Lightweight Security Algorithm for Wireless Sensor Network Computer Security


  • Sohail Imran Khan Assistant Professor, Department of Business Administration, Lebanese French University, Erbil-Iraq
  • Roshani Raut Pimpri Chinchwad College of Engineering, Akurdi, Savitribai Phule Pune University, Pune, Maharashtra, India


WSN, wireless security, optimisation, NS2/3, SFR, SDN


Like computers, wireless sensor nodes (WSNs) possess a processing unit, a limited computing capability, limited memory, sensors, and a battery to provide power. While the sensors' high computational capability makes them useful for military and surveillance applications, their wireless nature brings some security risks. Typical wireless network security architectures do not work with sensor networks due to their wireless nature and limited resource availability. Moreover, wireless sensor networks are also vulnerable because they are often situated in hostile and dangerous environments without physical protection. Data routing through WSNs must, therefore, be secure. A novel security solution for WSN is presented in this paper to address this issue. Using proper digital communication, the identification of network errors is maintained. The proposed method uses Hamming code to maintain network error identification. Users define initial security bits for this study. Additional security check bits are added to the security code word for generation. This study demonstrates that wireless sensor network computer security has improved by 25% due to performance analysis. Compared to traditional computer security, this can provide better results.


Download data is not yet available.


Kumar, N., Rani, P., Kumar, V., Verma, P. K., & Koundal, D. (2023). TEEECH: Three-Tier Extended Energy Efficient Clustering Hierarchy Protocol for Heterogeneous Wireless Sensor Network. Expert Systems with Applications, 216, 119448.

Kumar, N., Rani, P., Kumar, V., Athawale, S. V., & Koundal, D. (2022). THWSN: Enhanced energy-efficient clustering approach for three-tier heterogeneous wireless sensor networks. IEEE Sensors Journal, 22(20), 20053-20062.

Soufiane, Z., Slimane, B., and Abdeslam, E. N. (2016, November). A synthesis of communication architectures and services of smart grid systems. In 2016 Third International Conference on Systems of Collaboration (SysCo) (pp. 1-6). IEEE.

Sahoo, P. and Dehury, C. (2018). Efficient data and CPU-intensive job scheduling algorithms for healthcare cloud. Computers and Electrical Engineering, 68, pp.119-139.

Smith, I. (2015). Joint academic network (JANET). Computer Networks and ISDN Systems, 16(1-2), pp.101-105.

Bhola, B., Kumar, R., Rani, P., Sharma, R., Mohammed, M. A., Yadav, K., ... & Alkwai, L. M. (2022). Quality‐enabled decentralized dynamic IoT platform with scalable resources integration. IET Communications.

Akbaş, D. and Gümüşkaya, H. (2011). Real and OPNET modeling and analysis of an enterprise network and its security structures. Procedia Computer Science, 3, pp.1038-1042.

Zhang, J., Chen, Y., Jin, N., Hou, L., and Zhang, Q. (2017, July). OPNET based simulation modeling and analysis of DoS attack for digital substation. In 2017 IEEE Power and Energy Society General Meeting (pp. 1-5). IEEE.

Rani, P., & Sharma, R. (2022, August). An Experimental Study of IEEE 802.11 n Devices for Vehicular Networks with Various Propagation Loss Models. In International Conference on Signal Processing and Integrated Networks (pp. 125-135). Singapore: Springer Nature Singapore.

Sarkar, N. I., Gul, S., and Anderton, B. (2019, January). Gigabit Ethernet with Wireless Extension: OPNET Modelling and Performance Study. In 2019 International Conference on Information Networking (ICOIN) (pp. 216-221). IEEE.

Luong, N.C., Hoang, D.T., Wang, P., Niyato, D. and Han, Z., 2017. Applications of economic and pricing models for wireless network security: A survey. IEEE Communications Surveys & Tutorials, 19(4), pp.2735-2767.

Fu, Y., Jiang, C.A., Qin, Y. and Yin, L., 2020. Secure routing and transmission scheme for space-ocean broadband wireless network. Science China Information Sciences, 63(4), p.149303.

Rubin, H., Brewington, J.K., Sawkar, A.S. and Poticny, D.M., All Purpose Networks LLC, 2018. Multiple-use wireless network. U.S. Patent 9,974,091.

Le Rouzic, E., Indre, R., Chiaraviglio, L., Musumeci, F., Pattavina, A., & Lopez Vizcaino, J. et al. (2013). TREND big picture on energy-efficient backbone networks. 2013 24Th Tyrrhenian International Workshop On Digital Communications - Green ICT (TIWDC).

Akbaş, D. and Gümüşkaya, H. (2011). Real and OPNET modelling and analysis of an enterprise network and its security structures. Procedia Computer Science, 3, pp.1038-1042.

Hussain, N., & Rani, P. (2020). Comparative studied based on attack resilient and efficient protocol with intrusion detection system based on deep neural network for vehicular system security. In Distributed Artificial Intelligence (pp. 217-236). CRC Press.

Hussain, N., Rani, P., Chouhan, H., & Gaur, U. S. (2022). Cyber security and privacy of connected and automated vehicles (CAVs)-based federated learning: challenges, opportunities, and open issues. Federated learning for IoT applications, 169-183.

Magnani, D., Carvalho, I. and Noronha, T. (2016). Robust Optimisation for OSPF Routing**This work was partially supported by CNPq, CAPES, and FAPEMIG. IFAC-PapersOnLine, 49(12), pp.461-466.

Sahoo, P. and Dehury, C. (2018). Efficient data and CPU-intensive job scheduling algorithms for healthcare cloud. Computers & Electrical Engineering, 68, pp.119-139.

Smith, I. (2015). Joint academic network (JANET). Computer Networks and ISDN Systems, 16(1-2), pp.101-105.

Amorosi, L., Chiaraviglio, L., Dell'Olmo, P., &Listanti, M. (2015). Sleep to stay alive: Optimising reliability in energy-efficient backbone networks. 2015 17Th International Conference On Transparent Optical Networks (ICTON).

Carpa, R., Gluck, O., &Lefevre, L. (2014). Segment routing based traffic engineering for energy-efficient backbone networks. 2014 IEEE International Conference On Advanced Networks And Telecommunications Systems (ANTS).

Energy-aware traffic engineering in hybrid SDN/IP backbone networks. (2016), 18(4), 559-566.

Ghosh, R., &Basagni, S. Napping backbones: energy efficient topology control for wireless sensor networks. 2006 IEEE Radio And Wireless Symposium.

Algorithm to Increase Energy Efficiency and Coverage for Wireless Sensor Network. (2015). International Journal of Science and Research (IJSR), 4(11), pp.1353-1357.

Chew, C. B., Mahinderjit-Singh, M., Wei, K. C., Sheng, T. W., Husin, M. H., and Malim, N. H. A. H. 2015. Sensors-enabled smart attendance systems using NFC and RFID technologies. Int. J. New Comput. Archit. Appl, 5, 19-29.

Orozco, J., Chavira, G., Castro, I., Bolaños, J. F., Sánchez, R. A., and Cantú, J. F. 2014. Towards NFC and RFID Combination to Automatic Services. International Journal of Engineering, 48, 8269.

Park, C. W., Ahn, J. H., and Lee, T. J. 2017. Fast object identification with mode switching for coexistence of NFC and RFID. Transactions on Emerging Telecommunications Technologies, 282, e2939.

Prodanoff, Z. G., Jones, E. L., Chi, H., Elfayoumy, S., and Cummings, C. 2016. Survey of Security Challenges in NFC and RFID for E-Health Applications. International Journal of E-Health and Medical Communications IJEHMC, 72, 1-13.

Sekiguchi, T., Okano, Y., Ohmura, N., and Ogino, S. 2015, August. Study on effective pattern of magnetic sheet attached on NFC antenna. In Radio-Frequency Integration Technology RFIT, 2015 IEEE International Symposium on pp. 208-210. IEEE.

Sekiguchi, T., Okano, Y., Ohmura, N., and Ogino, S. 2015, November. A study on the effective pattern of magnetic sheet considering their characteristics attached on NFC antenna. In Antennas and Propagation ISAP, 2015 International Symposium pp. 1-4. IEEE.

Wahab, M. H. A., Suhaimi, N. F. M., Mohsin, M. F. M., Mustapha, A., Samsudin, N. A., and Ambar, R. 2018, June. NFC-based Data Retrieval Device. In Journal of Physics: Conference Series Vol. 1019, No. 1, p. 012084. IOP Publishing.

Wu, C. C., Hsu, C. W., & Cheng, R. S. (2018, April). The digital signature technology for access control system of mobile. In 2018 IEEE International Conference on Applied System Invention (ICASI) (pp. 896-898). IEEE.

Nayak, R. ., & Samanta, S. . (2023). Prediction of Factors Influencing Social Performance of Indian MFIs using Machine Learning Approach. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 77–87.

Andrew Hernandez, Stephen Wright, Yosef Ben-David, Rodrigo Costa, David Botha. Risk Assessment and Management with Machine Learning in Decision Science. Kuwait Journal of Machine Learning, 2(3). Retrieved from

Dhabliya, D., Dhabliya, R. Key characteristics and components of cloud computing (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 12-18.




How to Cite

Khan, S. I. ., & Raut, R. . (2023). Lightweight Security Algorithm for Wireless Sensor Network Computer Security. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 147–157. Retrieved from



Research Article