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An efficient Markov energy predictor for software defined wireless sensor networks

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Abstract

Software-defined wireless sensor networks (SDWSNs) are a great step forward to centralize and facilitate the management of low-power networks. However, the continuous sending of local information from the sensor nodes to the controller causes excessive energy loss and increases the network overhead. This dilemma has become one of the hot topics of current research to make SDWSNs more efficient. This paper has proposed an efficient energy predictor for SDWSNs (E2-SDWSN) to improve the performance of the sensor network. The proposed approach reduces the cost of packet delivery and energy consumption using the well-known MapReduce framework with an intelligent selection of the reducers. Using MapReduce in the SDWSN by in-network processing significantly reduces energy consumption and balances traffic. Furthermore, the controller using an energy prediction model predicts the residual energy level of the nodes. Therefore, assuming the position of the nodes is fixed, the need to sequential send local packets is reduced. Reducing the number of local and energy information packets of the network decreases the network traffic. This consequently improves the quality of service and reduces energy consumption. Therefore, the proposed model increases the lifetime of the network. The results also confirm that the proposed E2-SDWSN significantly improves the energy throughput, the packet delivery ratio, and the delay through the sensor network.

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References

  1. Hrbček, J., Božek, P., Svetlík, J., Šimák, V., Hruboš, M., Nemec, D., Janota, A., & Bubeníková, E. (2017). Control system for the haptic paddle used in mobile robotics. International Journal of Advanced Robotic Systems, 14(5), 1–11. https://doi.org/10.1177/1729881417737039

    Article  Google Scholar 

  2. Hossain, M. S., Muhammad, G., & Alamri, A. (2019). Smart healthcare monitoring: A voice pathology detection paradigm for smart cities. Multimedia Systems, 25(5), 565–575. https://doi.org/10.1007/s00530-017-0561-x

    Article  Google Scholar 

  3. GK, J. S., & Jasper, J. (2020). MANFIS based SMART home energy management system to support SMART grid. Peer-to-Peer Networking and Applications, 5, 1–12. https://doi.org/10.1007/s12083-020-00884-8

    Google Scholar 

  4. Pirník, R., Hruboš, M., Nemec, D., Mravec, T., & Božek, P. (2015). Integration of inertial sensor data into control of the mobile platform. In Federated Conference on Software Development and Object Technologies (pp. 271–282). Springer. https://doi.org/10.1007/978-3-319-46535-7_21

  5. Özdemir, V. (2020). Genomics, the internet of things, artificial intelligence, and society. Applied Genomics and Public Health. https://doi.org/10.1016/b978-0-12-813695-9.00015-7

    Article  Google Scholar 

  6. Milardo, S., Tinnirello, C. M., & Palazzo, C. M. (2018). A software defined approach to the internet of things: From wireless sensor networks to network operating systems, PhD thesis.

  7. Xu, F., Ye, H., Yang, F., & Zhao, C. (2019). Software defined mission-critical wireless sensor network: Architecture and edge offloading strategy. IEEE Access, 7(c), 10383–10391. https://doi.org/10.1109/ACCESS.2019.2890854

    Article  Google Scholar 

  8. Gungor, V. C. (2008). Efficient available energy monitoring in wireless sensor networks. International Journal of Sensor Networks, 3(1), 25–32. https://doi.org/10.1504/IJSNet.2008.016459

    Article  Google Scholar 

  9. Chang, C.-Y., Lin, C.-C., Shang, C., Chang, I.-H., & Roy, D. S. (2019). DBDC: A distributed bus-based data collection mechanism for maximizing throughput and lifetime in WSNs. IEEE Access, 7, 160506–160522. https://doi.org/10.1109/ACCESS.2019.2949569

    Article  Google Scholar 

  10. Pivarčiová, E., Božek, P., Turygin, Y., Zajačko, I., Shchenyatsky, A., Václav, Š, Císar, M., & Gemela, B. (2018). Analysis of control and correction options of mobile robot trajectory by an inertial navigation system. International Journal of Advanced Robotic Systems, 15(1), 1–15. https://doi.org/10.1177/1729881418755165

    Article  Google Scholar 

  11. Galluccio, L., Milardo, S., Morabito, G., & Palazzo, S. (2015). SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks. In Computer Communications (INFOCOM), 2015 IEEE Conference on (pp. 513–521), IEEE. Retrieved from https://doi.org/10.1109/INFOCOM.2015.7218418

  12. Bukar, U. A., & Othman, M. (2021). Architectural design, improvement, and challenges of distributed software-defined wireless sensor networks. Wireless Personal Communications, 122(3), 2395–2439.

    Article  Google Scholar 

  13. Hawbani, A., Wang, X., Zhao, L., Al-Dubai, A., Min, G., & Busaileh, O. (2020). Novel architecture and heuristic algorithms for software-defined wireless sensor networks. IEEE/ACM Transactions on Networking, 28(6), 2809–2822.

    Article  Google Scholar 

  14. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113. https://doi.org/10.1145/1327452.1327492

    Article  Google Scholar 

  15. Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of the ACM, 43(5), 51–58. https://doi.org/10.1145/332833.332838

    Article  Google Scholar 

  16. Anadiotis, A. C. G., Morabito, G., & Palazzo, S. (2016). An SDN-assisted framework for optimal deployment of MapReduce functions in WSNs. IEEE Transactions on Mobile Computing, 15(9), 2165–2178. https://doi.org/10.1109/TMC.2015.2496582

    Article  Google Scholar 

  17. Mini, R. A. F., Do Val Machado, M., Loureiro, A. A. F., & Nath, B. (2005). Prediction-based energy map for wireless sensor networks. Ad Hoc Networks, 3(2), 235–253. https://doi.org/10.1016/j.adhoc.2004.07.008

    Article  Google Scholar 

  18. Dias, G. M., Bellalta, B., & Oechsner, S. (2017). The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks. Computer Communications, 112, 58–72. https://doi.org/10.1016/j.comcom.2017.08.002

    Article  Google Scholar 

  19. Ghidini, G., & Das, S. K. (2011). An energy-efficient markov chain-based randomized duty cycling scheme for wireless sensor networks. In 2011 31st International Conference on Distributed Computing Systems (pp. 67–76). IEEE. Doi: https://doi.org/10.1109/ICDCS.2011.86

  20. Kang, H., Li, X., & Moran, P. J. (2007). Power-aware markov chain based tracking approach for wireless sensor networks. In IEEE Wireless Communications and Networking Conference, WCNC, (pp. 4212–4217). Doi: https://doi.org/10.1109/WCNC.2007.769

  21. Zhao, Y. J., Govindan, R., & Estrin, D. (2002). Residual energy scan for monitoring sensor networks. In 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No. 02TH8609) (Vol. 1, pp. 356–362). IEEE. Retrieved from https://doi.org/10.1109/WCNC.2002.993521

  22. Gillick, D., Faria, A., & DeNero, J. (2006). Mapreduce: Distributed computing for machine learning. Berkley, Dec, 18. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.9204

  23. Zaharia, M., Konwinski, A., Joseph, A. D., Katz, R. H., & Stoica, I. (2008). Improving MapReduce performance in heterogeneous environments. In Osdi (Vol. 8, p. 7). Retrieved from https://static.usenix.org/event/osdi08/tech/full_papers/zaharia/zaharia.pdf

  24. Chen, J., Low, K. H., Tan, C. K. Y., Oran, A., Jaillet, P., Dolan, J., & Sukhatme, G. (2012). Decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena. In Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012, (pp. 163–173). Retrieved from https://arxiv.org/abs/1206.6230

  25. Wang, Q., Lee, B., Murray, N., & Qiao, Y. (2019). MR-Edge: A MapReduce-based protocol for IoT Edge computing with resource constraints. In 2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019, (pp. 1–6). Doi: https://doi.org/10.1109/CCNC.2019.8651855

  26. Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 171–180). ACM. Retrieved from https://dl.acm.org/doi/abs/https://doi.org/10.1145/958491.958512

  27. Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies (Vol. 3, pp. 1567–1576). IEEE. Retrieved from https://doi.org/10.1109/INFCOM.2002.1019408

  28. Shukla, A., & Tripathi, S. (2020). A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network. Wireless Networks. https://doi.org/10.1007/s11276-020-02277-4

    Article  Google Scholar 

  29. Toor, A. S., & Jain, A. K. (2019). Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks. AEU-International Journal of Electronics and Communications, 102, 41–53. https://doi.org/10.1016/j.aeue.2019.02.006

    Google Scholar 

  30. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 56–67). ACM. Retrieved from https://dl.acm.org/doi/abs/https://doi.org/10.1145/345910.345920

  31. Montoya, G. A., & Donoso, Y. (2019). A prediction algorithm based on Markov Chains for finding the minimum cost path in a mobile WSNs. International Journal of Computers, Communications and Control, 14(1), 39–55. https://doi.org/10.15837/ijccc.2019.1.3487

    Article  Google Scholar 

  32. Mini, R. A. F., Nath, B., & Loureiro, A. A. F. (2002). A probabilistic approach to predict the energy consumption in wireless sensor networks. In IV Workshop de Comunicao sem Fio e Computao Mvel (pp. 23–25). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.4906

  33. Hadaidian Nejad Yousefi, H., Seifi Kavian, Y., & Mahmoudi, A. (2019). A markov model for investigating the impact of IEEE802.15.4 MAC layer parameters and number of clusters on the performance of wireless sensor networks. Wireless Networks, 25(7), 4415–4430. https://doi.org/10.1007/s11276-019-02105-4

    Article  Google Scholar 

  34. Ram, M., Kumar, S., Kumar, V., Sikandar, A., & Kharel, R. (2019). Enabling green wireless sensor networks: Energy efficient T-MAC using Markov chain based optimization. Electronics, 8(5), 534. https://doi.org/10.3390/electronics8050534

    Article  Google Scholar 

  35. Wang, R., Zhang, Z., Zhang, Z., & Jia, Z. (2018). ETMRM: An energy-efficient trust management and routing mechanism for SDWSNs. Computer Networks, 139, 119–135. https://doi.org/10.1016/j.comnet.2018.04.009

    Article  Google Scholar 

  36. Xiang, W., Wang, N., & Zhou, Y. (2016). An energy-efficient routing algorithm for software-defined wireless sensor networks. IEEE Sensors Journal, 16(20), 7393–7400. https://doi.org/10.1109/JSEN.2016.2585019

    Article  Google Scholar 

  37. Choi, Y., Choi, Y., & Hong, Y.-G. (2016). Study on coupling of software-defined networking and wireless sensor networks. In 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 900–902). IEEE. Doi: https://doi.org/10.1109/ICUFN.2016.7536926

  38. Jimenez, J. M., Romero, O., Lloret, J., & Diaz, J. R. (2019). Energy savings consumption on public wireless networks by sdn management. Mobile Networks and Applications, 24(2), 667–677.

    Article  Google Scholar 

  39. Duan, Y., Li, W., Fu, X., Luo, Y., & Yang, L. (2017). A methodology for reliability of WSN based on software defined network in adaptive industrial environment. IEEE/CAA Journal of Automatica Sinica, 5(1), 74–82. https://doi.org/10.1109/JAS.2017.7510751

    Article  Google Scholar 

  40. Din, S., Paul, A., Ahmad, A., & Kim, J. H. (2019). Energy efficient topology management scheme based on clustering technique for software defined wireless sensor network. Peer-to-Peer Networking and Applications, 12(2), 348–356.

    Article  Google Scholar 

  41. Wang, Y., Chen, H., Wu, X., & Shu, L. (2016). An energy-efficient SDN based sleep scheduling algorithm for WSNs. Journal of Network and Computer Applications, 59, 39–45. https://doi.org/10.1016/j.jnca.2015.05.002

    Article  Google Scholar 

  42. Tomovic, S., & Radusinovic, I. (2015). Performance analysis of a new SDN-based WSN architecture. In Telecommunications Forum Telfor (TELFOR), 2015 23rd (pp. 99–102). IEEE. Doi: https://doi.org/10.1109/TELFOR.2015.7377423

  43. Younus, M. U., Khan, M. K., & Bhatti, A. R. (2021). Improving the software defined wireless sensor networks routing performance using reinforcement learning. IEEE Internet of Things Journal, 9(5), 3495–3508.

    Article  Google Scholar 

  44. Younus, M. U., Islam, S. U., & Kim, S. W. (2019). Proposition and real-time implementation of an energy-aware routing protocol for a software defined wireless sensor network. Sensors (Basel, Switzerland). https://doi.org/10.3390/s19122739

    Article  Google Scholar 

  45. Rahimifar, A., Seifi Kavian, Y., Kaabi, H., & Soroosh, M. (2020). Predicting the energy consumption in software defined wireless sensor networks: A probabilistic Markov model approach. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02599-3

    Google Scholar 

  46. Luo, T., Tan, H. P., & Quek, T. Q. S. (2012). Sensor OpenFlow: Enabling software-defined wireless sensor networks. IEEE Communications Letters, 16(11), 1896–1899. https://doi.org/10.1109/LCOMM.2012.092812.121712

    Article  Google Scholar 

  47. Gardiner, C. (2009). Stochastic Methods (Vol 4). Springer.

    Google Scholar 

  48. Han, Z., & Ren, W. (2014). A novel wireless sensor networks structure based on the SDN. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2014/874047

    Google Scholar 

  49. Dunkels, A. (2006). The contiki operating system. Web page. Visited Oct, 24.

  50. Sehgal, A. (2013). Using the contiki cooja simulator. Computer Science, Jacobs University Bremen Campus Ring, 1, 28759.

    Google Scholar 

  51. Fan, C., Xiao, F., & Wang, S. (2014). Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Applied Energy, 127, 1–10. https://doi.org/10.1016/j.apenergy.2014.04.016

    Article  Google Scholar 

  52. González-Vidal, A., Ramallo-González, A. P., Terroso-Sáenz, F., & Skarmeta, A. (2017). Data driven modeling for energy consumption prediction in smart buildings. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4562–4569), IEEE. Doi: https://doi.org/10.1109/BigData.2017.8258499

  53. Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49, 591–603. https://doi.org/10.1016/j.enbuild.2012.03.010

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by Shahid Chamran University of Ahvaz under Grant Number 98/3/05/14909.

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  1. Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Atefeh Rahimifar, Yousef Seifi Kavian, Hooman Kaabi & Mohammad Soroosh

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  1. Atefeh Rahimifar
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  2. Yousef Seifi Kavian
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Correspondence to Yousef Seifi Kavian.

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Rahimifar, A., Seifi Kavian, Y., Kaabi, H. et al. An efficient Markov energy predictor for software defined wireless sensor networks. Wireless Netw 28, 3391–3409 (2022). https://doi.org/10.1007/s11276-022-03058-x

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