International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Cluster Routing for Real Time Location Awareness QoS Specific Spatial Distribution for Improved QoS in Wireless Sensor Networks (WSNs)

Author NameAuthor Details

K. Kalaiselvi, Chin-Shiuh Shieh, V. Senthil Murugan

K. Kalaiselvi[1]

Chin-Shiuh Shieh[2]

V. Senthil Murugan[3]

[1]Faculty of Engineering and Technology, Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

[2]Research Institute of IoT Cybersecurity, Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Taiwan.

[3]Faculty of Engineering and Technology, Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

Abstract

Wireless Sensor Networks (WSNs) have been used in various applications because of their salient characteristics. However, like any other network, they have their deficiencies. The sensor nodes of WSN have been fabricated with the radio device capable of transmitting or receiving the radio signals within a specific range. This restricts the sensor's direct communication with the faraway node. Regarding data transmission, the source and destination would be located in different geographies. Still, to perform data transmission, they are involved in cooperative transmission. Similarly, the traffic-based approaches select the route based on the traffic with the least traffic route. This introduces a longer hop count and increases the latency, which in turn affects the throughput performance. Finally, a QoS Specific Spatial Distribution Clustering Routing (QSSDCR) approach is presented. The method clusters the nodes according to their distribution factor of any window and identifies the list of routes in each cluster. The clustering of nodes is performed according to the value of the CTS (Cluster Transmission Support) measure, where the value of CTS is measured according to different factors like throughput, traffic, and energy. Similarly, the forwarding route is selected according to the Localized Coordinate Route Measure (LCRM) to perform data transmission. The proposed method improves the performance in data transmission and improves the QoS in WSN.

Index Terms

CTS (Cluster Transmission Support)

Cluster Head Selection

Data Transmission

Localized Coordinate Route Measure (LCRM)

QoS Specific Spatial Distribution Clustering Routing (QSSDCR)

Wireless Sensor Networks (WSNs)

Reference

  1. 1.
    Malarvizhi, K, Brindha, M & Kumar, M “Evaluation of energy efficient routing in wireless multimedia sensor networks”, IEEE (ICECS),2015, pp. 1387-1391.
  2. 2.
    A. Lipare and D. R. Edla, "Cluster Head Selection and Cluster Construction using Fuzzy Logic in WSNs," 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, 2019, pp. 1-4, doi: 10.1109/INDICON47234.2019.9030302.
  3. 3.
    R. S. Ranjith and H. N. Vishwas, "Evaluation study of secondary cluster head selection using fuzzy logic in WSN for conservation of battery energy," 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2017, pp. 50-55, doi: 10.1109/ICICCT.2017.7975157
  4. 4.
    John, A., Rajput, A., &Babu, K. V. “Energy saving cluster head selection in wireless sensor networks for Internet of things applications”, International Conference on Communication and Signal Processing (ICCSP). (2017). doi:10.1109/iccsp.2017.8286486.
  5. 5.
    Haider, S. K., Jamshed, M. A., Jiang, A., Pervaiz, H., & Ni, Q. “UAV-assisted Cluster-head Selection Mechanism for Wireless Sensor Network Applications”, 2019 UK/ China Emerging Technologies (UCET), doi:10.1109/ucet.2019.8881889.
  6. 6.
    W. A. Altakhayneh, M. Ismail, M. A. Altahrawi and M. K. AbuFoul, "Cluster Head Selection Using Genetic Algorithm in Wireless Network," 2019 IEEE 14th Malaysia International Conference on Communication (MICC), Selangor, Malaysia, pp. 13-18, doi: 10.1109/MICC48337.2019.9037609.
  7. 7.
    Panchal, A., & Singh, R. K. “Energy Efficient Cluster Head Selection with Adaptive Threshold in WSN”, 2018 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), doi:10.1109/ispacs.2018.8923283.
  8. 8.
    Alafeef, I, Awad, F & Al-Madi, N, “Energy-aware geographic routing protocol with sleep scheduling for wireless multimedia sensor networks”, IEEE (HONET-ICT), 2017, pp. 93-97.
  9. 9.
    Lenka, RK, “Cluster-based rendezvous routing protocol for wireless sensor network”, IEEE (ICCCA), 2017, pp. 748-752.
  10. 10.
    Bhaskar, SCV & Rani, VR, “Performance analysis of efficient routing protocols to improve quality of service in Wireless Sensor networks”, IEEE (ICCSP), 2017, pp. 0006-0009.
  11. 11.
    Cao, N et al., “The Comparisons of Different Location-Based Routing Protocols in Wireless Sensor Networks”, IEEE (CSU/EUC), 2017, pp. 324-327.
  12. 12.
    Kamarei, M., Patooghy, A., Alsharif, A., &Hakami, V,” SiMple: A Unified Single & Multi-Path Routing Algorithm for MWSNs with Source Location Privacy”. IEEE Access, (2020). 1–1. doi:10.1109/access.2020.2972354.
  13. 13.
    Zhang, J., Ren, F., Gao, S., Yang, H., & Lin, C,”Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless Sensor Networks”. IEEE Transactions on Mobile Computing,. (2015). 14(2), 328–343. doi:10.1109/tmc.2014.2313576.
  14. 14.
    Chen, W., Zhang, M., Hu, G., Tang, X., &Sangaiah, A. K.,” Constrained Random Routing Mechanism for Source Privacy Protection in MWSNs”. IEEE Access, (2017). 5, 23171–23181. doi:10.1109/access.2017.2752179.
  15. 15.
    Di Valerio, V., Presti, F. L., Petrioli, C., Picari, L., Spaccini, D., &Basagni, S. CARMA: Channel-aware Reinforcement Learning-based Multi-path Adaptive Routing for Underwater Wireless Sensor Networks. IEEE Journal on Selected Areas in Communications, (2019). 1–1. doi:10.1109/jsac.2019.2933968.
  16. 16.
    Muruganantham, N & El-Ocla, H, ‘Genetic Algorithm-Based Routing Performance Enhancement in Wireless Sensor Networks’, IEEE (ICCIS), 2018, pp. 79-82.
  17. 17.
    Wang, J., Bigham, J., & Phillips, C. A Geographical Proximity Aware Multi-Path Routing Mechanism for Resilient Networking. IEEE Communications Letters, (2017), 21(7), 1533–1536. doi:10.1109/lcomm.2017.2691698.
  18. 18.
    Farhoudi, M., Abrishamchi, B., Mihailovic, A., &Aghvami, A. H, Analysis of Practical Aspects of Multi-Plane Routing-Based Load Balancing Approach for Future Link-State Convergent All-IP Access Networks. IEEE Transactions on Mobile Computing, 17(4), (2018). 803–816. doi:10.1109/tmc.2017.2741479.
  19. 19.
    Jain, S, Pattanaik, KK, Verma, RK & Shukla, A “QRRP: A Query-driven Ring Routing Protocol for Mobile Sink based Wireless Sensor Networks”, IEEE (TENCON), 2019, pp. 1986-1991.
  20. 20.
    Pai, K.-J., & Chang, J.-M. “Dual-CISTs: Configuring a Protection Routing on Some Cayley Networks. IEEE/ACM Transactions on Networking, (2019). 1–12. doi:10.1109/tnet.2019.2910019.
  21. 21.
    W. -K. Yun and S. -J. Yoo, "Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks," in IEEE Access, 202, vol. 9, pp. 10737-10750, doi: 10.1109/ACCESS.2021.3051360.
  22. 22.
    Shital M. Shrirao, Gitanjali R. Shinde, Parikshit N. Mahalle, Nilesh P. Sable,Vivek S. Deshpande, "Navigating Congestion in Wireless Sensor Network: A Comprehensive Survey", International Journal of Computer Networks and Applications (IJCNA), 11(4), PP: 428-448, 2024, DOI: 10.22247/ijcna/2024/27.