor, Department of Information Technology, Institute of Engineering Technology Devi Ahilya University, Indore 452017, India.
AUTHOR
Ben Ayed, A. (2017). A Conceptual Secure Blockchain- Based Electronic Voting System. International Journal of Network Security & Its Applications (IJNSA), 9 (3).
1
Bhosale, K.; Akbarabbas, K.; Deepak, J. & Sankhe, A. (2019). Blockchain based Secure Data Storage. International Research Journal of Engineering and Technology (IRJET), 6 (3).
2
Bulut, R.; Kantarcı, A.; Keskin, S.; Bahtiyar, S. (2018). Blockchain-Based Electronic Voting System for Elections in Turkey. Istanbul Technical University Istanbul, Turkey.
3
Chan Zheng Wei, Clement; Chai Wen Chuah (2018): Blockchain-Based Electronic Voting Protocol. International Journal On Informatics Visualization, 2 (4).
4
Bergquist, Jonatan (2017): Blockchain Technology and Smart Contracts. Uppsala Universitet Examensarbete 30 hp .
5
Hatiskar, Vaibhav; G. Pai, Archana (2018): Blockchain and it’s Integration with Supply Chain. International Journal of Computer Applications (0975 – 8887), 179 (52).
6
Kaan Koç, Ali; Yavuz, Emre; Can Çabuk, Umut; Dalkılıç, Gökhan (2018): Towards Secure E-Voting Using Ethereum Blockchain. researchgate.net/publication/323318041.
7
McCorry, Patrick; F. Shahandashti Siamak; Hao Feng (2017): A Smart Contract for Boardroom Voting with Maximum Voter Privacy. School of Computing Science, Newcastle University UK.
8
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. "A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access 7 (2019): 58221-58240.
9
Pareek, Shubham; Upadhyay, Anuj; Doulani, Satya; Tyagi, Siddarth; Varma, Aditya(2018): E-Voting using Ethereum Blockchain. International Journal for Research Trends and Innovation, 3 (11).
10
Shrinivas, Manoj; S.Chandan; Farhan Shamail, Mohammed; K, Ramyashree (2019): A Decentralized Voting Application using Blockchain Technology. International Research Journal of Engineering and Technology (IRJET), 6 (4).
11
Tso, Raylin; Liu, Zi-Yuan; Hsiao, Jen-Ho(2019): Distributed E-Voting and E-Bidding Systems Based on Smart Contract. Multidisciplinary Digital Publishing Institute.
12
Arun; Dutta, Aditya; Rajeev, Sourav; Mathew Varghese, Rohan (2019): E-Voting using a Decentralized Ethereum Application. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, 8 (4).
13
www.dappuniversity.com/articles/the-ultimate-ethereum-dapp-tutorial (Building an Ethereum Decentralized Application)
14
ORIGINAL_ARTICLE
Exploring Story Cards for Evaluating Requirement Understanding in Agile Software Development
From the recent literature review, it is evident that existing agile methodology lacks the method to evaluate the requirement understanding of agile team members for a given set of requirement chosen for agile software development. Hence, there is a need to introduce a requirement understanding check to ensure every agile team member follows the given requirement clearly without any ambiguity. To fill this existing gap, this research paper proposes to extend the usage of story cards to evaluate the understanding of the given requirement and to highlight any challenges and risks in the early stage of requirement understanding under agile software development methodology, if any. This paper primarily focuses to introduce a robust requirement understanding evaluation process in agile methodology. The research results were found to be motivating and were analyzed by comparing the data-points using time-series for performing agile query analysis, agile team velocity analysis and agile team involvement analysis for two agile teams where one team delivered the sprint output using agile traditional method while another team opted for proposed approach. A considerable decrease of 33.07% was observed in the number of queried raised and a significant increase of 26.36% in agile velocity was observed for agile sprint under proposed approach when compared to agile traditional approach. Also, a significant shift from 40%-80% team involvement under traditional agile method was uplifted to 80%-90% team involvement under proposed approach.
https://jitm.ut.ac.ir/article_85646_823e80a71af0916daf4fa832ee8572d1.pdf
2022-03-01
9
22
10.22059/jitm.2022.85646
Software Engineering
Agile Methodology
Requirement Understanding
Story Cards
Sarika
Sharma
sarika.s17@gmail.com
1
Research Scholar, Amity Institute of Information Technology, Amity University, Sector-125, Noida, 201307, (U.P.) India
LEAD_AUTHOR
Deepak
Kumar
deepakgupta_du@rediffmail.com
2
Professor, Amity Institute of Information Technology, Amity University, Sector-125, Noida, 201307, (U.P.) India.
AUTHOR
Al-Garni, B. M. (2018). Analysis And Review Of Prescribing Clinical Decision Support System Within The Context Of Nhs Secondary Sector. Life: International Journal of Health and Life-Sciences. https://doi.org/10.20319/lijhls.2018.43.6071
1
Alam, S., Nazir, S., Asim, S., & Amr, D. (2017). Impact and Challenges of Requirement Engineering in Agile Methodologies: A Systematic Review. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2017.080455
2
Alyahya, S., Alqahtani, M., & Maddeh, M. (2016). Evaluation and improvements for agile planning tools. 2016 IEEE/ACIS 14th International Conference on Software Engineering Research, Management and Applications, SERA 2016. https://doi.org/10.1109/SERA.2016.7516149
3
Baruah, N. (2015). Requirement management in agile software environment. Procedia Computer Science. https://doi.org/10.1016/j.procs.2015.08.414
4
Beck, K. (2004). Extreme Programming explained: Embrace change. Reading, Mass. In Addison-Wesley.
5
Beck, K., & West, D. (2004). User Stories in Agile Software Development. Scenarios, Stories, Use Cases: Through the Systems Development Life-Cycle.
6
Cohen, M. (2004). User Stories Applied. Engineering.
7
Dalpiaz, F., & Brinkkemper, S. (2018). Agile requirements engineering with user stories. Proceedings - 2018 IEEE 26th International Requirements Engineering Conference, RE 2018. https://doi.org/10.1109/RE.2018.00075
8
Daneva, M., Van Der Veen, E., Amrit, C., Ghaisas, S., Sikkel, K., Kumar, R., Ajmeri, N., Ramteerthkar, U., & Wieringa, R. (2013). Agile requirements prioritization in large-scale outsourced system projects: An empirical study. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2012.12.046
9
Dimitrijević, S., Jovanovic, J., & Devedžić, V. (2015). A comparative study of software tools for user story management. Information and Software Technology. https://doi.org/10.1016/j.infsof.2014.05.012
10
Jeffries, R. (2001). Essential XP: Card, conversation, confirmation. Ronjeffries.Com.
11
Kavitha, C. ., & Thomas, S. M. (2011). Requirement Gathering for small Projects using Agile Methods. IJCA Special Issue on Computational Science - New Dimensions & Perspectives.
12
Khan, M. I., Din, Z. U., Abid, M. A., & Naeem, T. (2019). User Story Characteristics Affecting Software Cost in Agile Software Development: A Systematic Literature Review. International Journal of Computer Science and Network Security.
13
López-Martínez, J., Ramírez-Noriega, A., Juárez-Ramírez, R., Licea, G., & Jiménez, S. (2017). User stories complexity estimation using bayesian networks for inexperienced developers. Cluster Computing. https://doi.org/10.1007/s10586-017-0996-z
14
Lucassen, G., Dalpiaz, F., Van Der Werf, J. M. E. M., & Brinkkemper, S. (2015). Forging high-quality User Stories: Towards a discipline for Agile Requirements. 2015 IEEE 23rd International Requirements Engineering Conference, RE 2015 - Proceedings. https://doi.org/10.1109/RE.2015.7320415
15
Miranda, E., Bourque, P., & Abran, A. (2009). Sizing user stories using paired comparisons. Information and Software Technology. https://doi.org/10.1016/j.infsof.2009.04.003
16
Moreno, A. M., & Yagüe, A. (2012). Agile user stories enriched with usability. Lecture Notes in Business Information Processing. https://doi.org/10.1007/978-3-642-30350-0_12
17
O’hEocha, C., & Conboy, K. (2010). The role of the user story agile practice in innovation. Lecture Notes in Business Information Processing. https://doi.org/10.1007/978-3-642-16416-3_3
18
Okesola, J., Adebiyi, M., Okokpujie, K., Odepitan, D., Goddy-Worlu, R., Iheanetu, O., Omogbadegun, Z., & Adebiyi, A. (2019). A systematic review of requirement engineering practices in agile model. International Journal of Mechanical Engineering and Technology.
19
Rida, A., Nazir, S., Tabassum, A., Sultan, Z., & Abbas, R. (2016). Role of Requirements Elicitation & Prioritization to Optimize Quality in Scrum Agile Development. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2016.071239
20
Shim, W., & Lee, S. W. (2019). An agile approach for managing requirements change to improve learning and adaptability. Journal of Industrial Information Integration. https://doi.org/10.1016/j.jii.2018.07.005
21
Stettina, C. J., & Hörz, J. (2015). Agile portfolio management: An empirical perspective on the practice in use. International Journal of Project Management. https://doi.org/10.1016/j.ijproman.2014.03.008
22
Strode, D. (2012). a Theory of Coordination in Agile Software Development Projects. Researcharchive.Vuw.Ac.Nz.
23
Trkman, M., Mendling, J., & Krisper, M. (2016). Using business process models to better understand the dependencies among user stories. Information and Software Technology. https://doi.org/10.1016/j.infsof.2015.10.006
24
Wake, B. (2003). INVEST in Good Stories, and SMART Tasks. XP123: Exploring Extreme Programming.
25
Zhu, X. (2017). Agile mining: a novel data mining process for industry practice based on Agile Methods and visualization. Opus.Lib.Uts.Edu.Au.
26
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. "A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access 7 (2019): 58221-58240.
27
ORIGINAL_ARTICLE
A secure and robust stereo image encryption algorithm based on DCT and Schur decomposition
Security solutions of stereo images are always a major concern during the transmission and communication. In this manuscript, a simple yet efficient framework for encrypting stereo images is formulated using discrete cosine transform (DCT), generalized logistic map, Schur decomposition and magic square method. The framework initiated with the integration of DCT and generalized logistic map to unify both pair of images. This unified image is then encrypted using the Schur decomposition and magic square method. The various experiments and analysis have been done to explore the validity, proficiency and performance of the purport framework.
https://jitm.ut.ac.ir/article_85647_d56a7075f5f5bf0e4d9b5a35047a5588.pdf
2022-03-01
23
43
10.22059/jitm.2022.85647
Stereo Image
Encryption and Decryption
Discrete Cosine Transform
Generalize Logistic Map
Schur Decomposition
S.
Kumar
sanoj.kumar@ddn.upes.ac.in
1
Assistant Prof., Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India
LEAD_AUTHOR
Singh
M. K.
mkumar@ddn.upes.ac.in
2
Assistant Professor, Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India.
AUTHOR
G.
Dobhal
gdobhal@ddn.upes.ac.in
3
Assistant Professor, Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India
AUTHOR
D.
Saini
deepikasaini@geu.ac.in
4
Assistant Professor, Department of Mathematics, Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, India.
AUTHOR
G.
Bhatnagar
goravb@iitj.ac.in
5
Associate Professor, Department of Mathematics, Indian Institute of Technology Jodhpur, Rajasthan, 342037, India.
AUTHOR
Agreste S. & Andaloro G. (2008). A new approach to pre-processing digital image for wavelet-based transform. Journal of Computational and Applied Mathematics, 221(2), 274-283.
1
algorithm with mixed modulation incorporated. Information Sciences, 519, 161-182.
2
Ali M., Ahn C. W., Pant M., Kumar S., Singh M. K., Saini D. (2020). An optimized digital watermarking scheme based on invariant DC coefficients in spatial domain. Electronics, 9 (9), 14-28.
3
Alturki F. T., Almutairi A., & Mersereauu R, M. (2007). Analysis of blind data hiding using discrete cosine transform phase modulation. Signal Processing and Image Communication, 22(4), 347–362.
4
Amirgholipour S.K. & Naghsh-Nilchi A. R. (2009). Robust digital image watermarking based on joint DWT-DCT. IEEE Transaction on Image Processing, 3(2), 42–54.
5
based on spatiotemporal chaotic system. Optik, 217, 64884,
6
based secret image sharing with authentication. Signal Processing, 173, 107571.
7
Bhat K. V., Sengupta I., & Das A. (2011). A new audio watermarking scheme based on singular value decomposition and quantization. Circuit, System and Signal Processing, 30(5), 915–927.
8
Bhatnagar G. & Wu Q. M. J. (2012). Selective image encryption based on pixels of interest and singular value decomposition. Digital Signal Processing, 22, 648-663.
9
Chang C. C., Hwang M. S., & Chen T. S. (2001). A new encryption algorithm for image cryptosystems. The Journal of System and Software, 58, 83-91.
10
Chuang T. & Lin J. (1999). A new multiresolution approach to still image encryption. Pattern Recognition and Image Analysis, 9(3), 431–436.
11
compressed images. In: Advanced Concepts for Intelligent Vision Systems, (pp. 90–97).
12
Dong H., Lu P., & Ma X. (2011). Image scrambling algorithm based on mixed chaotic systems and extended zigzag transformation. Computer Engineering and Design, 32(4), 1241–1245.
13
Droogenbroeck M. & Benedett R. (2002). Techniques for a selective encryption of uncompressed and
14
Guo J.I. & Yen J. C. (2000). A new mirror-like image encryption algorithm and its VLSI architecture. Pattern Recognition and Image Analysis, 10(2), 236–247.
15
Haq T.U. & Shah T. (2020). 12×12 S-box Design and its Application to RGB Image Encryption. Optik,
16
Hu H.T., Hsu L. Y., & Chou H. H. (2020). An improved SVD-based blind color image watermarking
17
Huijuan X., Shuisheng Q., Chengliang D. , Zhong H. Y., & Ying C. (2007, November). A composite image encryption scheme using aes and chaotic series. In: The First International Symposium on Data, Privacy, and E-Commerce (ISDPE), (pp. 277–279).
18
Images. In Transactions on Data Hiding and Multimedia Security IX, (pp. 25–41).
19
Knockaert L., Backer B., & Zutter D. (1999). SVD compression, unitary transforms, and computational complexity. IEEE Transaction on Signal Processing, 47(10), 2724-2729.
20
Kumar S., Bhatnagar G. (2019). SIE: an application to secure stereo images using encryption. In Handbook of Multimedia Information Security: Techniques and Applications, (pp. 37-61).
21
Kumar S., Bhatnagar G., Raman B., Sukavanam N. (2012). Security of stereo images during communication and transmission. Advanced Science Letters, 6 (1), 173-179.
22
Lin C., Wu M., Bloom M. J., Cox I. J., Miller M., & Lui Y. (2001). Rotation, scale, and translation resilient watermarking for images. IEEE Transaction on Signal Processing, 10(5), 767-782.
23
Lin K.T. (2011). Hybrid encoding method by assembling the magic-matrix scrambling method and the binary encoding method in image hiding. Optics Communication, 284, 1778–1784.
24
Liu X. (2004). Four alternative patterns of the Hilbert curve. Applied Mathematics and Computation, 147(3), 675-685.
25
Maniccam S.S. & Bourbakis N.G. (2004). Image and video encryption using SCAN patterns. Pattern Recognition, 37(4), 725–737.
26
Menezes A. J., Oorschot P. C. V., & Vanstone S. (1997). In Handbook of Applied cryptography, Vol. 1. CRC Press.
27
Mohammad A., Alhaj A., & Shaltaf S. (2008). An improved SVD-based watermarking scheme for protecting rightful ownership. Signal Processing, 88(9), 2158–2180.
28
Qin Y., Wang H., Wang Z., Gong Q., & Wang D. (2016). Encryption of QR code and grayscale image in interference-based scheme with high quality retrieval and silhouette problem removal. Optics and Lasers in Engineering, 84, 62-73.
29
Shannon C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.
30
Singh M. K., Kumar S., Ali M., Saini D. (2020). Application of a novel image moment computation in X-ray and MRI image watermarking. IET Image Processing, 15(3), 666-682.
31
Singh S. P. & Bhatnagar G. (2018). A new robust watermarking system in integer DCT domain. Journal of Visual Communication and Image Representation, 53, 86-101.
32
Stinson D. R. & Paterson M. (2018). In Cryptography: Theory and Practice, Vol. 1. CRC Press.
33
Wang X. & Yang J. (2020). A novel image encryption scheme of dynamic S-boxes and random blocks
34
Xiong L., Zhong X., & Yang C. N. (2020). DWT-SISA: a secure and effective discrete wavelet transform
35
Yahya A. & Abdalla A. (2008). A shuffle image encryption algorithm. Journal of Computer Science,
36
Yamaguchi Y. (2014). Extended Visual Cryptography Scheme for Multiple-Secrets Continuous-Tone
37
Wang X., Liu L., & Zhang Y. (2015). A novel chaotic block image encryption algorithm based on dynamic random growth technique. Optics and Lasers in Engineering, 66, 10–18.
38
Li J. (2016). Asymmetric multiple-image encryption based on octomom Fresnel transform and sine logistic modulation map. Journal of the Optical Society of Korea, 20(3), 341–357.
39
Jithin K. & Sankar S. (2020). Colour image encryption algorithm combining, Arnold map, DNA sequence operation, and a Mandelbrot set. Journal of Information Security and Applications, 50(102428).
40
Li J., Xiang S., Wang H., Gong J., & Wen A. (2018). A novel image encryption algorithm based on synchronized random bit generated in cascade-coupled chaotic semiconductor ring lasers. Optics and Lasers in Engineering, 102, 170-180.
41
ORIGINAL_ARTICLE
STCP: A Novel Approach for Congestion Control in IoT Environment
The main idea of IoT is to connect several objects to each other through Internet. In the field of Computer Network the main problem identified by researchers is network congestion. Now a day’s network congestion is increasing very rapidly because IoT connect a huge number of devices to internet. A transport layer protocol TCP (Transmission Control Protocol) is accountable for network congestion control. The behavior of TCP is not stable as it takes long time to fill the available capacity of the network. It also continuously keeps assessing the capacity of data transmission through increasing the limits.TCP drops its data transmission rate aggressively when packets are dropped, which significantly reduces the throughput. This paper suggests a new approach, stable transmission control protocol for IoT applications. The experimental results show that stable transmission control protocol achieves better performance in terms of goodput.
https://jitm.ut.ac.ir/article_85648_33dad5815f7414a9c5454888594cba21.pdf
2022-03-01
44
51
10.22059/jitm.2022.85648
Internet of Things
Protocol
Internet
Computer Network
Ajay Kumar
Gupta
ajayguptagorakhpur@gmail.com
1
Ph.D. Candidate, SCA, IFTM University, Moradabad, UP, India.
AUTHOR
Devendra
Singh
dev625g@gmail.com
2
Associate Professor, Ph.D., SCA, IFTM University, Moradabad, UP, India.
AUTHOR
Karan
Singh
karancs12@gmail.com
3
Assistant Professor, Ph.D., SCSS, Jawaharlal Nehru University, New Delhi, India.
LEAD_AUTHOR
Lal Pratap
Verma
er.lpverma1986@gmail.com
4
Associate Professor, CSE Deptt, Ph.D., Moradabad Institute of Technology, Moradabad, UP, India.
AUTHOR
Allman M,Paxson V, Stevens W (1999) TCP Congestion Control.IETF Internet Drfat.https://tools.ietf.org/html/rfc2581. Accessed 14 June 2019
1
Chu J, Dukkipati N, Cheng Y, Mathis M (2013) Increasing TCP's Initial Window.IETF Internet Drfat.https://tools.ietf.org/html/rfc6928.Accessed20 April 2019
2
DovrolisC, RamanathanP, MooreD (2004) Packet-Dispersion Techniques and a Capacity-Estimation Methodology.IEEE/ACM Transaction on Networking. 12:963-977.
3
DukkipatiN, ReficeT, ChengY (2010)An argument for increasing TCP’s initial congestion window.ACM SIGCOMM Computer Communication Revview. 40:27–33.
4
Floyd S, AllmanM, Jain A,Sarolahti P (2007) Quick-Start for TCP and IP.IETF Internet Drfat.https://tools.ietf.org/html/rfc4782. Accessed14 June 2019
5
Floyd S, Henderson T (1999) The NewReno modification to TCP’s fast recovery algorithm.IETF Internet Drfat. https://tools.ietf.org/html/rfc2582.Accessed 14 June 2019
6
Floyd S, HendersonT, Gurtov A (2004) The NewReno modification to TCP’s fast recovery algorithm.IETF Internet Drfat. http://tools.ietf.org/html/rfc3782. Accessed 14 June 2019.
7
HaS, Rhee I, Xu L (2008) CUBIC: a new TCP-friendly high-speed TCP variant.ACM SIGOPS Operating Systems Review. 42:64–74.
8
JacobsonV (1988) Congestion Avoidance and Control.ACM SIGCOMM Computer Communication Review. 18:314–329.
9
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. (2019) "A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access 7: 58221-58240.
10
MathisM,Mahdavi J (1996) Forward acknowledgement: refining TCP congestion control.ACM SIGCOMM Computer Communications Review. 26:281–291.
11
MQTT Project (2015) http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/errata01/os/mqtt-v3.1.1-errata01-os-complete.doc.
12
Postel J (1981) Transmission Control Protocol.IETF Internet Drfat.https://tools.ietf.org/html/rfc793. Accessed 14 June 2019
13
RESTful HTTP http://www.infoq.com/articles/designing-restful-http-apps-roth.
14
Saint-Andre P (2011) Extensible Messaging and Presence Protocol (XMPP): Core, https://tools.ietf.org/html/rfc6120.
15
Sallantin R, Baudoin C, ChaputE, ArnalF, Dubois E,Beylot A (2013) Initial Spreading: a Fast Start–Up TCP Mechanism.in IEEE 38th annual conference on Local Computer Network. 492 – 99.
16
Tan K, SridharanM, Bansal D, Thaler D (2008) Compound TCP: A New TCP Congestion Control for High-Speed and Long Distance Networks. IETF Internet Drfat.https://tools.ietf.org/html/draft-sridharan-tcpm-ctcp-02. Accessed 19 June 2019
17
Wang G, RenY, LiJ (2014) An effective approach to alleviating the challenges of transmission control protocol.IET Communication. 8:860-869.
18
ORIGINAL_ARTICLE
Mapping Grayscale Images to Colour Space Using Deep Learning
People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.
https://jitm.ut.ac.ir/article_85649_9c9b473afbcfd184d9407d00a7e12c1c.pdf
2022-03-01
52
68
10.22059/jitm.2022.85649
Convolutional Neural Networks (CNN)
Convolution
RGB
CIELAB (Lab)
Deep Neural Networks
Feature vector
Prediction
sampling
Anu
Saini
anuanu16@gmail.com
1
Assistant Professor, Ph.D., Department of Computer Science and Engineering, G. B. Pant Govt. Engineering College, New Delhi, India.
LEAD_AUTHOR
Jyoti
Tripathi
loginjyoti@gmail.com
2
Assistant Professor, Department of Computer Science and Engineering, G.B. Pant Govt. Engineering College, New Delhi, India.
AUTHOR
Chang, H., Fried, O., Liu, Y., DiVerdi, S., & Finkelstein, A. (2015). Palette-based photo recoloring. ACM Transactions on Graphics (TOG), 34(4), 139.
1
Chen, X., Zou, D., Zhao, Q., & Tan, P. (2012). Manifold preserving edit propagation. ACM Transactions on Graphics (TOG) 31(6), 132.
2
Cheng, Z., Yang, Q., & Sheng, B. (2015). Deep Colorization. IEEE International Conference on Computer Vision (ICCV), 415–423.
3
Chia, A.Y.S., Zhuo, S., Gupta, R.K., Tai, Y.W., Cho, S.Y., Tan, P., & Lin, S. (2011). Semantic colorization with internet images. ACM Transactions on Graphics (TOG). ACM, 30, 156.
4
Dahl, R. (2016). Automatic colourization. Retrieved from http://tinyclouds.org/colourize
5
Deshpande, A., Rock, J., & Forsyth, D. (2015). Learning Large-Scale Automatic Image Colourization. IEEE International Conference on Computer Vision (ICCV), 567–575.
6
Gupta, R.K., Chia, A.Y.S., Rajan, D., Ng, E.S., & Zhiyong, H. (2012). Image colorization using similar images. In Proceedings of the 20th ACM international conference on Multimedia (ACMM '12). Nara, Japan: ACM, 369–378.
7
Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., & Salesin. D.H. (2001). Image analogies. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques. Los Angeles, CA: ACM.
8
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9
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2016). Let there be Colour: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colourization with Simultaneous Classification. ACM Transactions on Graphics (TOG), 35(4), Article 110.
10
Irony, R., Cohen-Or, D., & Lischinski, D. (2005). Colorization by example. Eurographics Symp. on Rendering. Citeseer,2.
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Isola, P., Zhu, J.Y., Zhou, T., & Efros, A.A. (2017). Image-to-image translation with conditional adversarial networks. CVPR.
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Li, X., Zhao, H., Nie, G., and Huang, H. (2015). Image recoloring using geodesic distance based color harmonization. Computational Visual Media 1(2), 143– 155.
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Li, Y., Adelson, E., & Agarwala, A. (2008). ScribbleBoost: Adding Classification to Edge-Aware Interpolation of Local Image and Video Adjustments. Computer Graphics Forum. Wiley Online Library, 27, 1255–1264.
16
Liu, X., Wan, L., Qu, Y., Wong, T.T., Lin, S., Leung, C.S., & Heng, P.A. (2008). Intrinsic colorization. ACM Transactions on Graphics (TOG). ACM, 27, 152.
17
Liu, Y., Cohen, M., Uyttendaele, M., & Rusinkiewicz, S. (2014). AutoStyle: automatic style transfer from image collections to users’ images. Computer Graphics Forum. Wiley Online Library, 33, 21–31.
18
Sangkloy, P., Lu, J., Fang, C., Yu, F., & Hays, J. (2017). Scribbler: Controlling Deep Image Synthesis with Sketch and Color. CVPR (2017).
19
Simo-Serra, E., Iizuka, S., Sasaki, K., & Ishikawa, H. (2016). Learning to simplify: fully convolutional networks for rough sketch cleanup. ACM Transactions on Graphics (TOG). ACM, 35(4), 121.
20
Wang, B., Yu, Y., Wong, T.T., Chen, C., & Xu, Y.Q. (2010). Data-driven image color theme enhancement. ACM Transactions on Graphics (TOG). ACM, 29, 146.
21
Welsh, T., Ashikhmin, M., & Mueller, K. (2002). Transferring color to greyscale images. ACM Transactions on Graphics (TOG). ACM, 21(3), 277–280.
22
Yan, Z., Zhang, H., Wang, B., Paris, S., & Yu, Y. (2016). Automatic photo adjustment using deep neural networks. ACM Transactions on Graphics (TOG). ACM, 35(2), 11.
23
Zhang, R., Isola, P., & Efros, A.A. (2016). Colourful Image Colourization. Computer Vision – ECCV 2016, 649-666.
24
Zhang, R., Zhu, J.U., Isola, P., Geng, X., Lin, A.S., Yu, T., & Efros, A.A. (2017). Real-Time User-Guided Image Colourization with Learned Deep Priors. SIGGRAPH.
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Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. (2014). Learning deep features for scene recognition using places database. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS '14). Montreal, QC: MIT Press, 487-495.
26
ORIGINAL_ARTICLE
Epidemiological Model for Stability Analysis of Wireless Sensor Network under Malware Attack
Malware attack is growing day by day in cyberspace. And Wireless Sensor Network (WSN) is also facing a hazardous type of situation due to attack of malware (malicious code, virus, worm etc.). Malwares target sensor nodes easily because, nodes are equipped with limited resources. Hence, security of WSN against malware attack is one of the imperative requisite. Malware spreads in the entire network wirelessly, which initiates from single infectious node and spread in the whole WSN. In this way the complete network comes under the security threat. Therefore, it is mandatory to apply the security technique through which to secure WSN against malware attacks. To secure WSN due to malware attacks a quarantine based model has been proposed. The proposed model consists of various epidemic states namely: Susceptible Carrier - Infectious - Quarantine - Recovered - Susceptible (SCIQRS). The model explained the propagation dynamics of malware in WSN and proposed a technique to prevent its propagation. The technique of quarantine along with recovery is to much effective in prevailing of malware propagation in WSN. For the determination of WSN stability and equilibrium points the expression of basic reproduction number has been obtained. Malware propagation is affected by different network parameters, which has been also discussed. The comparative investigation of proposed model has been carried out with existing model. The proposed model has been substantiated by simulation outcomes
https://jitm.ut.ac.ir/article_86484_9d027bcd3f11c3f98bb54bfa396f931e.pdf
2022-03-01
69
88
10.22059/jitm.2022.86484
Basic Reproduction Number
Malware Security
Stability
Wireless Sensor Network
Chakradhar
Verma
chakradharverma@gmail.com
1
Research Scholar, Rajasthan Technical University, Kota ,Rajasthan-324010, India,
LEAD_AUTHOR
C. P.
Gupta
guptacp2@rediffmail.com
2
Professor, Department of Computer Science and Engineering, Rajasthan Technical University, Kota Rajasthan -324010, India,
AUTHOR
Alaba, A., Othman, M., Hashem, T., and Alotaibi, F., Internet of Thing security: A survey. J Netw Comput Appl, Volume 88, pages 10-28, 2017.
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De,P.,Liu, Y.,and Das, S. K.,Deployment-aware modeling of node compromise spread in wireless sensor networks using epidemic theory, ACM Transactions on Sensor Networks, vol.5(3),pp.1-33.(2009).
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Feng, Liping and Song, Lipeng and Zhao, Qingshan and Wang, Hongbin, Modeling and Stability Analysis of Worm Propagation in Wireless Sensor Network, Mathematical Problems in Engineering, volume 2015, number Article ID 129598, pages 1-8,2015.
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Haghighi.,S., M., Wen, S.,Xiang,Y.,Quin, B., and Zhou, W.On the Race of Worms and Patches: Modeling the Spread of Information in Wireless Sensor Networks", IEEE Transactions on Information Forensics and Security, Volume 11, Number12,Pages 2854-2865, 2016.
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45
ORIGINAL_ARTICLE
Energy Consumption Analysis of Iot-Manet Based Systems with Anova Assessment for Tackling Covid-19
The epidemic situation generated as a result of COVID -19 crossways the sphere observed the practices of various emerging technology like Internet of Thing (IoT) along with norm of dynamic fields. The wireless communication based on networks such as wireless mesh networks (WMN) and Mobile Ad-hoc networks (MANETS) proven to be very successful for monitoring of patients remotely. The MANET protocols that are simulated in this study are Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid Wireless Mesh Protocol (HWMP). In this investigation work, most appropriate routing protocols to knob DDoS attacks are simulated using NS-2 and assessed in terms of average energy consumption in the state of changing speed connections among devices called mesh nodes. Further ANOVA test is utilized for further accessing for the best suited routing protocol for handling the data packets, which is HWMP , considerable less susceptible for DDoS assaults dominant in healthcare field.
https://jitm.ut.ac.ir/article_86485_b8e6e7e79d1db75afa17b87b6534de92.pdf
2022-03-01
89
102
10.22059/jitm.2022.86485
COVID -19
AODV
SAODV
HWMP
DDoS attacks
Energy consumption
ANOVA
IoT-MANET
e- Healthcare sector
Ashu
Gautam
ashuone@gmail.com
1
Department of ECE, MRIIRS, Faridabad, India 121001.
LEAD_AUTHOR
Rashima
Mahajan
rashima.fet@mriu.edu.in
2
Department of CSE, MRIIRS, Faridabad, Inida,1210011.
AUTHOR
Sherin
Zafar
zafarsherin@gmail.com
3
Department of CSE, SEST, Jamia Hamdard, New Delhi, India 110062.
AUTHOR
Agrawal, V. (2014, October). Security and privacy issues in wireless sensor networks for healthcare. In International Internet of Things Summit (pp. 223-228). Springer, Cham.
1
Alameri, I. A., & Komarkova, J. (2020, June). A Multi-Parameter Comparative Study of MANET Routing Protocols. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.
2
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. "A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access 7 (2019): 58221-58240.
3
Alharbi, T., Aljuhani, A., & Liu, H. (2018). SYN Flooding Detection and Mitigation using NFV. International Journal of Computer Engineering and Information Technology, 10(1), 11-19.
4
Brill, C., & Nash, T. (2017, December). A comparative analysis of MANET routing protocols through simulation. In 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST) (pp. 244-247). IEEE.
5
Cai, C., Fu, J., Qiu, H., & Lu, Y. (2020, October). An Active Idle Timeslot Transfer TDMA for Flying Ad-Hoc Networks. In 2020 IEEE 20th International Conference on Communication Technology (ICCT) (pp. 746-751). IEEE.
6
Chelli, K. (2015, July). Security issues in wireless sensor networks: Attacks and countermeasures. In Proceedings of the World Congress on Engineering (Vol. 1, No. 20).
7
Cogliati, D., Falchetto, M., Pau, D., Roveri, M., & Viscardi, G. (2018, September). Intelligent cyber-physical systems for industry 4.0. In 2018 First International Conference on Artificial Intelligence for Industries (AI4I) (pp. 19-22). IEEE.
8
Gautam, A., Mahajan, R. and Zafar,S.(2019).Implementing Blockchain Security to Prevent DDoS Attacks in Networks”, International Journal of Security and Its Applications (IJSIA), Vol 13, No.4, pp 27-40.
9
Gautam, A., Mahajan, R., & Zafar, S. (2020). QoS Optimization in Internet of Medical Things for Sustainable Management. In Cognitive Internet of Medical Things for Smart Healthcare (pp. 163-179). Springer, Cham.
10
Gautam,A.,Mahajan, R., & Zafar, S. (2021). DDoS Attacks Impact on Data Transfer in IOT-MANET-Based E-Healthcare for Tackling COVID-19. In Data Analytics and Management (pp. 301-309). Springer, Singapore.
11
Singh,G and Singh,K.(2019).Detection and Prevention of Vulnerabilities in Open Source Software: An experimental study. Journal of Discrete Mathematical Sciences and Cryptography, Taylor & Francis, Vol 22, No 8.
12
Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2015). The internet of things for health care: a comprehensive survey. IEEE access, 3, 678-708.
13
Jain, A. K., & Tokekar, V. (2015, January). Mitigating the effects of Black hole attacks on AODV routing protocol in mobile ad hoc networks. In 2015 International Conference on Pervasive Computing (ICPC) (pp. 1-6). IEEE.
14
Khalaf, O. I., & Abdulsahib, G. M. (2020). Energy Efficient Routing and Reliable Data Transmission Protocol in WSN. Int. J. Advance Soft Compu. Appl, 12(3).
15
Kumari,D., Singh,K. and Manjul,M.(2020).Performance Evaluation of Sybil Attack in Cyber Physical
16
System.In Proceedia of Computer Science Elsevier, India Volume 167, (pp 1013-1027).
17
Sanzgiri, K., Dahill, B., Levine, B. N., Shields, C., & Belding-Royer, E. M. (2002, November). A secure routing protocol for ad hoc networks. In 10th IEEE International Conference on Network Protocols, 2002. Proceedings. (pp. 78-87). IEEE.
18
Sharma, A. K., & Trivedi, M. C. (2016, February). Performance comparison of AODV, ZRP and AODVDR routing protocols in MANET. In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 231-236). IEEE.
19
Siwach, V., Sehrawat, H., & Singh, Y. (2020). Energy-Efficient Schemes in Underwater Wireless Sensor Network: A Review. Computational Methods and Data Engineering, 495-510.
20
Walikar, G. A., & Biradar, R. C. (2017). A survey on hybrid routing mechanisms in mobile ad hoc networks. Journal of Network and Computer Applications, 77, 48-63.
21
Yadav, P., & Agrawal, R. (2018). A Multi-homing Based Framework Against Denial of Service Open Threat Signaling in Healthcare Environment. International Journal of Control and Automation, 11(11), 1-18.
22
ORIGINAL_ARTICLE
Q-Learning Enabled Green Communication in Internet of Things
Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.
https://jitm.ut.ac.ir/article_86486_aecb312b47cc34d72adb686ae1fd659f.pdf
2022-03-01
103
117
10.22059/jitm.2022.86486
Energy balancing
QRL
Link Quality
Learning rate
Internet of Things
Mukesh
Kumar
mukeshn7177@gmail.com
1
Ph.D. Scholar, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067,
AUTHOR
Sushil
Kumar
skdohare@mail.jnu.ac.in
2
Assistant Professor, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067.
LEAD_AUTHOR
Ankita
Jaiswal
ankita79_scs@jnu.ac.in
3
Ph.D. Scholar, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067.
AUTHOR
Pankaj Kumar
Kashyap
pankaj76_scs@jnu.ac.in
4
Ph.D., School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067.
AUTHOR
A. Jaiswal, S. Kumar, O. Kaiwartya, N. Kumar, H. Song and J. Lloret, (2020) Secrecy Rate Maximization in Virtual-MIMO Enabled SWIPT for 5G Centric IoT Applications," in IEEE Systems Journal, doi: 10.1109/JSYST.2020.3036417.
1
Ahmed AA and Mohammed Y, (2007) A survey on clustering algorithms for wireless sensor networks, Elsevier, ComputerCommunications, Vol. 30, pp. 2826-2841.
2
Akyildiz IF, Su W, Sankarasubramaniam Y and Cayirci E, (2002) Wireless sensor networks: a survey, Computer Networks, Vol.38, No. 4, pp. 393-422.
3
C. Guestrin, P. Bodik, R. Thibaux, M. Paskin, and S. Madden, (2004) Distributedregression: An efficient framework for modeling sensor network data inProc. 3rd Int. Symp. Inf. Process. Sensor Netw., pp. 1–10.
4
F. Bouabdallah, N. Bouabdallah, and R. Boutaba, (2008) Towards reliable and efficient reporting in wireless sensor networks, IEEE Trans. MobileComput., vol. 7, no. 8, pp. 978–994
5
Ishmanov F, Malik AS and Kim SW, (2011) Energy consumption balancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): A comprehensive overview, European Transactions on Telecommunications, Vol. 22, pp. 151-167.
6
J. Zhou, H. Jiang, J. Wu, L. Wu, C. Zhu, and W. Li, (2016)‘ SDN-based application framework for wireless sensor and actor networks,’’ IEEE Access,vol. 4, pp. 1583–1594.
7
Kashyap, P. K., Kumar, S., and Jaiswal, A. (2019) Deep Learning Based Offloading Scheme for IoT Networks Towards Green Computing. IEEE International Conference on Industrial Internet (ICII), pp. 22-27, Orlando, FL, USA, 2019.
8
Kashyap, P. K., Kumar, S., Dohare, U., Kumar, V., &Kharel, R. (2019) Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing. MDPI Electronics, 8(4), pp. 384-405.
9
Kashyap, P.K, Kumar, S. (2019) “Genetic-fuzzy based load balanced protocol for WSNs” International Journal of Electrical and Computer Engineering, Vol. 9, No.2, pp.1168-1183.
10
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. (2019) A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access vol. 7 pp-58221-58240.
11
N. Javaid, O. A. Karim, A. Sher, M. Imran, A. U. H. Yasar, and M. Guizani,(2003) Q-learning for energy balancing and avoiding the void hole routing protocol in underwater sensor networks,’’ in Proc. 14th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Jun. 2018, pp. 702–706
12
R. S. Sutton and A. G. Barto, (2018) Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press
13
T. Hu and Y. Fei, (2010) QELAR: A machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensornetworks,’’ IEEE Trans. Mobile Comput., vol. 9, no. 6, pp. 796–809.
14
W. Guo, C. Yan, and T. Lu,(2019) Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing,” Int. J. Distrib. Sensor Netw., vol. 15, no. 2.
15
Z. Jin, Y. Ma, Y. Su, S. Li, and X. Fu, (2017) A Q-learning-based delay-aware routing algorithm to extend the lifetime of underwater sensor networks,” Sensors, vol. 17, no. 7
16
ORIGINAL_ARTICLE
Moving Vehicles Detection and Tracking on Highways and Transportation System for Smart Cities
The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.
https://jitm.ut.ac.ir/article_86655_fe0fa12c09e35ffbae66c94d01d8e8f7.pdf
2022-03-01
118
131
10.22059/jitm.2022.86655
Background subtraction
Transportation Systems
Highways
Moving Vehicle De-tection
Post processing
Manoj
Kumar
manojattri003@gmail.com
1
Department of Computer Science & Engineering, Manav Rachna University, Faridabad.
LEAD_AUTHOR
Susmita
Ray
susmita@mru.edu.in
2
Professor, Department of Computer Science& Engineering, Manav Rachna University, Faridabad.
AUTHOR
Dileep Kumar
Yadav
dileep252000@gmail.com
3
Associate Professor, Department of Computer Science& Engineering, Galgotias University, Greater Noida.
AUTHOR
Aastho (2010). National Research Council (US). Transportation Research Board. Task Force on Development of the Highway Safety Manual, & Transportation Officials. Joint Task Force on the Highway Safety Manual. Highway safety manual (Vol. 1).
1
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.
2
Chen, L., Ye, F., Ruan, Y., Fan, H., & Chen, Q. (2018). An algorithm for highway vehicle detection based on convolutional neural network. Eurasip Journal on Image and Video Processing, 2018(1), 1-7.
3
Goyette, N., Jodoin, P. M., Porikli, F., Konrad, J., & Ishwar, P. (2012). Changedetection. net: A new change detection benchmark dataset. In 2012 IEEE computer society conference on computer vision and pattern recognition workshops (pp. 1-8). IEEE.
4
Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018). Sensor technologies for intelligent transportation systems. Sensors, 18(4), 1212.
5
Huang, T., Wang, S., & Sharma, A. (2020). Highway crash detection and risk estimation using deep learning. Accident Analysis & Prevention, 135, 105392.
6
Inkoom, S., Sobanjo, J., Barbu, A., & Niu, X. (2019). Prediction of the crack condition of highway pavements using machine learning models. Structure and Infrastructure Engineering, 15(7), 940-953.
7
Khan, T., Singh, K., Abdel-Basset, M., Long, H. V., Singh, S. P., & Manjul, M. (2019). A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks. IEEE Access, 7, 58221-58240.
8
Kukkala, V. K., Tunnell, J., Pasricha, S., & Bradley, T. (2018). Advanced driver-assistance systems: A path toward autonomous vehicles. IEEE Consumer Electronics Magazine, 7(5), 18-25.
9
Lanner (2019). Transportation: https://www.lanneramerica.com/blog/ examples-artificial-intelligence-applications-transportation/.
10
Ma, Y., Chowdhury, M., Sadek, A., & Jeihani, M. (2009). Real-time highway traffic condition assessment framework using vehicle–infrastructure integration (VII) with artificial intelligence (AI). IEEE Transactions on Intelligent Transportation Systems, 10(4), 615-627.
11
Machin, M., Sanguesa, J. A., Garrido, P., & Martinez, F. J. (2018). On the use of artificial intelligence techniques in intelligent transportation systems. In 2018 IEEE wireless communications and networking conference workshops (WCNCW) (pp. 332-337). IEEE.
12
Microsoft (2020). https://www.microsoft.com/en-us/download/details.aspx?id=54651.
13
Microsoft Asia News Center (2019). Artificial Intelligence and road safety: A new eye ont the highway. https://news.microsoft.com/apac/features/artificial-intelligence-and-road-safety-a-new-e ye-on-the-highway/
14
NCRB (2019). Accidental Deaths & Suicides in India 2019 (https://ncrb.gov.in/sites/default/files/Chapter-1A-Traffic-Accidents_2019.pdf)" pp. 117-128, 2019.
15
Özdağ, M. E., & Atasoy, N. A. (2019). Analysis of Highway Traffic Using Deep Learning Techniques. ISAS WINTER-2019, Samsun, Turkey, 4.
16
Pan, G., Fu, L., & Thakali, L. (2017). Development of a global road safety performance function using deep neural networks. International journal of transportation science and technology, 6(3), 159-173.
17
Peng, Y. T., Lu, Z., Cheng, F. C., Zheng, Y., & Huang, S. C. (2019). Image haze removal using airlight white correction, local light filter, and aerial perspective prior. IEEE Transactions on Circuits and Systems for Video Technology, 30(5), 1385-1395.
18
Phillips, D. J., Aragon, J. C., Roychowdhury, A., Madigan, R., Chintakindi, S., & Kochenderfer, M. J. (2019). Real-time prediction of automotive collision risk from monocular video. arXiv preprint arXiv:1902.01293.
19
Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17.
20
Saluja, N. (2019). Road Accidents Claimed Over 1.5 Lakh Lives in 2018, Over Speeding Major Killer. The Ministry of Road Transport, India.
21
Song, H., Liang, H., Li, H., Dai, Z., & Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. European Transport Research Review, 11(1), 1-16.
22
Wu, D., Wang, N., Wang, F., & Hong, S. (2017). Applying Machine Learning Algorithms to Highway Safety EEPDO. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1421-1426).
23
Xu, X., Yang, P., Xian, H., & Liu, Y. (2019). Robust moving objects detection in long-distance imaging through turbulent medium. Infrared Physics & Technology, 100, 87-98.
24
Yadav, D. K. (2019). Detection of Moving Human in Vision-Based Smart Surveillance under Cluttered Background: An Application of Internet of Things. In From Visual Surveillance to Internet of Things: Technology and Applications (pp. 161-174).
25
Yadav, D. K., & Singh, K. (2019). Adaptive background modelling technique for moving object detection in video under dynamic environment. International Journal of Spatio-Temporal Data Science, 1(1), 4-21..
26
Yazdi, M., & Bouwmans, T. (2018). New trends on moving object detection in video images captured by a moving camera: A survey. Computer Science Review, 28, 157-177.
27
Yuan, T., da Rocha Neto, W. B., Rothenberg, C., Obraczka, K., Barakat, C., & Turletti, T. (2019). Harnessing machine learning for next-generation intelligent transportation systems: a survey. Proceedings of the Computational Intelligence, Communication Systems and Networks (CICSyN).
28
Zeng, Q., Adu, J., Liu, J., Yang, J., Xu, Y., & Gong, M. (2020). Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT. Journal of Real-Time Image Processing, 17(5), 1103-1115.
29
ORIGINAL_ARTICLE
Internet of Things Care Device for Visually Impaired and Old People
Focusing on the problems faced by blind people, this paper has come up with the technology solution for the assistance of blind people. The solution is based on the intelligent data transmission to the earphone of a person based on task associated. The solution consists of a jacket to detect the obstacles along with a wearable box with task priority switchs. The system helps in detection of the obstacle and its height, one-touch cab booking and support of relatives, Ambulance services, Police services, etc. in the case of emergency. Either wired and wireless headphones or speakers can be interfaced with the device (box) to get audio notifications.The various tasks are triggered using multiple switches. The system will use a definitive SOC (System on Chip) platform recognized as Rasp-Pi-Pi along with ultrasonic sensor HC-SR04, Neo-6M GPS (Global Positioning System) module, and different switches. The system uses a 20,000 mAh lion battery for the power supply. The voice signals can be provided in more than fifty languages. A fall detection system is also discussed in this paper. This system will be beneficial not only for blind but also for care of old aged people
https://jitm.ut.ac.ir/article_86656_a50d0d782516cc9b6e19b5ed85037333.pdf
2022-03-01
132
146
10.22059/jitm.2022.86656
Blind Assistive Device
IOT
Rasp-Pi-Pi
Wearable Systems
Obstacle detection
Cab Booking
Emergency Contact
Fall Detection
A
Anmol
guptaanmol14@gmail.com
1
B.Tech., Department of Electronics and Communication Engineering, JSS Academy of Technical Education – C-20/1 Sec-62 Noida, Uttar Pradesh 201301, India.
AUTHOR
Gayatri
Sakya
gayatri.sakya@rediffmail.com
2
Assistant Professor, Department of Electronics and Communication Engineering, JSS Academy of Technical Education – C-20/1 Sec-62 Noida, Uttar Pradesh 201301, India.
AUTHOR
Suyash
Verma
vermasuyash2@gmail.com
3
B.Tech.,Department of Electronics and Communication Engineering, JSS Academy of Technical Education – C-20/1 Sec-62 Noida, Uttar Pradesh 201301, India.
AUTHOR
“HC-SR04 Ultrasonic Range Sensor on the Rasp-Pi Pi” (n.d.). [Online] Available:https://thepihut.com/blogs/Rasp-Pi-pi-tutorials/hc-sr04-ultrasonic-range-sensor-on-the-Rasp-Pi-pi
1
“RPi Low-level peripherals” (n.d.). [Online]. Available:https://elinux.org/RPi_Low-level_peripherals#cite_note-2
2
G. t. B. P. Accessible Pedestrian Signals. (n.d.). "Travel by Pedestrians Who Are Blind or Who Have Low Vision,"[Online]. Available:http://www.apsguide.org/chapter2.cfm.
3
A.G.Sareeka, K.Kirthika, M.R.Gowthame, V.Sucharitha (2018) “pseudoEye – Mobility Assistance for Blind Using Image Recognition” Proceedings of the Second International Conference on Inventive Systems and Control, ICISC
4
Ali Khan, Aftab Khan, Muhammad Waleed (2018) “Wearable Navigation Assistance System for theBlind and Visually Impaired”International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies
5
Ankit Agarwal, Deepak Kumar, Abhishek Bhardwaj (2015), “Ultrasonic Stick for Blind,” International Journal of Engineering and Computer Science ISSN:2319-7242 Volume 4, Page No. 11375-11378.
6
Assistive Technology Blog. (2014).VEST, “A VEST THAT HELPS DEAF PEOPLE HEAR,” [online].Available:http://assistivetechnologyblog.com/2014/10/vest-vest-thathelps-deaf-people-hear.html.
7
Carmen Willing. (n.d.)., “Visual Impairment”, Available online:http://www.teachingvisuallyimpaired.com/assistive-technology.html.
8
Deepthi Jain B, Shwetha M Thakur and K V Suresh. (2018). “Visual Assistance for Blind using ImageProcessing” (2018) International Conference on Communication and Signal Processing, India
9
Electronic Wings (n.d.). “GPS Receiver Module”[Online] Available:https://www.electronicwings.com/sensors-modules/gps-receiver-module
10
Electronic Wings (n.d.).“GPS Module Interfacing with Rasp-Pi Pi”[Online] Available:https://www.electronicwings.com/Rasp-Pi-pi/gps-module-interfacing-with-Rasp-Pi-pi
11
espeak.sourceforge.net. (n.d.). "eSpeak text to speech," [Online]. Available:http://espeak.sourceforge.net/.
12
G Prasanthi, P.Tejaswitha. (2015), “Sensor Assisted stick for the blind people” Available online:www.techscripts.org/jan_2015/jan201503.pdf.
13
G.Open Source. (n.d.). "Tesseract OCR," [Online]. Available:https://opensource.google.com/projects/tesseract.
14
John Dew. (2010). “Visual Impairment” Available online: https://en.wikipedia.org/Wiki/Visual impairment.Available:https://www.Rasp-Pipi.org/documentation/usage/python/
15
Khan, Tayyab, Karan Singh, Mohamed Abdel-Basset, Hoang Viet Long, Satya P. Singh, and Manisha Manjul. (2019) "A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks." IEEE Access 7: 58221-58240.
16
Media Centre (2014), "Visual impairment and blindness," who.int, [Online]. Available:http://www.who.int/mediacentre/factsheets/fs282/en/.
17
Mera Senthilingam(2011), “Sonar sticks use ultrasound to guide blind people”, Available online:http://edition.cnn.com/2014/06/20/tech/innovation/sonar-sticks-use-ultrasound-blind/.
18
Rasp-PipPi. (n.d.). https://www.Rasp-Pipi.org/documentation/hardware/Rasp-Pipi/gpio/README.md
19
Rasp-PipPi. (n.d.).“Raspbian”[Online] Available:https://www.Rasp-Pipi.org/documentation/raspbian/
20
Romteera Khlaikhayai, Chavana Pavaganun, et al., (2010). “An Intelligent, Walking Stick for Elderly and Blind Safety”, 2nd International Science, Social Science, Engineering and Energy Conference, Engineering Science and Management, AP.
21
Sparkfun. (n.d.). “Ultrasonic Ranging Module HC - SR04”[Online] Available:https://cdn.sparkfun.com/datasheets/Sensors/Proximity/HCSR04.pdf
22
Sunil Kumar KN, Vinayak S, Sathish R, Tarkeshwor Parsad Pandit (2019). “Braille Assistance System for Visually Impaired, Blind & Deaf-Mute people in Indoor & Outdoor Application” 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology, RTEICT.
23
M. magazine (n.d.). "RASP-PI PI 3 IS OUT NOW! SPECS, BENCHMARKS & MORE," [Online]. Available:https://www.Rasp-Pipi.org/magpi/Rasp-Pi-pi-3-specsbenchmarks/.
24
Vishal Kumar, Gayatri Sakya & Chandra Shankar. (2019) WSN and IoT based smart city model using the MQTT protocol, Journal of Discrete Mathematical Sciences and Cryptography, 22:8, 1423-1434, DOI: 10.1080/09720529.2019.1692449
25
Vishal T. Mishra (2015), “FOSS for Cause,” International Journal of Scientific and Research Publications, Volume 5, Issue 4, ISSN 2250-3153.
26
Wu Yi, Liu Pai, Wu Tong. (2013). “Smart Home System Based on ZigBee and ARM,” 11th IEEE International Conference On Electronic Measurement and Instrumentation, ICEMI.
27
ORIGINAL_ARTICLE
Multi Trust-based Secure Trust Model for WSNs
Trust “establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication” overhead.
https://jitm.ut.ac.ir/article_86657_2f91b6ad1e98600eae23ef561a3030cd.pdf
2022-03-01
147
158
10.22059/jitm.2022.86657
Trust management
Resource scheduling
Attacks
WSN
Tayyab
Khan
tayyabkhan.cse2012@gmail.com
1
School of Computer and Systems Sciences, JNU, New Delhi- 110067, India.
AUTHOR
Karan
Singh
karan@mail.jnu.ac.in
2
Ph.D., SCSS, Jawaharlal Nehru University, New Delhi, India.
AUTHOR
Sakshi
Gupta
guptasak1396@gmail.com
3
School of Computer and Systems Sciences, JNU, New Delhi- 110067, India.
AUTHOR
Manisha
Manjul
manishamanjul@gbpec.edu.in
4
Department of Computer Science and Engineering, G B PEC, New India.
AUTHOR
Boukerch, A., Xu, L., & El-Khatib, K. (2007). Trust-based security for wireless ad hoc and sensor networks. Computer Communications, 30(11-12), 2413-2427.
1
Basan, A., Basan, E., & Makarevich, O. (2016, October). Methodology of Countering Attacks for Wireless Sensor Networks Based on Trust. In 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (pp. 409-412). IEEE.
2
He, D., Chen, C., Chan, S., Bu, J., & Vasilakos, A. V. (2012). ReTrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE transactions on information technology in biomedicine, 16(4), 623-632.
3
Crosby, G. V., Pissinou, N., & Gadze, J. (2006, April). A framework for trust-based cluster head election in wireless sensor networks. In Second IEEE Workshop on Dependability and Security in Sensor Networks and Systems (pp. 10-pp). IEEE.
4
Ganeriwal, S., Balzano, L. K., & Srivastava, M. B. (2008). Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(3), 1-37.
5
Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2014). An efficient distributed trust model for wireless sensor networks. IEEE transactions on parallel and distributed systems, 26(5), 1228-1237.
6
Ishmanov, F., Malik, A. S., Kim, S. W., & Begalov, B. (2015). Trust management system in wireless sensor networks: design considerations and research challenges. Transactions on Emerging Telecommunications Technologies, 26(2), 107-130.
7
Jadidoleslamy, H., Aref, M. R., & Bahramgiri, H. (2016). A fuzzy fully distributed trust management system in wireless sensor networks. AEU-International Journal of Electronics and Communications, 70(1), 40-49.
8
Tan, S., Li, X., & Dong, Q. (2015). A trust management system for securing data plane of ad-hoc networks. IEEE Transactions on Vehicular Technology, 65(9), 7579-7592.
9
Zhou, Y., Huang, T., & Wang, W. (2009, September). A trust establishment scheme for cluster-based sensor networks. In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (pp. 1-4). IEEE.
10
Feng, R., Xu, X., Zhou, X., & Wan, J. (2011). A trust evaluation algorithm for wireless sensor networks based on node behaviors and ds evidence theory. Sensors, 11(2), 1345-1360.
11
Liu, Z., Zhang, Z., Liu, S., Ke, Y., & Chen, J. (2011, September). A trust model based on Bayes theorem in WSNs. In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (pp. 1-4). IEEE.
12
Shaikh, R. A., Jameel, H., d'Auriol, B. J., Lee, H., Lee, S., & Song, Y. J. (2008). Group-based trust management scheme for clustered wireless sensor networks. IEEE transactions on parallel and distributed systems, 20(11), 1698-1712.
13
Bao, F., Chen, R., Chang, M., & Cho, J. H. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE transactions on network and service management, 9(2), 169-183.
14
Li, X., Zhou, F., & Du, J. (2013). LDTS: A lightweight and dependable trust system for clustered wireless sensor networks. IEEE transactions on information forensics and security, 8(6), 924-935.
15
Zhang, B., Huang, Z., & Xiang, Y. (2014). A novel multiple-level trust management framework for wireless sensor networks. Computer Networks, 72, 45-61.
16
Ishmanov, F., Kim, S. W., & Nam, S. Y. (2015). A robust trust establishment scheme for wireless sensor networks. Sensors, 15(3), 7040-7061.
17
Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2014). An efficient distributed trust model for wireless sensor networks. IEEE transactions on parallel and distributed systems, 26(5), 1228-1237.
18
Won, J., & Bertino, E. (2015, November). Distance-based trustworthiness assessment for sensors in wireless sensor networks. In International conference on network and system security (pp. 18-31). Springer, Cham.
19
Talbi, S., Koudil, M., Bouabdallah, A., & Benatchba, K. (2017). Adaptive and dual data-communication trust scheme for clustered wireless sensor networks. Telecommunication Systems, 65(4), 605-619.
20
Dogan, G., & Avincan, K. (2017). MultiProTru: A kalman filtering based trust architecture for two-hop wireless sensor networks. Peer-to-Peer Networking and Applications, 10(1), 278-291.
21
Singh, M., Sardar, A. R., Majumder, K., & Sarkar, S. K. (2017). A lightweight trust mechanism and overhead analysis for clustered WSN. IETE Journal of research, 63(3), 297-308.
22
Karthik, N., & Ananthanarayana, V. S. (2017). A hybrid trust management scheme for wireless sensor networks. Wireless Personal Communications, 97(4), 5137-5170.
23
Khan, T., Singh, K., Abdel-Basset, M., Long, H. V., Singh, S. P., & Manjul, M. (2019). A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks. Ieee Access, 7, 58221-58240.
24
Dai, L., Chang, Y., & Shen, Z. (2011). An optimal task scheduling algorithm in wireless sensor networks. International Journal of Computers Communications & Control, 6(1), 101-112.
25
Yao, Z., Kim, D., & Doh, Y. (2006, October). PLUS: Parameterized and localized trust management scheme for sensor networks security. In 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems (pp. 437-446). IEEE.
26
Zhang, J., Shankaran, R., Mehmet, A. O., Varadharajan, V., & Sattar, A. (2010, October). A trust management architecture for hierarchical wireless sensor networks. In IEEE Local Computer Network Conference (pp. 264-267). IEEE.
27
Nasser, N., Karim, L., & Taleb, T. (2013). Dynamic multilevel priority packet scheduling scheme for wireless sensor network. IEEE transactions on wireless communications, 12(4), 1448-1459.
28
ORIGINAL_ARTICLE
Detection of Wormhole Attack in Vehicular Ad-hoc Network over Real Map using Machine Learning Approach with Preventive Scheme
VANET (Vehicular Ad-hoc Network) is a developing technology, which is a combination of cellular technology, ad-hoc network & wireless LAN to improve the safety of vehicle as well as driver. VANET communication can be of two types, first one is broadcast and second one is unicast. Either communication may be broadcast or unicast both are sensitive to different types ofassaults, for example message forgery, (DOS) denial of service, Sybil assault, Greyhole, Blackhole & Wormhole assault. In this paper machine learning method is used to detect the wormhole assault in VANET’s multi-hop communication. We have created a scenario of VANET by using AODV routing protocol on NS-3.24.1 simulator, which utilizes the overall mobility traces generated by the simulator SUMO-0.32.0 to model the wormhole assault. The simulation is performed by using NS-3.24.1 simulator, and the statistics created by flow monitor are collected. The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. The novelty of this research work is that with the help of proposed detection & prevention technique, vehicular ad-hoc network can be made free from wormhole assault by using ML approach. The performance of proposed machine learning models is compared with existing work. In this way it is clear that our proposed approach by using ML is powerful tool by which the wormhole assaults can be detected in VANETs. A scheme based on packet lease and cryptographic techniques is used to prevent the wormhole attack in VANET
https://jitm.ut.ac.ir/article_86658_9c2005e995c9f21b270aa370b502a44d.pdf
2022-03-01
159
179
10.22059/jitm.2022.86658
VANET
AODV
Broadcast
Unicast
k-NN
Random forest
SUMO-0.32.0
NS-3.24.1
Packet leash
Cryptography
Shahjahan
Ali
shahjahansrms@gmail.com
1
Assistant Professor, Department of Computer Science & Engineering at SRMSCET, Bareilly (UP) India, Affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India.
LEAD_AUTHOR
Parma
Nand
parmaastya@gmail.com
2
Professor and Dean Academic Affairs, Sharda University Greater Noida (U.P.) India, Pin Code:201306.
AUTHOR
Shailesh
Tiwari
shail.tiwari@yahoo.com
3
Professor and Director, KEC, Ghaziabad (U.P.) India, Affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India.
AUTHOR
Albouq, S.S., & Fredericks, E.M. (2017). Detection and avoidance of wormhole attacks in connected vehicles. Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. https://doi.org/10.1145/3132340.3132346
1
Ali Alheeti, K.M., Gruebler, A., & McDonald-Maier, K. (2016). Intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks. Computers, 5(3), 16. https://doi.org/10.3390/computers5030016
2
Ali, S., Nand, P. & Tiwari, S. (2017). Secure message broadcasting in VANET over wormhole attack by using cryptographic technique. 2017 International Conference on Computing, Communication and Automation (ICCCA), 520-523. doi: 10.1109/CCAA.2017.8229856.
3
Ali, S., Nand, P., & Tiwari, S. (2020). Impact of wormhole attack on AODV routing protocol in vehicular ad-hoc network over real map with detection and prevention approach. Int. J. Vehicle Information and Communication Systems, 5(3), 354–373. doi: 10.1504/IJVICS.2020.110997
4
Altman, N.S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. Journal of the American Statistical Association published by Taylor & Francis Ltd., 46(3), 175–185. https://doi.org/10.2307/2685209
5
Argyroudis, P.G., & O’mahony, D. (2005). Secure routing for mobile ad hoc networks. IEEE Commun. Surv.Tutor, 7(3), 1-21.
6
Bakhouya, M., Gaber, J., & Lorenz, P. (2011). An adaptive approach for information dissemination in vehicular ad hoc networks. Journal of Network and Computer Applications, 34(6), 1971–1978. https://doi.org/10.1016/j.jnca.2011.06.010
7
Bellare, M., Canetti, R., & Krawczyk, H. (1996). Keying hash functions for message authentication. In Advances in Cryptology – CRYPTO ’96 edited by Neal Koblitz, volume 1109 of Lecture Notes in Computer Science, 1–15. Springer-Verlag, Berlin Germany, 1996. https://doi.org/10.1007/3-540-68697-5_1
8
Canetti, R., Garay, J., Itkis, G., Micciancio, D., Naor, M., & Pinkas, B.(1999). Multicast security: a taxonomy and some efficient constructions. In Proceedings of the Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies, 2, 708-716 doi: 10.1109/INFCOM.1999.751457.
9
Chahal, M., Harit, S., Mishra, K., Sangaiah, A., & Zheng, Z. (2017). A Survey on software-defined networking in vehicular ad hoc networks: Challenges, applications and use cases. International Journal of Sustainable Cities and Society, 35, 830–840. https://doi.org/10.1016/j.scs.2017.07.007
10
Chung, T., & Roedig, U. (2007) ‘Poster: efficient key establishment for wireless sensor networks using elliptic curve Diffie-Hellman’, September 2008 IEEE 5th International Conference on Mobile Adhoc and Sensor Systems, MASS 2008, 29 September - 2 October 2008, Atlanta, Georgia, USA, DOI: 10.1109/MAHSS.2008.4660127.
11
Grover, J., Prajapati, N.K., Laxmi, V., & Gaur, M.S. (2011). Machine learning approach for multiple misbehavior detection in VANET. International Conference on Advances in Computing and Communications, 644–653. https://doi.org/10.1007/978-3-642-22720-2_68
12
Hu, Y.C., Perrig, A., & Johnson, D.B. (2003). Packet leashes: A defense against wormhole attacks in wireless networks. IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), 3, 1976–1986. doi: 10.1109/INFCOM.2003.1209219
13
IEEE Standard for Information technology-Local and metropolitan area networks- Specific requirements-- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments. In IEEE Std 802.11p-2010 (Amendment to IEEE Std 802.11-2007 as amended by IEEE Std 802.11k-2008, IEEE Std 802.11r-2008, IEEE Std 802.11y-2008, IEEE Std 802.11n-2009, and IEEE Std 802.11w-2009) , 1-51, 15 July 2010. doi: 10.1109/IEEESTD.2010.5514475.
14
Kang, M.J., & Kang, J.W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLOS ONE 11(6), e0155781(2016). https://doi.org/10.1371/journal.pone.0155781
15
Khalil, I., Bagchi, S., Shroff, N.B. (2007). LiteWorp: Detection and isolation of the worm-hole attack in static multi hop wireless networks. Comput.Netw.,51(13), 3750–3772. https://doi.org/10.1016/j.comnet.2007.04.001
16
Khan, T., & Singh, K. (2021). TASRP: a trust aware secure routing protocol for wireless sensor networks. International Journal of Innovative Computing and Applications, 12(2), 108-122. doi:10.1504/IJICA.2021.113750
17
Khan, T., Singh, K., Le, H. S., Mohamed Abdel‑Basset, Hoang, V. L., Singh, S. P., & Manjul, M. (2019). A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks. IEEE Access 7 (2019):58221-58240. doi: 10.1109/ACCESS.2019.2914769.
18
Kumar, S., & Singh, R.K. (2016). Secure authentication approach using Diffie-Hellman key exchange algorithm for WSN. Int. J. Communication Networks and Distributed Systems, 17(2), 189–201. https://doi.org/10.1504/IJCNDS.2016.079102
19
Loukas, G., Vuong, T., Heartfield, R., Sakellari, G., Yoon, Y., & Gan, D.(2018). Cloud- based cyber-physical intrusion detection for vehicles using Deep Learning. In IEEE Access, vol. 6, 3491-3508. doi: 10.1109/ACCESS.2017.2782159.
20
Perkins, C.E., & Royer, E.M. (1999). Ad-Hoc on-Demand Distance Vector Routing. Proc. of IEEE Workshop Mobile Computing Systems and Applications, 90-100. doi: 10.1109/MCSA.1999.749281
21
Provost, F., Hibert, C., Malet, J.-P., Stumpf, A., & Doubre, C. (2016). Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm. EGU General Assembly 2016, held 17-22 April, 2016 in Vienna Austria, id. EPSC2016-15705.
22
R.Henderson, T., Lacage, M., Riley, G.F., Dowell, C., & Kopena, J. (2008). Network simu-lations with the ns-3simulator. SIGCOMMDemonstr.14(14),527, ACM 978-1-60558-175-0/08/08.
23
Rawat, G., & Singh, K. (2020). Joint beacon frequency and beacon transmission power adaptation for internet of vehicles. Transactions on Emerging Telecommunications Technologies, e4124, https://doi.org/10.1002/ett.4124
24
Safi, S., Movaghar, A., & Mohammadizadeh, M. (2009). A Novel approach for avoiding Wormhole Attacks in VANET. Second International Workshop on Computer Science and Engineering, 160- 165. doi: 10.1109/WCSE.2009.787
25
Sanzgiri, K., Dahill, B., Levine, B.N., Shields, C., & Belding-Royer, E.M. (2002). A secure routing protocol for ad hoc networks. 10th IEEE International Conference on Network Protocols, 2002. Proceedings, 78-87. doi: 10.1109/ICNP.2002.1181388
26
Singh, P.K., Dash, M.K., Mittal, P., Nandi, S.K., & Nandi, S.(2018). Misbehavior detection in C-ITS using deep learning approach. In: 18th International Conference on Intelligent Systems Design and Applications (ISDA),Springer(2018), 641-652. doi:10.1007/978-3-030-16657-1_60
27
Singh, P.K., Gupta, R.R., Nandi, S.K., & Nandi, S. (2019). Machine learning based approach to detect wormhole attack in VANETs. Web, Artificial Intelligence and Network Applications, Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019). https://doi.org/10.1007/978-3-030-15035-8_63
28
Singh, P.K., Sharma, S., Nandi, S.K., & Nandi, S. (2018). Multipath TCP for V2I commu-nication in SDN controlled small cell deployment of smart city. Veh. Commun. (2018), https://doi.org/10.1016/j.vehcom.2018.11.002.
29
Stalings, W. (2005). Cryptography and Network Security, Principles and Practices. Prentice Hall 2005, 268-270 & 296-297.
30
Taylor, A., Leblanc, S., & Japkowicz, N. (2016). Anomaly detection in automobile control network data with long short-term memory networks. 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 130-139. doi: 10.1109/DSAA.2016.20
31
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32
ORIGINAL_ARTICLE
Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm
Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving objects. Its shows less absolute error and less object tracing error while evaluating the proposed work.
https://jitm.ut.ac.ir/article_86665_90f87be9bf0cccd684906295e0b7452e.pdf
2022-03-01
180
199
10.22059/jitm.2022.86665
Background subtraction
Merge Sort Algorithm
Binary Search Algorithm
Extended Kalman filter
Object Detection
Object Prediction and Correction
Sandeep
Kumar
er.sandeepsahratia@gmail.com
1
Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India.
AUTHOR
A
Shailu
shailureddy.posham@gmail.com
2
M.Tech. Scholar, Dept of ECE, Sreyas Institute of Engineering and Technology, Hyderabad, In-dia.
AUTHOR
Arpit
Jain
dr.jainarpit@gmail.com
3
Associate Professor, Faculty of Engineering & Computing Sciences, Teerthanker Mahaveer Uni-versity, Moradabad, U.P, India.
AUTHOR
Nageswara Rao
Moparthi
mnrphd@gmail.com
4
Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India
AUTHOR
Alex, D. S., Wahi, A. (2014). Bsfd: Background Subtraction Frame Difference Algorithm for Moving Object Detection and Extraction. Journal of Theoretical and Applied Information Technology, 60(3).
1
Alt, N., Hinterstoisser, S., & Navab, N. (2010, June). Rapid Selection of Reliable Templates for Visual Tracking. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1355-1362). IEEE.
2
Cheng, J., & Kang, S. (2017). Robust Visual Tracking with Improved Subspace Representation Model. Telkomnika, 15(1), 328.
3
Chien, S.-Y., Chan, W.-K., Tseng, Y.-H., & Chen, H.-Y. (2013). Video Object Segmentation and Tracking Framework with Improved Threshold Decision and Diffusion Distance. IEEE Transactions on Circuits and Systems for Video Technology, 23(6).
4
Ghai, D., Gianey, H.K., Jain, A. and Uppal, R.S., (2020). Quantum and dual-tree complex wavelet transform-based image watermarking. International Journal of Modern Physics B, 34(04).
5
Granstrom, K., Banum, M., & Reuter, S. (2016). Extended Object Tracking: Introduction, Overview, and Applications. ArXiv e-prints.
6
Gutchess, D., Trajkovics, M., Cohen-Solal, E., Lyons, D., & Jain, A. K. (2001, July). A background model initialization algorithm for video surveillance. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 (pp. 733-740). IEEE.
7
Hong-de, D., Shao-wu, D., Yuan-cai, C., & Guang-bin, W. (2012). Performance comparison of EKF/UKF/CKF for the tracking of ballistic target. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10(7), (pp. 1692-1699).
8
Hosaka, T., Kobayashi, T., & Otsu, N. (2011). Object Detection Using Background Subtraction and Foreground Motion Estimation. IPSJ Transactions on Computer Vision and Applications, 3(9), (pp. 20-25).
9
Hung, M. H., Pan, J. S., & Hsieh, C. H. (2014). A fast algorithm of temporal median filter for background subtraction. J. Inf. Hiding Multim. Signal Process., 5(1), (pp. 33-40).
10
Jain, A. and Kumar, A., 2021. Desmogging of still smoggy images using a novel channel prior. Journal of Ambient Intelligence and Humanized Computing, 12(1), (pp.1161-1177).
11
Jain, A., Gahlot, A.K., Dwivedi, R., Kumar, A. and Sharma, S.K., 2018. Fat Tree NoC Design and Synthesis. In Intelligent Communication, Control and Devices (pp. 1749-1756).
12
Jain, A., Kumar, A. and Sharma, S., (2015). Comparative Design and Analysis of Mesh, Torus and Ring NoC. Procedia Computer Science, (pp.330-337).
13
Kartika, I., Mohamed, S. S. (2011). Frame differencing with post-processing techniques for moving object detection in an outdoor environment. IEEE 7th International Colloquium on Signal Processing and Its Applications, (pp. 172-176).
14
Kim, ZuWhan (2008). "Real time object tracking based on dynamic feature grouping with background subtraction." In IEEE Conference on Computer Vision and Pattern Recognition, (pp. 1-8). IEEE.
15
Kumar, S., Jain, A., Kumar Agarwal, A., Rani, S., & Ghimire, A. (2021). Object-Based Image Retrieval Using the U-Net-Based Neural Network. Computational Intelligence and Neuroscience, 2021.
16
Kumar, S., Raja, R., & Gandham, A. (2020). Tracking an Object Using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter. In Applications of Machine Learning (pp. 47-65). Springer, Singapore.
17
Kumar, S., Singh, S., & Kumar, J. (2018). Automatic live facial expression detection using genetic algorithm with haar wavelet features and SVM. Wireless Personal Communications, 103(3), (pp. 2435-2453).
18
Kumar, S., Singh, S., & Kumar, J. (2018). Live detection of face using machine learning with multi-feature method. Wireless Personal Communications, 103(3).
19
Kumar, S., Singh, S., & Kumar, J. (2019). Multiple face detection using hybrid features with SVM classifier. In Data and Communication Networks (pp. 253-265). Springer, Singapore.
20
Kumar, S., Singh, S., & Kumar, J. (2019, January). Gender classification using machine learning with multi-feature method. In 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0648-0653). IEEE.
21
Kumar, S., Singh, S., & Kumar, J. (2021). Face spoofing detection using improved SegNet architecture with a blur estimation technique. International Journal of Biometrics, 13(2).
22
Kumar, S., Swetha, S., Kiran, V. T., & Johri, P. (2018, September). IoT based smart home surveillance and automation. In 2018 International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 786-790). IEEE.
23
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013). A survey of appearance models in visual object tracking. ACM transactions on Intelligent Systems and Technology (TIST), 4(4).
24
Michalis, Z., Pascal, F., & Ben, S. H. (2012). Real-Time Multi-Object Tracking using Multiple Cameras, Semester Project, School of Computer, and Communication Sciences.
25
Mohamed, S. S., Tahir, N. M., & Adnan, R. (2010, May). Background modelling and background subtraction performance for object detection. In 6th International Colloquium on Signal Processing & its Applications (pp. 1-6). IEEE.
26
Murshed, M., Ramirez, A., & Chae, O. (2010, September). Statistical background modeling: an edge segment based moving object detection approach. In 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 300-306). IEEE.
27
Nenavath, H., & Jatoth, R. K. (2018). A new method for ball tracking based on α-β, Linear Kalman and extended Kalman filters via bubble sort algorithm. Indones J Electr Eng Comput Sci, 10(3).
28
Patel, H. A., & Thakore, D. G. (2013). Moving object tracking using kalman filter. International Journal of Computer Science and Mobile Computing, 2(4), 326-332.
29
Raja, R., Kumar, S., & Mahmood, M. R. (2020). Color object detection based image retrieval using ROI segmentation with multi-feature method. Wireless Personal Communications, 112(1).
30
Santiago, C. B., Sousa, A., Estriga, M. L., Reis, L. P., & Lames, M. (2010, June). Survey on team tracking techniques applied to sports. In International Conference on Autonomous and Intelligent Systems, AIS 2010 (pp. 1-6). IEEE.
31
Shaikh, S. H., Saeed, K., & Chaki, N. (2014). Moving object detection using background subtraction. In Moving object detection using background subtraction (pp. 15-23). Springer, Cham.
32
Sharma, S. K., Jain, A., Gupta, K., Prasad, D., & Singh, V. (2019). An internal schematic view and simulation of major diagonal mesh network-on-chip. Journal of Computational and Theoretical Nanoscience, 16(10), 4412-4417.
33
Singla, N. (2014). Motion detection based on frame difference method. International Journal of Information & Computation Technology, 4(15), 1559-1565.
34
Soumya and S.Kumar (2018). Health Care Monitoring Based on Internet of Things. the (Springer) International Conference on Artificial Intelligence & Cognitive Computing (AICC).
35
Srikrishnaswetha, K., Kumar, S., & Rashid Mahmood, M. (2019). A Study on Smart Electronics Voting Machine using Face Recognition and Aadhar Verification with IOT. In Innovations in Electronics and Communication Engineering (pp. 87-95). Springer, Singapore.
36
Tang, Z., & Miao, Z. (2007, October). Fast background subtraction and shadow elimination using improved gaussian mixture model. In IEEE International Workshop on Haptic, Audio and Visual Environments and Games (pp. 38-41). IEEE.
37
Weng, S. K., Kuo, C. M., & Tu, S. K. (2006). Video object tracking using adaptive Kalman filter. Journal of Visual Communication and Image Representation, 17(6), 1190-1208.
38
Yang, S., & Baum, M. (2017, March). Extended Kalman filter for extended object tracking. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4386-4390). IEEE.
39
Zhan, C., Duan, X., Xu, S., Song, Z., & Luo, M. (2007, August). An improved moving object detection algorithm based on frame difference and edge detection. In Fourth international conference on image and graphics (ICIG 2007) (pp. 519-523). IEEE.
40
Zivkovic, Z. (2004, August). Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. (pp. 28-31). IEEE.
41
ORIGINAL_ARTICLE
Technical Assessment of Various Image Enhancement Techniques using Finger Vein for personal Authentication
The most important aspects of image processing are image enhancement. The visual form of an image can be enhanced by using image enhancement techniques for better human interpretation. In this research paper we discuss an outline and analysis of commonly used image enhancement techniques using finger vein image or personal authentication. Also, experiments are carried out to compare performance of various types of filters for removal of noise from the noisy images through evaluation performance parameter such as mean square error (MSE), peak signal to noise ratio (PSNR) values and structural similarity (SSIM). It was found that application of max filter technique ensures an improved quality of the finger vein image. The mean filter is most advanced in de-noising the images. Mean filter is most efficient in eliminating the salt and pepper noise. From the experiments performed on finger vein image using SDUMLA-HMT database, it is proven that Weiner filters are outstanding for elimination of Gaussian Speckle and Poisson noises and thus, Weiner filter is found to be most appropriate and well-suited for eliminating nearly all types of noise.
https://jitm.ut.ac.ir/article_86666_8142d32dfc5521d2349a4030c14d20b1.pdf
2022-03-01
200
224
10.22059/jitm.2022.86666
Finger Vein
Filters
Histogram Equalization
Image Enhancement
MSE
Noise
PSNR
SSIM
Sapna
Sharma
sapnaloksharma@gmail.com
1
Research Scholar, School of Engineering, Computer Science and Engineering, GD Goenka University, Gurgaon, India
AUTHOR
Shilpy
Agrawal
shilpy.agrawal@gdgoenka.ac.in
2
Assistant Professor, School of Engineering, Computer Science and Engineering, GD Goenka University, Gurgaon, India
AUTHOR
Manisha
Munjal
bhavikamanisha@gmail.com
3
Assistant Professor, School of Engineering, Computer Science and Engineering, G B Pant New Delhi, India.
AUTHOR
Abdalla, M. Dr. Zhijun P. and Faustini L. 2015. Image Noise Reduction and Filtering Techniques. International Journal of Science and Research (IJSR), 6(3), 2033-2038.
1
Agaian, S. S., Silver, B., & Panetta, K. A. (2007). Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE transactions on image processing, 16(3), 741-758.
2
Bansal, A., Bajpai, R., & Saini, J. P. (2007, March). Simulation of image enhancement techniques using Matlab. In First Asia International Conference on Modelling & Simulation (AMS'07) (pp. 296-301). IEEE.
3
Boyat, A. K., & Joshi, B. K. (2015). A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489.
4
Chen, S. D., & Ramli, A. R. (2004). Preserving brightness in histogram equalization-based contrast enhancement techniques. Digital Signal Processing, 14(5), 413-428.
5
Duan, F., & Zhang, Y. J. (2010). A highly effective impulse noise detection algorithm for switching median filters. IEEE Signal Processing Letters, 17(7), 647-650.
6
Figueiredo, M. A., & Bioucas-Dias, J. M. (2010). Restoration of Poissonian images using alternating direction optimization. IEEE transactions on Image Processing, 19(12), 3133-3145.
7
Ghimire, D., & Lee, J. (2011). Nonlinear transfer function-based local approach for color image enhancement. IEEE Transactions on Consumer Electronics, 57(2), 858-865.
8
Ghosh, D., & Dey, K. N(2014). A Comparative Study of Contrast Enhancement using Image Fusion.
9
Gonzalez, R. C. (2009). Digital image processing. Pearson education India.
10
Hoshyar, A. N., Al-Jumaily, A., & Hoshyar, A. N. (2014). Comparing the performance of various filters on skin cancer images. Procedia Computer Science, 42, 32-37.
11
Janaki, K., & Madheswaran, M. (2016). Performance Analysis of Different Filters with Various Noises In Pre-processing Of Images. International Journal of Advanced Networking & Applications (IJANA), 372-376.
12
Janani, P., Premaladha, J., & Ravichandran, K. S. (2015). Image enhancement techniques: A study. Indian Journal of Science and Technology, 8(22), 1-12.
13
Jung, S. W., Jeong, J. Y., & Ko, S. J. (2011). Sharpness enhancement of stereo images using binocular just-noticeable difference. IEEE Transactions on Image Processing, 21(3), 1191-1199.
14
Kang, W., Liu, H., Luo, W., & Deng, F. (2019). Study of a full-view 3D finger vein verification technique. IEEE Transactions on Information Forensics and Security, 15, 1175-1189.
15
Kaur, H., & Kaur, L. (2014). Performance comparison of different feature detection methods with Gabor filter. Int J Sci Res, 3, 1879-1886.
16
Khidse, S., & Nagori, M. (2013). A comparative study of image enhancement techniques. Int. J. Comput. Appl, 81(15), 28-32.
17
Levin, A., & Nadler, B. (2011, June). Natural image denoising: Optimality and inherent bounds. In CVPR 2011 (pp. 2833-2840). IEEE.
18
Lu, Y., Wu, S., Fang, Z., Xiong, N., Yoon, S., & Park, D. S. (2017). Exploring finger vein based personal authentication for secure IoT. Future Generation Computer Systems, 77, 149-160.
19
Maini, R., & Aggarwal, H. (2010). A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053.
20
Panthi, R., Gawande, S., Shivhare, A., Scholar, M. T., Engineering, C., & Bhopal, B. (2016). A new Image Enhancement method and Its Simulation. Inertnational Research Journal of Engineering and Technology (IJIRET)eISSN: 2395-0056 volume :03 issue :04 Apr-2016 pp 719-723.
21
Park, Y. H., & Park, K. R. (2012). Image quality enhancement using the direction and thickness of vein lines for finger-vein recognition. International Journal of Advanced Robotic Systems, 9(4), 154.
22
Patel, O., Maravi, Y. P., & Sharma, S. (2013). A comparative study of histogram equalization-based image enhancement techniques for brightness preservation and contrast enhancement. arXiv preprint arXiv:1311.4033.
23
Pathak, S. S., Dahiwale, P., & Padole, G. (2015, March). A combined effect of local and global method for contrast image enhancement. In 2015 IEEE International Conference on Engineering and Technology (ICETECH) (pp. 1-5). IEEE.
24
Putra, R. D., Purboyo, T. W., & Prasasti, A. L. (2017). A Review of Image Enhancement Methods. International Journal of Applied Engineering Research, 12(23), 13596-13603.
25
Rahmi-Fajrin, H., Puspita, S., Riyadi, S., & Sofiani, E. (2018). Dental radiography image enhancement for treatment evaluation through digital image processing. Journal of clinical and experimental dentistry, 10(7), e629.
26
Rani, N. (2017). Image Processing Techniques: A Review. Journal on Today's Ideas-Tomorrow's Technologies, 5(1), 40-49.
27
Rashid, M. M. (2020). Multimedia Image Processing Lab Experiment/Simulation. American International Journal of Sciences and Engineering Research, 3(1), 1-13.
28
Rivera, A. R., Ryu, B., & Chae, O. (2012). Content-aware dark image enhancement through channel division. IEEE transactions on image processing, 21(9), 3967-3980.
29
Schulte, S., De Witte, V., & Kerre, E. E. (2007). A fuzzy noise reduction method for color images. IEEE Transactions on image Processing, 16(5), 1425-1436.
30
Shaheed, K., Liu, H., Yang, G., Qureshi, I., Gou, J., & Yin, Y. (2018). A systematic review of finger vein recognition techniques. Information, 9(9), 213.
31
Shukla, K. N., Potnis, A., & Dwivedy, P. (2017). A review on image enhancement techniques. Int. J. Eng. Appl. Comput. Sci, 2(07), 232-235.
32
Singh, G., & Mittal, A. (2014). Various image enhancement techniques-a critical review. International Journal of Innovation and Scientific Research, 10(2), 267-274.
33
Singh, G., & Mittal, A. (2014). Various image enhancement techniques-a critical review. International Journal of Innovation and Scientific Research, 10(2), 267-274.
34
Singh, I., & Neeru, N. (2014). Performance comparison of various image denoising filters under spatial domain. International Journal of Computer Applications, 96(19), 21-30.
35
Wittman, Todd. "An Introduction to Mathematical Image Processing IAS, Park City Mathematics Institute, Utah Undergraduate Summer School 2010."
36
Yasmin, M., Mohsin, S., Sharif, M., Raza, M., & Masood, S. (2012). Brain image analysis: a survey. World Applied Sciences Journal, 19(10), 1484-1494.
37
Zhang, H., Zhao, Q., Li, L., Li, Y. C., & You, Y. H. (2011, October). Muti-scale image enhancement based on properties of human visual system. In 2011 4th International Congress on Image and Signal Processing (Vol. 2, pp. 704-708). IEEE.
38
Zhang, W. (2020). imageProcAnal: A novel Matlab software package for image processing and analysis. Network, 5(1-2), 1-32.
39
ORIGINAL_ARTICLE
Enhanced Lightweight and Secure Session Key Establishment Protocol for Smart Hospital Inhabitants
In the era of internet technologies, to provide wireless communication and transfer the information in seconds from one place to another has arrived because of the need to consume information technologies. All users desire to quickly access the smart world’s life and interact with the entire world socially. This paper proposed an environment for the safe and secure smart patient’s room connected to the WSN, BAN, and RFID. All the data will be transferred to the session key, secure and contains the patient’s information. The network connected through WSN and data will be sent through the session key to make an smart hospital’s patient cabin. The small token is there that will be transferred throughout the network to get authenticated by each network. This proposed scheme is secure enough to overcome the drawbacks of the other protocol in such a way as to make the protocol more secure from the entire adversary’s attack may occur.
https://jitm.ut.ac.ir/article_86733_621f9c41f1f45f8039c383fd147c9c35.pdf
2022-03-01
225
234
10.22059/jitm.2022.86733
Session Key
Cryptanalysis
Smart Hospital Environment
WSN (Wireless Sensor Network)
BAN (Body Area Network)
Anshita
Dhoot
anshita.dhoot@phystech.edu
1
Department of Radio Engineering & Computer Technology, Moscow Institute of Physics & Technology, Moscow, Russia – 9 Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russian Federation.
LEAD_AUTHOR
A. N.
Nazarov
a.nazarov05@bk.ru
2
Department of Radio Engineering & Computer Technology, Moscow Institute of Physics & Technology, Moscow, Russia – 9 Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russian Federation
AUTHOR
Tayyab
Khan
tayyabkhan.cse2012@gmail.com
3
School of Computer and Systems Sciences, JNU, New Delhi- 110067, India.
AUTHOR
Seshadri, A., Luk, M., & Perrig, A. (2011). SAKE: Software attestation for key establishment in sensor networks. Ad Hoc Networks, 9(6), 1059-1067.
1
Aghili, S. F., Mala, H., Shojafar, M., & Peris-Lopez, P. (2019). LACO: Lightweight three-factor authentication, access control and ownership transfer scheme for e-health systems in IoT. future generation computer systems, 96, 410-424.
2
Al Ameen, M., Liu, J., & Kwak, K. (2012). Security and privacy issues in wireless sensor networks for healthcare applications. Journal of medical systems, 36(1), 93-101.
3
Almulhim, M., & Zaman, N. (2018, February). Proposing secure and lightweight authentication scheme for IoT based E-health applications. In 2018 20th International Conference on advanced communication technology (ICACT) (pp. 481-487). IEEE.
4
Anunobi, C. V., & Okoye, I. B. (2008). The role of academic libraries in universal access to print and electronic resources in the developing countries. Library philosophy and practice, 5(20), 1-5.
5
Attkan, A., & Ahlawat, P. (2020). Lightweight two-factor authentication protocol and session key generation scheme for WSN in IoT deployment. In Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies (pp. 189-198). Springer, Singapore.
6
Gomez, C., & Paradells, J. (2010). Wireless home automation networks: A survey of architectures and technologies. IEEE Communications Magazine, 48(6), 92-101.
7
Mantas, G., Lymberopoulos, D., & Komninos, N. (2011). Security in smart home environment. In Wireless Technologies for Ambient Assisted Living and Healthcare: Systems and Applications (pp. 170-191). IGI global.
8
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660.
9
Han, K., Kim, J., Shon, T., & Ko, D. (2013). A novel secure key paring protocol for RF4CE ubiquitous smart home systems. Personal and ubiquitous computing, 17(5), 945-949.
10
He, D., Zeadally, S., Kumar, N., & Lee, J. H. (2016). Anonymous authentication for wireless body area networks with provable security. IEEE Systems Journal, 11(4), 2590-2601.
11
Jalal, A., Kamal, S., & Kim, D. (2014). A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors, 14(7), 11735-11759.
12
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13
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ORIGINAL_ARTICLE
Reconstruction of Simple and Complex Three Dimensional Images Using Pattern Recognition Algorithm
The reconstruction of 3D images is always a difficult task for the researchers. The 3D reconstruction of the image is a core technique of various fields such as Computer graphics, computer vision, CAD systems, medical science, computer application, etc. Reconstruction of the 3D image allows us to gather the quantitative features of the objects such as the shape, size, and volume of the objects. The existing computer algorithms need spatial dimension information to make the distinguished inference from the given 3D image which is not always possible. This paper simplifies the 3D reconstruction of the image. This research paper introduced a novel algorithm for the representation of the Three Dimensional images into a textual form. The syntactic approach is used for the extraction of the features of the image and these are called knowledge vectors. The knowledge vector consists of the direction information and length information. This a new approach in the field of image processing where images can be represented as a knowledge vector and it could be a great contribution in the field where security is a major concern. Further, the knowledge vector is used for the reconstruction of the 3D image. The performance of the algorithm is evaluated on the PASCAL 3D + and example-based Synthesis of the 3D Object Arrangements dataset. According to the obtained results, the proposed methodology is having better accuracy, and the processing time of reconstruction of the original 3D image is 1.02 Seconds. Single-pass is sufficient for reconstructing the original image
https://jitm.ut.ac.ir/article_87475_4dced9e47acf4126bc7758f2ef1d119f.pdf
2022-03-01
235
247
10.22059/jitm.2022.87475
Syntactic approach
construction
Reconstruction
3D images
Shilpa
Rani
shilpachoudhary2020@gmail.com
1
Ph.D. Scholar, Lovely Professional University, Punjab; Assistant Professor, Department of CSE, Neil Gogte Institute of Technology, Hyderabad, Telangana, India.
LEAD_AUTHOR
Deepika
Ghai
deepika.21507@lpu.co.in
2
Assistant Professor, Lovely Professional University, Punjab, India.
AUTHOR
Sandeep
Kumar
er.sandeepsahratia@gmail.com
3
Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, A.P., India.
AUTHOR
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