Bi-Level Minimal Resource Protected Key Generation Framework For Fog Computing Applications

Authors

  • Arul Sindhia P. University College of Engineering Nagercoil, India
  • Bharathi.R University College of Engineering Nagercoil, India

DOI:

https://doi.org/10.15837/ijccc.2022.6.4363

Keywords:

Minimal Resource Viterbi based Bi-level Secured Key Generation (MRV-BSKG) , Fog Computing, Lagrange’s Key, Location-Based Key, Shortest path

Abstract

Fog computing is a viewpoint that expands on the Cloud stage concept by placing processing assets at the organization’s edges. It might be described as a cloud-like platform with comparable data, computation, storage, and applications. They are unique in that they are decentralized in nature. Data protection and route analysis of time-sensitive data are made easier using fog computing. This minimizes the volume and distance of data sent to the cloud, lowering the risk of security and privacy breaches in IoT applications. When it comes to security and privacy, fog computing confronts several issues. The constraints of fog computing resources are the root of these difficulties. The fog system, in fact, may raise new security and privacy concerns. To address these challenges, cryptography is used in conjunction with key management techniques to provide safe data transfer. A Minimal Resource Viterbi based Bi-level Secured Key Generation (MRV-BSKG) technique for a secured fog-based system is proposed to compromise the security level and computational complexity. The BSKG technique, which combines Lagrange’s Key Generation (LKG) and the Location-Based Key (LBK) generation approaches, can safeguard secrecy and integrity. In comparison to the previous techniques, the new MRV, BSKG, delivers security with improved outcomes.

References

Haina Zheng, KeXiong, Pingyi Fan, ZhangduiZhong and Khaled Ben Letaief, 2019."Fog-Assisted Multi-User SWIPT Networks: Local Computing or Offloading", IEEE Internet of Things Journal, pp. 5246-5264.

https://doi.org/10.1109/JIOT.2019.2899458

Andrea Tassi, IoannisMavromatis, Robert Piechockiy and Andrew Nix, 2019."Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs", Networking and Internet Architecture.

https://doi.org/10.1109/VTCSpring.2019.8746302

Mahesh U. Shankarwar and Ambika V. Pawar, 2016."Security and Privacy in Cloud Computing: A Survey", Springer.

https://doi.org/10.1007/978-3-319-12012-6_1

MadjidMerabti, Bashar Alohali and KashifKifayat,2015. "A New Key Management Scheme Based on Smart Grid Requirements", Advances in Information Science and Computer Engineering, pp. 436-444.

W. Wang and Z. Lu, 2013 "Cyber security in the smart grid: Survey and challenges," Computer Networks, vol. 57, no. 5, pp. 1344-1371.

https://doi.org/10.1016/j.comnet.2012.12.017

M. Benmalek and Y. Challal, 2016 "MK-AMI: Efficient multi-group key management scheme for secure communications in AMI systems," in Proc. of IEEE Wireless Communications and Networking Conference (IEEE WCNC), pp. 1-6.

https://doi.org/10.1109/WCNC.2016.7565124

K. K. R. Choo, O. F. Rana, and M. Rajarajan. 2017"Cloud Security Engineering:Theory, Practice and Future Research." IEEE Transactions on CloudComputing, 5(3):372-374.

https://doi.org/10.1109/TCC.2016.2582278

O. A. Osanaiye, S. Chen, Z. Yan, R. Lu, K. K. R. Choo, and M. E.Dlodlo. 2017 From "Cloud to Fog Computing: A Review and a ConceptualLive VM Migration Framework". IEEE Access, 5(1):8284-8300.

https://doi.org/10.1109/ACCESS.2017.2692960

AshkanYousefpour et al, 2017 "All one needs to know about fog computing and related edge computing paradigms: A complete survey", Elsevier, Journal of Systems Architecture, Vol. 89, pp. 289-330.

https://doi.org/10.1016/j.sysarc.2019.02.009

Shanhe Yi, Zhengrui Qin, and Qun Li, 2015 "Security and Privacy Issues of Fog Computing: A Survey", Conference on wireless algorithms, systems, Springer.

P. Zhang, T. Zhuo, W. Huang, K. Chen, M. Kankanhalli, Online object tracking based on CNN with spatial-temporal saliency guided sampling, Neurocomputing 257 (2017) 115-127.

https://doi.org/10.1016/j.neucom.2016.10.073

J. Zhang, K.A. Ehinger, H.Wei, K. Zhang, J. Yang, A novel graph-based optimization framework for salient object detection, PatternRecognit. 64 (1) (2017) 39-50.

https://doi.org/10.1016/j.patcog.2016.10.025

H. Chen, Y. Li, D. Su, Multi-modal fusion network with multi-scale multi- path and cross-modal interactions for RGB-D salient object detection, Pattern Recognit. 1 (1) (2018).1-1.

E. Macaluso, C.D. Frith, J. Driver, Directing attention to locations and to sensory modalities: multiple levels of selective processing revealed with PET, Cerebral Cortex 12 (4) (2002) 357-368.

https://doi.org/10.1093/cercor/12.4.357

T.S. Lee, D. Mumford, Hierarchical bayesian inference in the visual cortex, JOSAA 20 (7) (2003) 1434-1448.

https://doi.org/10.1364/JOSAA.20.001434

Q. Yan, L. Xu, J. Shi, J. Jia, Hierarchical saliency detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1155-1162.

https://doi.org/10.1109/CVPR.2013.153

. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597-1604. IEEE (2009)

https://doi.org/10.1109/CVPR.2009.5206596

Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409-416. IEEE (2011)

https://doi.org/10.1109/CVPR.2011.5995344

Cui, X., Liu, Q., Metaxas, D.: Temporal spectral residual: fast motion saliency detection. In: Proceedings of the ACM International Conference on Multimedia (2009).

https://doi.org/10.1145/1631272.1631370

B. X. Nie, P. Wei, and S.-C. Zhu, "Monocular 3D human pose estimation by predicting depth on joints." in IEEE International Conference on Computer Vision, 2017

https://doi.org/10.1109/ICCV.2017.373

D. Zhang, J. Han, C. Li, J. Wang, and X. Li, "Detection of co-salient objects by looking deep and wide", International Journal of Computer Vision, vol. 120, no. 2, pp. 215-232, 2016.

https://doi.org/10.1007/s11263-016-0907-4

X. Dong et al., "Occlusion-aware real-time object tracking," IEEE Trans. Multimedia, vol. 19, no. 4, pp. 763-771, Apr. 2017.

https://doi.org/10.1109/TMM.2016.2631884

X. Dong, J. Shen, L. Shao, and L. Van Gool, "Sub-Markov random walk for image segmentation," IEEE Trans. Image Process., vol. 25, no. 2, pp. 516-527, Feb. 2016.

https://doi.org/10.1109/TIP.2015.2505184

J. Shen et al., "Real-time superpixel segmentation by DBSCAN clustering algorithm", IEEE Trans. Image Process., vol. 25, no. 12, pp. 5933-5942, Dec. 2016.

https://doi.org/10.1109/TIP.2016.2616302

Y. Yuan, C. Li, J. Kim, W. Cai, D.D. Feng, Dense and sparse labeling with multidimensional features for saliency detection, IEEE Trans. Circuits Syst. Video Technol. 28 (5) (2018) 1130-1143.

https://doi.org/10.1109/TCSVT.2016.2646720

W. Wang, J. Shen, F. Guo, M.-M. Cheng, A. Borji, Revisiting video saliency: a large-scale benchmark and a new model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4894-4903.

https://doi.org/10.1109/CVPR.2018.00514

Li Q., Chen S., Zhang B. (2012) Predictive Video Saliency Detection. In: Liu CL., Zhang C., Wang L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg.

https://doi.org/10.1007/978-3-642-33506-8_23

Wang, Wenguan et al. "Deep Learning For Video Saliency Detection." ArXiv abs/1702. 00871 (2017): n. pag.

F. Guo et al., "Video Saliency Detection Using Object Proposals," in IEEE Transactions on Cybernetics, vol. 48, no. 11, pp. 3159-3170, Nov. 2018.

https://doi.org/10.1109/TCYB.2017.2761361

Karthik, A., MazherIqbal, J.L. Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder. Wireless Pers Commun 119, 1959-1973 (2021).

https://doi.org/10.1007/s11277-021-08313-6

Yuming Fang, Xiaoqiang Zhang, Feiniu Yuan, NevrezImamoglu, Haiwen Liu, Video saliency detection by gestalt theory, Pattern Recognition, Volume 96,2019,106987, ISSN 0031-3203.

https://doi.org/10.1016/j.patcog.2019.106987

https://docs.microsoft.com/en-us/cpp/build/reference/clr common language runtime compilation? View = msvc-160

https://docs.microsoft.com/en-us/cpp/dotnet/walkthrough-compiling-a-cpp-program-thattargets- the-clr-in-visual-studio?view=msvc-160

https://en.wikipedia.org/wiki/Common_Language_Runtime

https://www.red-gate.com/simple-talk/dotnet/net-development/creating-ccli-wrapper/

Wang, Bofei et al. "Object-based Spatial Similarity for Semi-supervised Video Object Segmentation." (2019).

https://doi.org/10.1109/TPAMI.2018.2819173

Li F., Kim T., Humayun A., Tsai D., Rehg J. M.,"Video Segmentation byTracking Many Figure- Ground Segments" In:IEEE International Conference onComputer Vision (ICCV), 2013.

https://doi.org/10.1109/ICCV.2013.273

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Published

2022-12-14

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