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

Quality of Service (QoS) Enhancement in Healthcare Mobile Wireless Sensor Networks Using Adaptable Hummingbird Optimization Based Dynamic Source Routing (AHODSR)

Author NameAuthor Details

S. Kawsalya, D. Vimal Kumar

S. Kawsalya[1]

D. Vimal Kumar[2]

[1]Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, India.

[2]Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, Tamil Nadu, India.

Abstract

The research introduces MC-DSR-AHO a routing protocol integrating Markov Chain-based Dynamic Source Routing (MC-DSR) with Adaptable Hummingbird Optimization (AHO) for Mobile Wireless Sensor Networks (MWSNs). MWSNs face challenges such as packet loss, latency, limited throughput, and energy inefficiency in dynamic and resource-constrained environments. MC-DSR-AHO addresses these issues by combining the probabilistic modeling of MC-DSR with the adaptive optimization of AHO. This integration results in improved packet delivery reliability, reduced packet drops, efficient data transmission, optimized delays, and energy conservation. Simulations demonstrate the protocol’s scalability and consistent performance across varying node counts. This research highlights the effectiveness of utilizing probability modeling and bio-inspired optimization to enhance the adaptability and efficiency of routing protocols in MWSNs. MC-DSR-AHO represents a significant advancement, providing practical benefits and guiding future research in dynamic network environments.

Index Terms

Dynamic Source Routing

Hummingbird Optimization

MWSNs

Routing Protocol

Bio-inspired Optimization

QoS

Healthcare

Reference

  1. 1.
    S. El khediri et al., “Integration of artificial intelligence (AI) with sensor networks: Trends, challenges, and future directions,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 1, p. 101892, 2024, doi: 10.1016/j.jksuci.2023.101892.
  2. 2.
    D. W. Wajgi and J. V. Tembhurne, “Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques,” Multimed. Tools Appl., vol. 83, no. 3, pp. 6829–6879, 2024, doi: 10.1007/s11042-023-15956-z.
  3. 3.
    R. Karthikeyan and R. Vadivel, “Proficient Dazzling Crow Optimization Routing Protocol (PDCORP) for Effective Energy Administration in Wireless Sensor Networks,” in IEEE International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023, 2023, pp. 1–6. doi: 10.1109/ELEXCOM58812.2023.10370559.
  4. 4.
    R. Karthikeyan and R. Vadivel, “Boosted Mutated Corona Virus Optimization Routing Protocol (BMCVORP) for Reliable Data Transmission with Efficient Energy Utilization,” Wirel. Pers. Commun., 2024, doi: 10.1007/s11277-024-11155-7.
  5. 5.
    Z. Sadreddini, E. Güler, M. Khalily, and H. Yanikomeroglu, “MRIRS: Mobile Ad Hoc Routing Assisted With Intelligent Reflecting Surfaces,” IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 4, pp. 1333–1346, 2021, doi: 10.1109/TCCN.2021.3084402.
  6. 6.
    A. K. Sangaiah et al., “Energy-Aware Geographic Routing for Real-Time Workforce Monitoring in Industrial Informatics,” IEEE Internet Things J., vol. 8, no. 12, pp. 9753–9762, 2021, doi: 10.1109/JIOT.2021.3056419.
  7. 7.
    S. Iqbal, A. Ahmed, M. Siraj, M. A. Tamimi, A. R. Bhangwar, and P. Kumar, “A Multi-Hop QoS-Aware and Predicting Link Quality Estimation (PLQE) Routing Protocol for Reliable WBSN,” IEEE Access, vol. 11, pp. 35993–36003, 2023, doi: 10.1109/ACCESS.2023.3266067.
  8. 8.
    M. T. Nuruzzaman and H.-W. Ferng, “Routing Protocol for a Heterogeneous MSN With an Intermittent Mobile Sink,” IEEE Sens. J., vol. 22, no. 22, pp. 22255–22263, 2022, doi: 10.1109/JSEN.2022.3212197.
  9. 9.
    S. Memon et al., “Enhanced Probabilistic Route Stability (EPRS) Protocol for Healthcare Applications of WBAN,” IEEE Access, vol. 11, pp. 4466–4477, 2023, doi: 10.1109/ACCESS.2023.3235837.
  10. 10.
    L. Mani, S. Arumugam, and R. Jaganathan, “Performance Enhancement of Wireless Sensor Network Using Feisty Particle Swarm Optimization Protocol,” ACM Int. Conf. Proceeding Ser., pp. 1–5, Dec. 2022, doi: 10.1145/3590837.3590907.
  11. 11.
    R. Jaganathan and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2021, doi: 10.12785/ijcds/100196.
  12. 12.
    J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
  13. 13.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  14. 14.
    G. Santhosh and K. V. Prasad, “Energy optimization routing for hierarchical cluster based WSN using artificial bee colony,” Meas. Sensors, vol. 29, p. 100848, 2023, doi: 10.1016/j.measen.2023.100848.
  15. 15.
    K. Sakthidasan @ Sankaran, X. Z. Gao, K. R. Devabalaji, and Y. Mohana Roopa, “Energy based random repeat trust computation approach and Reliable Fuzzy and Heuristic Ant Colony mechanism for improving QoS in WSN,” Energy Reports, vol. 7, pp. 7967–7976, Nov. 2021, doi: 10.1016/j.egyr.2021.08.121.
  16. 16.
    S. Yalç?n and E. Erdem, “TEO-MCRP: Thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5333–5348, 2022, doi: 10.1016/j.jksuci.2022.01.007.
  17. 17.
    P. Srividya and L. N. Devi, “An optimal cluster & trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN,” Glob. Transitions Proc., vol. 3, no. 1, pp. 317–325, 2022, doi: 10.1016/j.gltp.2022.03.018.
  18. 18.
    A. Ali et al., “Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks,” Ain Shams Eng. J., vol. 15, no. 2, p. 102356, 2024, doi: 10.1016/j.asej.2023.102356.
  19. 19.
    T. Vaiyapuri, V. S. Parvathy, V. Manikandan, N. Krishnaraj, D. Gupta, and K. Shankar, “A Novel Hybrid Optimization for Cluster?Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing,” Wirel. Pers. Commun., vol. 127, no. 1, pp. 39–62, 2022, doi: 10.1007/s11277-021-08088-w.
  20. 20.
    J. D. Abdulai, K. S. Adu-Manu, F. A. Katsriku, and F. Engmann, “A modified distance-based energy-aware (mDBEA) routing protocol in wireless sensor networks (WSNs),” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 8, pp. 10195–10217, 2023, doi: 10.1007/s12652-021-03683-y.
  21. 21.
    M. Hajiee, M. Fartash, and N. Osati Eraghi, “An Energy-Aware Trust and Opportunity Based Routing Algorithm in Wireless Sensor Networks Using Multipath Routes Technique,” Neural Process. Lett., vol. 53, no. 4, pp. 2829–2852, 2021, doi: 10.1007/s11063-021-10525-7.
  22. 22.
    H. Basumatary, A. Debnath, M. K. D. Barma, and B. K. Bhattacharyya, “Centroid-Based Routing protocol with moving sink node for uniform and non-uniform distribution of wireless sensor nodes,” J. Supercomput., vol. 77, no. 4, pp. 3727–3751, 2021, doi: 10.1007/s11227-020-03414-8.
  23. 23.
    N. Malisetti and V. K. Pamula, “Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm,” Microprocess. Microsyst., vol. 93, p. 104593, 2022, doi: 10.1016/j.micpro.2022.104593.
  24. 24.
    S. Chaurasia, K. Kumar, and N. Kumar, “MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs,” Ad Hoc Networks, vol. 141, p. 103079, Mar. 2023, doi: 10.1016/j.adhoc.2022.103079.
  25. 25.
    Z. Liu, Y. Zhang, and H. Peng, “Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics,” IET Smart Cities, vol. 5, no. 3, pp. 210–219, Sep. 2023, doi: 10.1049/smc2.12060.
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