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A novel technique with overhead in Multi-Path Network Aggregation by Machine Learning Framework (MPAA-MLF)

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Abstract

Wireless Sensor Networks consist of a collection of nodes that transmit data from source to destination to make effective communication. The transmission is initiated by several routing protocols, which help to identify the optimal route to reach the destination. However, the existing researchers face overhead issues such as delay, transmission reliability, bandwidth, and residual energy. The network overhead difficulties reduce the packet delivery ratio, throughput and maximize the packet drop rate. The research issues are overcome by applying the Multi-Path Network Aggregation by Machine Learning Framework. This algorithm is used on the 500*500 simulation region, and every node is examined in terms of residual energy, transmission rate, and distance. These factors were examined using the Monti consensus clustering and fuzzy inference machine learning system. The consensus algorithm identifies the cluster head, and fuzzy rules are utilized to select the supercluster heads. In addition, the candidate nodes are selected to form the cluster, which helps to transmit the data from source to destination. The vector function and probability value are computed to identify the network aggregated multipaths. This process reduces the transmission delay and improves the overall network performance. The discussed MPAA-MLF was implemented using the NS2 simulator, ensuring 98.53% of the packet delivery rate.

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References

  1. Liu, X., & Jie, Wu. (2019). A method for energy balance and data transmission optimal routing in wireless sensor networks. Sensors, 19(13), 3017.

    Article  Google Scholar 

  2. Maurya, S., Jain, V. K., & Chowdhury, D. R. (2019). Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network. Journal of Network and Computer Applications, 144, 118–137.

    Article  Google Scholar 

  3. Sadia, H. (2021). Water environment monitoring system based on wireless sensor network. Academic Journal of Environmental Biology, 2(1), 39–47.

    Google Scholar 

  4. Lilhore, U. K., Khalaf, O. I., Simaiya, S., Tavera Romero, C. A., Abdulsahib, G. M., & Kumar, D. (2022). A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks. International Journal of Distributed Sensor Networks, 18(9), 15501329221117118.

    Article  Google Scholar 

  5. Chan, L., Gomez Chavez, K., Rudolph, H., & Hourani, A. (2020). Hierarchical routing protocols for wireless sensor network: A compressive survey. Wireless Networks, 26, 3291–3314.

    Article  Google Scholar 

  6. Tang, L., Zhilin, Lu., & Fan, B. (2020). Energy efficient and reliable routing algorithms for wireless sensor networks. Applied Sciences, 10(5), 1885.

    Article  Google Scholar 

  7. Nguyen, T.-T., Pan, J.-S., & Dao, T.-K. (2019). A compact bat algorithm for unequal clustering in wireless sensor networks. Applied Sciences, 9(10), 1973.

    Article  Google Scholar 

  8. Landaluce, H., Arjona, L., Perallos, A., Falcone, F., Angulo, I., & Muralter, F. (2020). A review of IoT sensing applications and challenges using RFID and wireless sensor networks. Sensors, 20(9), 2495.

    Article  Google Scholar 

  9. Aliady, W. A., & Al-Ahmadi, S. A. (2019). Energy preserving secure measures against wormhole attack in wireless sensor networks. IEEE Access, 7, 84132–84141.

    Article  Google Scholar 

  10. Li, X., Keegan, B., Mtenzi, F., Weise, T., & Tan, M. (2019). Energy-efficient load balancing ant-based routing algorithm for wireless sensor networks. IEEE Access, 7, 113182–113196.

    Article  Google Scholar 

  11. Bhardwaj, V., Kaur, N., Vashisht, S., & Jain, S. (2021). SecRIP: Secure and reliable intercluster routing protocol for efficient data transmission in flying ad hoc networks. Transactions on Emerging Telecommunications Technologies, 32(6), e4068.

    Article  Google Scholar 

  12. Cvitić, I., Peraković, D., Periša, M., & Stojanović, M. D. (2021). Novel classification of IoT devices based on traffic flow features. Journal of Organizational and End User Computing (JOEUC), 33(6), 1–20. https://doi.org/10.4018/JOEUC.20211101.oa12

    Article  Google Scholar 

  13. Bangotra, D. K., Singh, Y., Selwal, A., Kumar, N., & Singh, P. K. (2022). A trust based secure intelligent opportunistic routing protocol for wireless sensor networks. Wireless Personal Communications, 127(2), 1045–1066.

    Article  Google Scholar 

  14. Wang, Z., Jiang, Z., Wang, Z., Tang, X., Liu, C., Yin, S., & Hu, Y. (2020). Enabling latency-aware data initialization for integrated CPU/GPU heterogeneous platform. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(11), 3433–3444.

    Article  Google Scholar 

  15. Haider, Z., Jamal, T., Asam, M., Butt, S., & Ajaz, A. (2020). Mitigation of wireless body area networks challenges using cooperation. International Journal of Security and Its Applications, 14(1), 15–30.

    Article  Google Scholar 

  16. Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4–19.

    Article  Google Scholar 

  17. Agarkhed, J., Kadrolli, V., & Patil, S. (2020). Fuzzy based multilevel multiconstraint multipath reliable routing in wireless sensor network. Int. j. inf. tecnol., 12, 1133–1146. https://doi.org/10.1007/s41870-020-00476-y

    Article  Google Scholar 

  18. Li, W., Zhang, H., Gao, S., Xue, C., Wang, X., & Sanglu, Lu. (2019). SmartCC: A reinforcement learning approach for multipath TCP congestion control in heterogeneous networks. IEEE Journal on Selected Areas in Communications, 37(11), 2621–2633.

    Article  Google Scholar 

  19. Jemili, I., Ghrab, D., Belghith, A., Mosbah, M., & Al-Ahmadi, S. (2021). Cross-layer multipath approach for critical traffic in duty-cycled wireless sensor networks. Journal of Network and Computer Applications, 191, 103154.

    Article  Google Scholar 

  20. Sreedevi, P., & Venkateswarlu, S. (2022). A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO. Pervasive and Mobile Computing, 85, 101642.

    Article  Google Scholar 

  21. Chiariotti, F., Kucera, S., Zanella, A., & Claussen, H. (2019). Analysis and design of a latency control protocol for multipath data delivery with predefined QoS guarantees. IEEE/ACM Transactions on Networking, 27(3), 1165–1178.

    Article  Google Scholar 

  22. Chen, G., Lu, Y., Li, B., et al. (2019). Mp-rdma: Enabling rdma for multipath transport in datacenters. IEEE/ACM Transactions on Networking, 27(6), 2308–2323.

    Article  Google Scholar 

  23. Liu, Q., Ke, F., Liu, Z., & Zeng, J. (2019). Loss-aware CMT-based multipathing scheme for efficient data delivery to heterogeneous wireless networks. International Journal of Digital Multimedia Broadcasting. https://doi.org/10.1155/2019/9474057

    Article  Google Scholar 

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  1. Xinxiang University, Xinxiang, 453003, Henan, China

    Xiangrong Li

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  1. Xiangrong Li
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Li, X. A novel technique with overhead in Multi-Path Network Aggregation by Machine Learning Framework (MPAA-MLF). Wireless Netw 29, 2833–2844 (2023). https://doi.org/10.1007/s11276-023-03364-y

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  • DOI: https://doi.org/10.1007/s11276-023-03364-y

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