Fuzzy Data Aggregation Approach to Enhance Energy-Efficient Routing Protocol for HWSNs
Abstract
The sensor nodes' computing capability, communication capabilities, and power supply are severely constrained in WSNs, making sensor battery replacement or recharging difficult or even impossible. Therefore, energy is an important challenge to consider while creating WSNs. In hazardous circumstances, accurate data aggregation and routing are crucial, and the energy consumption of sensors must be closely controlled. Due to environmental conditions and short-distance sensors, however, there is a high possibility of duplicating data. Large datasets include a range of data, some of which are helpful while others are entirely unnecessary. This redundancy reduces performance in terms of redundant transmission and computation expense. Data aggregation, on the other hand, may reduce duplicate data in a network, hence reducing the volume of data sent and increasing the network's lifespan. In this context, two novel energy-conscious approaches called Fuzzy Data Aggregation with Spider monkey optimization (FDA-SMORP) for data aggregation in the cluster head and routing to the sink are presented. These strategies attempt to offset the energy consumption among all nodes in a wireless network such that these nodes exhaust all of their energy and die almost simultaneously. To demonstrate the efficacy of the suggested approaches in terms of minimizing delay caused by route planning, balancing energy usage, and extending network lifespan, the proposed methods are compared to some of the most well-known WSN systems.
Full Text:
PDFReferences
C. Nakas, D. Kandris, and G. Visvardis, “Energy efficient routing in wireless sensor networks: A comprehensive survey,” Algorithms, vol. 13, no. 3, p. 72, 2020.
M. D. Aljubaily and I. S. Alshawi, “Energy sink-holes avoidance method based on fuzzy system in wireless sensor networks.,” Int. J. Electr. Comput. Eng., vol. 12, no. 2, 2022.
I. S. Alshawi, “Balancing Energy Consumption in Wireless Sensor Networks Using Fuzzy Artificial Bee Colony Routing Protocol,” Int. J. Manag. Inf. Technol., vol. 7, no. 2, pp. 1018–1032, 2013.
S. Kumar and S. Kumar, “Data aggregation using spatial and temporal data correlation,” 2015 1st Int. Conf. Futur. Trends Comput. Anal. Knowl. Manag. ABLAZE 2015, no. Ablaze, pp. 479–483, 2015, doi: 10.1109/ABLAZE.2015.7155043.
N. Nguyen, B. Liu, S. Chu, and H. Weng, “Challenges , Designs , and Performances of a Distributed Algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs,” IEEE Trans. Netw. Serv. Manag., vol. PP, no. c, p. 1, 2018, doi: 10.1109/TNSM.2018.2884445.
M. R. Choudhari and U. Rote, “Data Aggregation Approaches in WSNs,” 2021 Int. Conf. Comput. Commun. Informatics, ICCCI 2021, pp. 27–32, 2021, doi: 10.1109/ICCCI50826.2021.9402430.
A. Karaki, A. Nasser, C. A. Jaoude, and H. Harb, “An adaptive sampling technique for massive data collection in distributed sensor networks,” 2019 15th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2019, pp. 1255–1260, 2019, doi: 10.1109/IWCMC.2019.8766469.
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, 2020.
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, 2022, doi: 10.12928/TELKOMNIKA.v20i1.20984.
D. K. Altmemi and I. S. Alshaw, “Enhance Data Similarity Using a Fuzzy Approach,” J. Posit. Sch. Psychol., pp. 1898–1909, 2022.
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.
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, 2006, pp. 5-pp.
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, 2013, pp. 866–870.
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.
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.
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, 2017, pp. 263–277.
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.
A. H. Jabbar and I. S. Alshawi, “Spider monkey optimization routing protocol for wireless sensor networks.,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, 2021.
I. S. Alshawi, Z. A. Abbood, and A. A. Alhijaj, “Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol,” Telkomnika, vol. 20, no. 1, 2022.
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.
W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000, pp. 10-pp.
DOI: https://doi.org/10.31449/inf.v46i7.4272
This work is licensed under a Creative Commons Attribution 3.0 License.