Abstract
The field of computer vision is encountering an extraordinary jump forward improvement today. This targets giving a thorough review of the ongoing advancement on computer vision calculations and their related equipment usage. Specifically, the noticeable accomplishments in computer vision errands, for example, picture characterization, object identification, and picture division, brought by profound learning methods are featured. Then again, survey of procedures for executing and upgrading profound learning put together by computer vision calculations with respect to Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), and other new ages of equipment quickening agents is introduced to encourage ongoing or potentially energy-productive activities. At long last, a few promising bearings for future examination are introduced to propel further advancement in the field. The capacity to convey remote sensor networks in far-off territories is making new occasions to screen jeopardized species and their current circumstance so as to guarantee their insurance and security, and trustworthiness is the fundamental issue in Internet of Things (IoT)-based organization climate in which capture attempt free made sure about correspondence is required. The blockchain innovation is one of the indisputable and tip-top procedures that can be used for the joining of security with remote frameworks. This part gives the definite contextual investigation on blockchain about IoT climate for checking and securing the natural life creatures like tigers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). University of Washington, Allen Institute for AI, Facebook AI Research.
Lex, H., Phil, L., Narendra, P., Narayanarao, K., Arjun, G., & Karanth, K. (2009). A tiger cannot change its stripes: Using a three-dimensional model to match images of living tigers and tiger skins. Biology Letters, 5, 383–386. https://doi.org/10.1098/rsbl.2009.0028.
Kumar, S., Gopalaswamy, A. M., & Karanth, K. U. (2011). Counting India’s wild tigers reliably. American Association for the Advancement of Science, 332(6031), 791.
Cheema, G. S., & Anand, S. (2017). Automatic detection and recognition of individuals in patterned species. Machine Learning and Knowledge Discovery in Database, 27–38.
Iyer, N., Jayanti, S., Lou, K., Kalyanaraman, Y., & Ramani, K. (2005). Three-dimensional shape searching: State-of-the-art review and future trends. Computer-Aided Design, 37(5), 509–530.
Lowe, D. G. (2004). Distinctive image features from scale-invariant key points. Computer Science Department, and University of British Columbia, 60, 91–110.
Gowtham, M. Intrusion detection and avoidance for home and smart city automation in IoT. In IoT: Security and Privacy Paradigm. CRC Press, Taylor and Francis Group.
Bahga, A., & Madisetti, V. (2017). Blockchain applications: A hands-on approach. Computers & Internet.
Chondros, N., Kokordelis, K., & Roussopoulos, M. (2012). On the practicality of practical byzantine fault tolerance. ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, 436–455.
Gowtham, M., Banga, M. K., & Patil, M. Secure internet of things: Assessing challenges and scopes for NextGen communication. In 2nd IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT-2019) at Vimal Jyothi Engineering College, Kerala.
Heilman, E., Kendler, A., Zohar, A., & Goldberg, S. (2015). Eclipse attacks on bitcoin’s peer-to-peer network. In Proceedings of the 24th USENIX Conference on Security Symposium, ser. SEC’15. USENIX Association (pp. 129–144).
Ohbuchi, R., Osada, K., Furuya, T., & Banno, T. Salient Local Visual Features for Shape-Based 3D Model Retrieval.
Gowtham, M. CL-PKA: Key management in dynamic wireless sensor network: A novel framework. In 3rd International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT-2018) at P.E.S College of Engineering, Mandya. Springer-LNEE.
Deepak, T., Shetty, S., & Kevin, A. K. (2017). Security implications of blockchain cloud with analysis of block withholding attack. In 17th IEEE/ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID) (p. 458).
Kiayias, A., Russell, A., David, B., & Oliynykov, R. (2016). Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol. Springer.
Swanson, T. Consensus-as-a-service: A brief report on the emergence of permissioned distributed ledger systems.
Duong, T., Fan, L., & Zhou, H. S. (2016). 2-hop Blockchain: Combining Proof-of-Work and Proof-of-Stake Securely. Springer.
Wood, G. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper, 151.
Ji, S., Cai, Z., Han, M., & Beyah, R. (2015). Whitespace measurement and virtual backbone construction for cognitive radio networks: From the social perspective. In Sensing Communication and Networking (SECON) 2015 12th Annual IEEE International Conference on IEEE (pp. 435–443).
Han, M., Yan, M., Li, J., Ji, S., & Li, Y. (2014). Neighborhood-based uncertainty generation in social networks. Journal of Combinatorial Optimization, 28, 561–576.
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture consensus and future trends. In Big Data (BigData Congress) 2017 IEEE International Congress on IEEE (pp. 557–564).
Min, X., Li, Q., Liu, L., & Cui, L. (2016, August). A permissioned blockchain framework for supporting instant transaction and dynamic block size. In 2016 IEEE Trustcom/BigDataSE/ISPA (pp. 90–96).
Malkhi, D. (2012, January). Byzantine quorum systems. Distributed Computing, 4, 203213.
Kosba, A., Miller, A., Shi, E., Wen, Z., & Papamanthou, C. (2016, May). Hawk: The blockchain model of cryptography and privacy-preserving smart contracts. In IEEE Symposium on Security and Privacy (pp. 839–858).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Vishwas, D.B., Gowtham, M., Ajay, A.V., Raghavendra, K., Ravi, V., Goundar, S. (2022). Blockchain-Based Secure Method for Tiger Detection Using Machine Learning. In: Gururaj, H.L., Ravi Kumar, V., Goundar, S., Elngar, A.A., Swathi, B.H. (eds) Convergence of Internet of Things and Blockchain Technologies. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-76216-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-76216-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76215-5
Online ISBN: 978-3-030-76216-2
eBook Packages: EngineeringEngineering (R0)