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Energy Efficient and Delay Aware Optimization Reverse Routing Strategy for Forecasting Link Quality in Wireless Sensor Networks

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Abstract

Wireless Sensor Networks (WSNs) have a rapidly increasing number of applications due to the development of long-range low-powered wireless devices. Node decoupling for (NoRD) efficient power-supplying of nodes offers a evade network to avoid sleepy nodes (Chen and Pinkston in NoRD: Node-node decoupling for effective power-gating of on-chip nodes, in Intl, Symp, On Microarchitecture (MICRO), 2012). Though, it obtains a huge latency as well as restricted scalability. In addition, it enhances energy utilization. To defeat this problem, Energy Efficient and Delay Aware Optimization Reverse Routing Strategy (EEOS) is proposed for forecasting link quality in WSN. The main objective of this research is to design a Multi-hop Reverse Routing Technique in WSN. The reverse routing technique avoids the amount of retransmission. Forecasting link quality is used to measure the link quality by Estimating Communication Count (ECC), energy, and delay. This technique enhances routing, link stability, and energy efficiency and minimizes network congestion. It supports Quality of Service (QoS) necessities of energy control, traffic arrangement, and route allotment. In this scheme, the Signal-to-Interference and Noise Ratio (SINR) assists in measuring the quality of a wireless connection. In addition, the route link score is used to form the route from sender to receiver. The reverse routing also provides an efficient route. Simulation results prove that the EEOS minimizes both the delay and the energy utilization and increases the network throughput compared to the baseline protocol.

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References

  1. Chen, L., & Pinkston, T. (2012). NoRD: Node-node decoupling for effective power-gating of on-chip nodes, in Intl. On Microarchitecture (MICRO): Symp.

    Google Scholar 

  2. Yang, B., Xu, T. C., Säntti, T., &Plosila, J. (2010). Tree-model based mapping for energy-efficient and low-latency network-on-chip. In 13th IEEE Symposium on Design and Diagnostics of Electronic Circuits and Systems (pp. 189–192). IEEE.

  3. Kodi, A., Louri, A., & Wang, J. (2009). Design of energy-efficient channel buffers with node bypassing for network-on-chips (WSNs). In 2009 10th International Symposium on Quality Electronic Design (pp. 826–832). IEEE.

  4. Huang, P. T., Fang, W. L., Wang, Y. L., & Hwang, W. (2008,). Low power and reliable interconnection with self-corrected green coding scheme for network-on-chip. In Second ACM/IEEE International Symposium on Networks-on-Chip (WSNs 2008) (pp. 77–83). IEEE.

  5. Wang, Y., Liu, D., Qin, Z., & Shao, Z. (2013). “Optimally removing inter core communication overhead for streaming applications on MPSoCs”,IEEE Trans. Comput., 62(2), 336–350.

    MATH  Google Scholar 

  6. Panda, S. (2018). Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique. Op. Fiber. Technol., 42, 140–150.

    Article  Google Scholar 

  7. Kaur, T., & Kumar, D. (2020). A survey on QoS mechanisms in WSN for computational intelligence based routing protocols. Wirel. Netw, 26(4), 2465–2486.

    Article  Google Scholar 

  8. Pandey, A. C., Rajpoot, D. S., & Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search method. Inform. Process. Manag., 53(4), 764–779.

    Article  Google Scholar 

  9. Ahmed, S. H., Bouk, S. H., Yaqub, M. A., Kim, D., & Song, H. (2017). DIFS: Distributed interest forwarder selection in vehicular named data networks. IEEE Trans. Intell. Transp. Syst., 19(9), 3076–3080.

    Article  Google Scholar 

  10. Liu, F., Zhang, H., Chen, Y., Huang, Z., & Gu, H. (2017). Wavelength-reused hierarchical optical network on chip architecture for manycore processors. IEEE. Transact. Sustain. Comput, 4(2), 231–244.

    Article  Google Scholar 

  11. Wang, L., Wang, X., & Wang, Y. (2017). Abdtr: Approximation-based dynamic traffic regulation for networks-on-chip systems. In 2017 IEEE International Conference on Computer Design (ICCD) (pp. 153–160). IEEE

  12. Arvind, M., &Amrutur, B. (2008). Power reduction in on-chip interconnection network by serialization. In Proceeding of the 13th international symposium on Low power electronics and design (ISLPED'08) (pp. 201–204). IEEE.

  13. Bhardwaj, K., & Mane, P. S. (2014). C3Map and ARPSO based mapping algorithms for energy-efficient regular 3-D WSN architectures. In Technical papers of 2014 international symposium on VLSI design, automation and test (pp. 1–4). IEEE.

  14. Michelogiannakis, G., Sanchez, D., Dally, W. J., & Kozyrakis, C. (2010). Evaluating bufferless flow control for on-chip networks. In 2010 Fourth ACM/IEEE International Symposium on Networks-on-Chip (pp. 9–16). IEEE.

  15. He, Y., & Kondo, M. (2016). Opportunistic circuit-switching for energy efficient on-chip networks. In 2016 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC) (pp. 1–6). IEEE.

  16. Pan, Y., Kim, J., &Memik, G. (2010). Flexishare: Channel sharing for an energy-efficient nanophotonic crossbar. In HPCA-16 2010 The Sixteenth International Symposium on High-Performance Computer Architecture (pp. 1–12). IEEE.

  17. Kim, H., Tang, H., & Park, J. (2019). An Energy-efficient On-chip Learning Architecture for STDP based Sparse Coding. In 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED) (pp. 1–6). IEEE.

  18. Wei, H., Yu, J., Yu, H., Qin, M., & Gao, G. (2012). “Software pipelining for stream programs on resource constrained multicore architectures”,IEEE Trans. Parallel. Distrib. Syst., 23(12), 2338–2350.

    Article  Google Scholar 

  19. Nychis, G., Fallin, C., Moscibroda, T., Mutlu, O., & Seshan, S. (2012). On chip networks from a networking perspective: congestion and scalabilityin many-core interconnects”. ACM. Proc. SIGCOMM, 42(4), 407–418.

    Article  Google Scholar 

  20. Sendhilkumar, N. C. (2017). Design and Implementation of power efficient modified russian peasant multiplier using ripple carry adder. Int. J. MC Sq. Sci. Res., 9, 154–165.

    Google Scholar 

  21. KeerthiAnand, V. D. (2015). Power Efficient Multicast Opportunistic Routing Protocol To Optimize Lifetime of MANET. Int. J. MC Sq. Sci. Res., 7(1), 154–165.

    Google Scholar 

  22. Patel, S., & Pathak, H. (2022). A mathematical framework for link failure time estimation in MANETs. Eng. Sci. Technol. Int. J, 25, 1–13.

    Google Scholar 

  23. Mkongwa, K. G., Liu, Q., & Zhang, C. (2019). Link reliability and performance optimization in wireless body area networks. IEEE. Access., 7, 155392–155404.

    Article  Google Scholar 

  24. Maalej, M., Cherif, S., & Besbes, H. (2013). QoS and energy-aware cooperative routing protocol for wildfire monitoring wireless sensor networks. Sci. World. J., 2013, 1–11.

    Article  Google Scholar 

  25. Merline, G. (2015). Retrieval of dropped and modified packets by dynamic rerouting in wireless sensor networks. Int. J. Eng. Res. Technol., 4(2), 594–599.

    Google Scholar 

  26. Abkari, S., Mhamdi, J., & Abkari, A. H. (2021). Real-time RSS-based positioning system using neural network algorithm. Indone. J. Electr. Eng. Comput. Sci., 22(3), 1601–1610.

    Google Scholar 

  27. M. Y. Alnaham and I. A. M. Abdelrahaman, “Improving QoS in WSN based on an optimal path from source to sink node routing algorithm,” International Conference on Computer, Control, Electrical, and Electronics Engineering, pp. 1–6, 2018.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, India

    Guruva Reddy Elangovan

  2. Department of Computer Science and Engineering, Dr. MGR Educational and Research Institute, Chennai, India

    T. Kumanan

Authors
  1. Guruva Reddy Elangovan
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  2. T. Kumanan
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Contributions

Their individual scientific contributions to this paper: Elangovan:- Implementation and paper except delay aware routing. Kumanan:- Guided in Forecasting link quality technique and proof reading of the paper.

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Correspondence to Guruva Reddy Elangovan.

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Elangovan, G.R., Kumanan, T. Energy Efficient and Delay Aware Optimization Reverse Routing Strategy for Forecasting Link Quality in Wireless Sensor Networks. Wireless Pers Commun 128, 923–942 (2023). https://doi.org/10.1007/s11277-022-09982-7

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