Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks
Abstract
Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes
Full Text:
PDFReferences
T. Kavitha and S. Silas, “A survey on energy harvesting routing protocol for WSN,” Proc. Int. Conf. Trends Electron. Informatics, ICOEI 2019, no. Icoei, pp. 572–575, 2019, doi: 10.1109/ICOEI.2019.8862780.
N. R. Roy and P. Chandra, “EEDAC-WSN : Energy Efficient Data Aggregation in Clustered WSN,” 2019 Int. Conf. Autom. Comput. Technol. Manag., pp. 586–592, 2019, doi:10.1109/ICACTM.2019.8776679.
J. Abdullah, M. K. Hussien, N. A. M. Alduais, M. I. Husni, and A. Jamil, “Data reduction algorithms based on computational intelligence for wireless sensor networks applications,” ISCAIE 2019 - 2019 IEEE Symp. Comput. Appl. Ind. Electron., pp. 166–171, 2019, doi: 10.1109/ISCAIE.2019.8743665.
W. Dargie and C. Poellabauer, "Fundamentals of Wireless Sensor Networks: Theory and Practice", 2010.
I. Ullah and H. Y. Youn, “A novel data aggregation scheme based on self-organized map for WSN,” J. Supercomput., vol. 75, no. 7, pp. 3975–3996, 2019, doi: 10.1007/s11227-018-2642-9.
T. Dao, T. Nguyen, J. Pan, Y. U. Qiao, and Q. Lai, “Identification Failure Data for Cluster Heads Aggregation in WSN based on Improving Classification of SVM,” IEEE Access, vol. PP, p. 1, 2020, doi: 10.1109/ACCESS.2020.2983219.
I. S. Alshawi, L. Yan, W. Pan, and B. Luo, "Fuzzy chessboard clustering and artificial bee colony routing method for energy-efficient heterogeneous wireless sensor networks," International Journal of Communication Systems , vol. 27, no. 12, pp. 3581- 3599, June 2014, doi: 10.1002/dac.2560.
W. K. Yun and S. J. Yoo, “Q-Learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks,” IEEE Access, vol. 9, pp. 10737–10750, 2021, doi: 10.1109/ACCESS.2021.3051360.
N. Chandnani and C. N. Khairnar, “Efficient Data Aggregation and Routing Algorithm for IoT Wireless Sensor Networks,” IFIP Int. Conf. Wirel. Opt. Commun. Networks, WOCN, vol. 2019-Decem, 2019, doi: 10.1109/WOCN45266.2019.8995074.
I. S. Alshawi, Z. A. Abbood, and A. A. Alhijaj, “Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol,” vol.20, no.1, pp.212–220, February 2022, doi: 10.12928/TELKOMNIKA.v20i1.20984.
I. S. Alshawi, A.-K. Y. Abdulla, and A. A. Alhijaj, “Fuzzy dstar-lite routing method for energy-efficient heterogeneous wireless sensor networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 2, pp. 1000–1010, August 2020, doi: 10.11591/ijeecs.v19.i2.pp906-916.
R. Misra and C. Mandal, “Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks,” in 2006 IFIP International Conference on Wireless and Optical Communications Networks, pp.5-pp, 2006, doi: 10.1109/WOCN.2006.1666600.
X. Shen, Z. Li, Z. Jiang, and Y. Zhan, “Distributed SVM classification with redundant data removing,” in 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 866–870, 2013, 10.1109/GreenCom-iThings-CPSCom.2013.152.
A. Muthu Krishnan and P. Ganesh Kumar, “An Effective Clustering Approach with Data Aggregation Using Multiple Mobile Sinks for Heterogeneous WSN,” Wirel. Pers. Commun., vol. 90, no. 2, pp. 423–434, 2016, doi: 10.1007/s11277-015-2998-6.
P. D. Ganjewar, S. Barani, and S. J. Wagh, “Data reduction using incremental Naive Bayes Prediction (INBP) in WSN,” Proc. - IEEE Int. Conf. Inf. Process. ICIP 2015, pp. 398–403, 2016, doi: 10.1109/INFOP.2015.7489415.
D. gan Zhang, T. Zhang, J. Zhang, Y. Dong, and X. dan Zhang, “A kind of effective data aggregating method based on compressive sensing for wireless sensor network,” Eurasip J. Wirel. Commun. Netw., vol. 2018, no. 1, 2018, doi: 10.1186/s13638-018-1176-4.
M. I. Adawy, S. A. Nor, and M. Mahmuddin, “Data redundancy reduction in wireless sensor network,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1–11, pp. 1–6, 2018.
H. Ramezanifar, M. Ghazvini, and M. Shojaei, “A new data aggregation approach for WSNs based on open pits mining,” Wirel. Networks, vol. 27, no. 1, pp. 41–53, 2021, doi: 10.1007/s11276-020-02442-9.
M. Pandey, L. K. Vishwakarma, and A. Bhagat, “An energy efficient clustering algorithm for increasing lifespan of heterogeneous wireless sensor networks,” in International Conference on Next Generation Computing Technologies, pp. 263–277, 2017, doi: 10.1007/978-981-10-
M. Al Mazaideh and J. Levendovszky, “A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm,”J. Commun. Networks,no.99, pp. 1–10, 2021,doi: 10.23919/JCN.2021.000003.
Jabbar, Ali H., and Imad S. Alshawi. "Spider monkey optimization routing protocol for wireless sensor networks." International Journal of Electrical & Computer Engineering (2088-8708), Vol. 11, No. 3, pp. 2432~2442, June. 2021, doi: 10.11591/ijece.v11i3.pp2432-2442.
O. Younis and S. Fahmy, “HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Trans. Mob. Comput., vol. 3, no. 4, pp. 366–379, October. 2004, doi: 10.1109/TMC.2004.41.
G. Sahar, K. A. Bakar, F. T. Zuhra, S. Rahim, T. Bibi, and S. H. H. Madni, “Data Redundancy Reduction for Energy-Efficiency in Wireless Sensor Networks: A Comprehensive Review,” IEEE Access, 2021, doi:
Zaboon, Khalid Hameed, Nagham Mumtaz Kudhair, and Imad S. Alshawi. "Fuzzy spider monkey optimization routing protocol to balance energy consumption in heterogeneous wireless sensor networks." Indonesian Journal of Electrical Engineering and Computer Science 29.2 (2023): 921-930.2." Informatica 46.7 (2022)..
Altmemi, Dhuha Kh, Abdulmalik Adil Abdulzahra, and Imad S. Alshawi. "A new approach based on intelligent method to classify quality of service." Informatica 46.9 (2022)..
Kheerallah, Yousif Abdulwahab, and Jawad Alkenani. "A new method based on machine learning to increase efficiency in wireless sensor networks." Informatica 46.9 (2023).
DOI: https://doi.org/10.31449/inf.v48i20.6768
This work is licensed under a Creative Commons Attribution 3.0 License.