Open Access Journal

ISSN : 2456-1304 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Science Engineering and Management (IJSEM)

Monthly Journal for Science Engineering and Management
Call For Paper : Vol 11, Issue 08, August 2024

ISSN : 2456-1304 (Online)

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Secured Communication and Trust Based Protocol Model Using Whale Swarm Secure Clustering Algorithm

Author : Syed Mohd Ali 1 Syed Abdul Sattar 2 D. Srinivasa Rao 3

PDF

Date of Publication :30th September 2020

Abstract: Heterogeneous Wireless Sensor Network users are increasing quickly in virtually every field of the infrastructure that needs for use efficiently. In WSNs, clustering is a useful technique to expand that network's lifetime and to protect each node in wireless networks. With several secure and reliable optimizations, or secure local optimization, many real-world optimization issues arise. Because of the untrustworthy architecture in a local and global environment, these nodes face communication skills issues such as small range and high throughput, which are likely to limit wireless communications in WSNs, making it difficult for WSNs to function properly. Another issue is the security and privacy factor where multiple sensors are unable to prevent unauthorized access and malicious attacks which lead to breaches of security. To address communication capacity issues and stable sensor systems (SS), we proposed a new framework called the Secured Communication and Trust-based Protocol Model (SC- TBPM) that takes into account reliability, protection and responsiveness. This model uses a Whale Swarm secured clustering Algorithm (WSSC) that mainly focuses on trustworthy nodes as cluster heads (CHs) by considering security parameters such as residual energy (ER), node density (DN), and average cluster distance (ADC).

Reference :

    1.  Sharma, R., Vashisht, V., Singh, A.V., et al.: ‘Analysis of existing clustering algorithms for wireless sensor networks’ in Kapur, P., Klochkov, Y., Verma, A., et al. (Eds.): ‘System Performance and Management Analytics. Asset Analytics (Performance and Safety Management)’ (Springer, Singapore, 2018), pp. 259–277
    2.  Ruan D, Huang J. A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks. Sensors (Basel). 2019;19(8):1835. Published 2019 Apr 17. doi:10.3390/s19081835
    3. Sharma, R., Vashisht, V., Singh, U., et al.: ‘Nature inspired algorithms for energy efficient clustering in wireless sensor networks’. Int. Conf. on Cloud Computing, Data Science and Engineering (Confluence), Noida, Uttar Pradesh, India, 2019, pp. 365–
    4. Juliana, R., Maheswari, P.U.: ‘An energy efficient cluster head selection technique using network trust and swarm intelligence’, Wirel. Pers. Commun., 2016, 89, (2), pp. 351–364
    5. Bayrakdar, M.E.: ‘A smart insect pest detection technique with qualified underground wireless sensor nodes for precision agriculture’, IEEE Sens. J., 2019, 19, (22), pp. 10892–10897, doi: 10.1109/JSEN.2019.2931816
    6. Bayrakdar, M.E.: ‘Cooperative communication based access technique for sensor networks’, Int. J. Electron., 2019, in press, doi: 10.1080/00207217.2019.1636313
    7. Arumugam, G.S.; Ponnuchamy, T. EE-Leach: Development of energy-efficient LEACH protocol for data gathering in WSN. EURASIP J. Wireless Commun. Netw. 2015, 2015, 1–9. [CrossRef]
    8. Yuea, J.; Zhang, W.; Xiao, W.; Tang, D.; Tang, J. Energy efficient and balanced cluster-based data aggregation algorithm for wireless sensor networks. Procedia Eng. 2012, 29, 2009–2015. [CrossRef]
    9. Pant,M.;Dey,B.;Nandi,S.Amulti-hop routing protocol for wireless sensor network based on grid clustering. In Proceedings of the 2015 Applications and Innovations in Mobile Computing (AIMoC), Kolkata, India, 12–14 February 2015; pp. 137–140. [CrossRef] 
    10. Huang,J.;Hong,Y.;Zhao,Z.;Yuan,Y.Anenergyefficientmulti-hoprouting protocol based on grid clustering for wireless sensor networks. Clust. Comput. 2017, 20, 3071–3083. [CrossRef]
    11. Ari, A.A.A.; Labraoui, N.; Yenké, B.O.; Gueroui, A. Clustering algorithm for wireless sensor networks: The honeybee swarms nest-sites selection process based approach. Int. J. Sens. Netw. 2018, 27, 1–13. [CrossRef]
    12. ]Yalçın, S.; Erdem, E. Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing in Mobile Heterogeneous Wireless Sensor Networks. Sensors 2019, 19, 867. [CrossRef]
    13. Karaboga, D.; Okdem, S.; Ozturk, C. Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel. Netw. 2012, 18, 847–860. [CrossRef]
    14. Rao, P.C.S.; Jana, P.K.; Banka, H. A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel. Netw. 2017, 23, 2005–2020. [CrossRef]
    15. Kuila, P.; Jana, P.K. Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Eng. Appl. Artif. Intell. 2014, 33, 127–140. [CrossRef]
    16. Bing Zeng, Xiang Li, Liang Gao, Yuyan Zhang and Haozhen Dong, “Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization”, Received: 9 July 2018 / Accepted: 18 December 2018 Springer-Verlag London Ltd., part of Springer Nature 2019
    17. Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ. An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network. Sensors (Basel). 2019;19(3):671. Published 2019 Feb 7. doi:10.3390/s19030671

Recent Article