Ensuring the Security and Privacy of Data in Wireless Sensor Intelligence Networks While Simultaneously Optimizing Usability and Efficacy

Authors

  • Parag Kalkar Pro-Vice Chancellor, Savitribai Phule Pune University, Pune, Maharashtra
  • Gaurav Katoch Assistant Professor, Jaypee Business School, JIIT Noida, Uttar Pradesh
  • Rasna Sehrawat Assistant Professor, Amity Institute of Education, Amity University, Noida (U.P.)
  • Deepali Rani Sahoo Assistant Professor, Symbiosis Law School, Noida, Symbiosis International (Deemed University),Pune
  • AR . Saravanakumar Associate Professor, Department of Education, CDOE, Alagappa University, Karaikudi-630 003, Tamil Nadu
  • Arjun Singh Associate Professor, Department of Computer and Communication Engineering, Manipal University Jaipur, Rajasthan
  • Pankaj Kumar Mishra Professor, Glocal School of Science and Technology, Glocal University, Delhi-Yamunotri Marg, Saharanpur, U.P., India

Keywords:

Wireless Network, Wireless Security, Wireless Threats, Wireless Privacy, Optimization

Abstract

It is critical to ensure data privacy and security in wireless sensor intelligence networks (WSINs).A comprehensive strategy is needed to balance data security, privacy, usability, and efficacy. It is crucial to achieve the ideal balance between protecting confidential data and ensuring the WSIN serves its intended purpose while adhering to privacy and legal standards. However, can be balanced with optimizing usability and efficacy. This paper explores existing approaches to achieving this balance and proposes a HPSOGA Algorithm to get optimal solution to a variety of security issues by replicating natural behaviours and processes.

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References

Choi, M. K., Robles, R. J., Hong, C. H., & Kim, T. H. (2008). Wireless network security: Vulnerabilities, threats and countermeasures. International Journal of Multimedia and Ubiquitous Engineering, 3(3), 77-86.

Choi, M. K., Robles, R. J., Hong, C. H., & Kim, T. H. (2008). Wireless network security: Vulnerabilities, threats and countermeasures. International Journal of Multimedia and Ubiquitous Engineering, 3(3), 77-86.

Chhikara, P., & Patel, A. K. (2013). Enhancing network security using ant colony optimization. Global J. Comput. Sci. Technol. Netw. Web Secur, 13(4), 19-22.

Dasgupta, D. (Ed.). (2012). Artificial immune systems and their applications. Springer Science & Business Media.

How To Overcome Challenges For Remote Workers. https://hrchallenges.com/how-to-overcome-challenges-for-remote-workers/

Jatana N and Suri B, “Particle swarm and genetic algorithm applied to mutation testing for test data generation: a comparative evaluation”, Journal of King Saud University-Computer and Information Sciences, vol.32, no.4, pp.514-21, May 2020.

Kaur, D. K. (2015). A ride from molecular recognition to development of optical sensors - An introduction. Kaav International Journal of Science, Engineering & Technology, 2(3), 28-50.

Kingma, D.P. and Ba, J., "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.

Noor, M. M., & Hassan, W. H. (2013). Wireless networks: developments, threats and countermeasures. International Journal of Digital Information and Wireless Communications (IJDIWC), 3(1), 125-140.

Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.

P. (2017). Formal Verification of Energy Saving Techniques In Wireless Sensor Networks (WSN). Kaav International Journal of Science, Engineering & Technology, 4(3), 132-139.

Potlapally, N. R., Ravi, S., Raghunathan, A., & Jha, N. K. (2005). A study of the energy consumption characteristics of cryptographic algorithms and security protocols. IEEE Transactions on mobile computing, 5(2), 128-143.

R., D. C. (2019). Energy Efficient Scheduling and Clustering in Wireless Sensor Networks: A Review. National Journal of Arts, Commerce & Scientific Research Review, 6(1), 274-279.

Sathyavani, K. S., & Selvi, P. (2014). Wireless network security vulnerabilities, threats and countermeasures. In International Conference on Information and Image Processing. Retrieved from http://www. conference. bonfring. org/papers/sankara_iciip2014/iciip89. pdf.

Teodorović, D. (2009). Bee colony optimization (BCO). In Innovations in swarm intelligence (pp. 39-60). Berlin, Heidelberg: Springer Berlin Heidelberg.

Yang, X. S., & Deb, S. (2014). Cuckoo search: recent advances and applications. Neural Computing and applications, 24, 169-174.

Yang, X. S., & He, X. (2013). Bat algorithm: literature review and applications. International Journal of Bio-inspired computation, 5(3), 141-149.

Yang, X. S., & He, X. (2013). Firefly algorithm: recent advances and applications. International journal of swarm intelligence, 1(1), 36-50.

Wang, D., Tan, D. and Liu, L., "Particle swarm optimization algorithm: an overview," Soft Computing, vol.22, no.2, pp.387-408, 2018.

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Published

07.01.2024

How to Cite

Kalkar, P. ., Katoch, G. ., Sehrawat, R. ., Sahoo, D. R. ., Saravanakumar, A. . ., Singh, A. ., & Mishra, P. K. . (2024). Ensuring the Security and Privacy of Data in Wireless Sensor Intelligence Networks While Simultaneously Optimizing Usability and Efficacy. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 226–231. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4365

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Section

Research Article