Abstract:
Efficient and reliable clustering mechanisms play a crucial role in enhancing the energy management efficiency of underwater wireless sensor networks (UWSNs). In recent y...Show MoreMetadata
Abstract:
Efficient and reliable clustering mechanisms play a crucial role in enhancing the energy management efficiency of underwater wireless sensor networks (UWSNs). In recent years, game theory has been widely applied in clustering mechanisms for its ability to provide theoretical support in optimizing strategies. However, existing game theory-based clustering mechanisms only analyze the current cooperation and competition relationships of nodes in a single dimension, which limits the efficient energy utilization of the network. To address these limitations, this article proposes an adaptive energy-efficient clustering mechanism for UWSNs based on multidimensional game theory (MDGTC). During the candidate cluster head (C-CH) nodes selection, MDGTC enhances the opportunity of the potential optimal CH node to act as C-CH again by establishing a multidimensional clustering game model. Subsequently, an adaptive CH competition mechanism is introduced to further optimize the CH selection strategy by considering the energy and energy consumption status of local nodes and global networks. In addition, by combining a hierarchical architecture and a hybrid CH rotation mechanism, the stability of the proposed model is ensured, leading to a more balanced energy consumption among network nodes. In conclusion, MDGTC offers an effective distributed energy management architecture for UWSNs. The simulation results show that the MDGTC can achieve efficient energy utilization and prolong the network lifetime significantly.
Published in: IEEE Sensors Journal ( Volume: 24, Issue: 16, 15 August 2024)
Funding Agency:
References is not available for this document.
Contents Select All
1.
A. Boukerche, R. W. L. Coutinho, A. A. F. Loureiro, and L. F. M. Vieira, “Underwater wireless sensor networks: A new challenge for topology control-based systems,” Acm Comput. Surv., vol. 51, no. 1, pp. 1–36, 2018.
2.
X. Fu, Q. Pan, and X. Huang, “AoI-Energy-Aware collaborative data collection in UAV-enabled wireless powered sensor networks,” IEEE Sensors J., vol. 23, no. 24, pp. 31307–31324, Dec. 2023.
3.
X. Wei, H. Guo, X. Wang, X. Wang, and M. Qiu, “Reliable data collection techniques in underwater wireless sensor networks: A survey,” IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 404–431, 1st Quart., 2022.
4.
Y. Zhou, H. Yang, Y.-H. Hu, and S.-Y. Kung, “Cross-layer network lifetime maximization in underwater wireless sensor networks,” IEEE Syst. J., vol. 14, no. 1, pp. 220–231, Mar. 2020.
5.
R. Su, D. Zhang, C. Li, Z. Gong, R. Venkatesan, and F. Jiang, “Localization and data collection in AUV-aided underwater sensor networks: Challenges and opportunities,” IEEE Netw., vol. 33, no. 6, pp. 86–93, Nov./Dec. 2019.
6.
J. Luo, Y. Chen, M. Wu, and Y. Yang, “A survey of routing protocols for underwater wireless sensor networks,” IEEE Commun. Surveys Tuts., vol. 23, no. 1, pp. 137–160, 1st Quart., 2021.
7.
H. Khan, S. A. Hassan, and H. Jung, “On underwater wireless sensor networks routing protocols: A review,” IEEE Sensors J., vol. 20, no. 18, pp. 10371–10386, Sep. 2020.
8.
I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: Research challenges,” Ad Hoc Netw., vol. 3, no. 3, pp. 257–279, 2005.
9.
A. Khan, M. Imran, A. Alharbi, E. M. Mohamed, and M. M. Fouda, “Energy harvesting in underwater acoustic wireless sensor networks: Design, taxonomy, applications, challenges and future directions,” IEEE Access, vol. 10, pp. 134606–134622, 2022.
10.
S. M. M. H. Daneshvar and S. M. Mazinani, “On the best fitness function for the WSN lifetime maximization: A solution based on a modified salp swarm algorithm for centralized clustering and routing,” IEEE Trans. Netw. Service Manag., vol. 20, no. 4, pp. 4244–4254, Dec. 2023.
11.
N. Magaia, P. Ferreira, P. R. Pereira, K. Muhammad, J. Del Ser, and V. H. C. de Albuquerque, “Group'n route: An edge learning-based clustering and efficient routing scheme leveraging social strength for the Internet of Vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 10, pp. 19589–19601, Oct. 2022.
12.
X. Fu, G. Fortino, P. Pace, G. Aloi, and W. Li, “Environment-fusion multipath routing protocol for wireless sensor networks,” Inf. Fusion, vol. 53, pp. 4–19, Jan. 2020.
13.
C. Cooper, D. Franklin, M. Ros, F. Safaei, and M. Abolhasan, “A comparative survey of VANET clustering techniques,” IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 657–681, 1st Quart., 2017.
14.
M. Ozger, F. Alagoz, and O. B. Akan, “Clustering in multi-channel cognitive radio ad hoc and sensor networks,” IEEE Commun. Mag., vol. 56, no. 4, pp. 156–162, Apr. 2018.
15.
J.-S. Lee and H.-T. Jiang, “An extended hierarchical clustering approach to energy-harvesting mobile wireless sensor networks,” IEEE Internet Things J., vol. 8, no. 9, pp. 7105–7114, May 2021.
16.
V. Srivastava, “Using game theory to analyze wireless ad hoc networks,” IEEE Commun. Surveys Tuts., vol. 7, no. 4, pp. 46–56, 2005.
17.
B. Wang, Y. Wu, and K. J. R. Liu, “Game theory for cognitive radio networks: An overview,” Comput. Netw., vol. 54, no. 14, pp. 2537–2561, Oct. 2010.
18.
D. Muhammed, M. Anisi, M. Zareei, C. Vargas-Rosales, and A. Khan, “Game theory-based cooperation for underwater acoustic sensor networks: Taxonomy, review, research challenges and directions,” Sensors, vol. 18, no. 2, p. 425, Feb. 2018.
19.
G. Koltsidas and F.-N. Pavlidou, “A game theoretical approach to clustering of ad-hoc and sensor networks,” Telecommun. Syst., vol. 47, nos. 1–2, pp. 81–93, Jun. 2011.
20.
Q. Liu and M. Liu, “Energy-efficient clustering algorithm based on game theory for wireless sensor networks,” Int. J. Distrib. Sensor Netw., vol. 13, no. 11, Nov. 2017, Art. no. 155014771774370.
21.
G. Xing, “Game-theory-based clustering scheme for energy balancing in underwater acoustic sensor networks,” IEEE Internet Things J., vol. 8, no. 11, pp. 9005–9013, Jun. 2021.
22.
W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002.
23.
R. Hou, L. He, S. Hu, and J. Luo, “Energy-balanced unequal layering clustering in underwater acoustic sensor networks,” IEEE Access, vol. 6, pp. 39685–39691, 2018.
24.
W. Khan, H. Wang, M. S. Anwar, M. Ayaz, S. Ahmad, and I. Ullah, “A multi-layer cluster based energy efficient routing scheme for UWSNs,” IEEE Access, vol. 7, pp. 77398–77410, 2019.
25.
R. Hou, J. Fu, M. Dong, K. Ota, and D. Zeng, “An unequal clustering method based on particle swarm optimization in underwater acoustic sensor networks,” IEEE Internet Things J., vol. 9, no. 24, pp. 25027–25036, Dec. 2022.
26.
W. Zhang, J. Wang, G. Han, Y. Feng, and X. Tan, “A non-uniform clustering routing algorithm based on a virtual gravitational potential field in underwater acoustic sensor network,” IEEE Internet Things J., vol. 10, no. 15, pp. 13814–13825, Aug. 2023.
27.
D. Xie, Q. Sun, Q. Zhou, Y. Qiu, and X. Yuan, “An efficient clustering protocol for wireless sensor networks based on localized game theoretical approach,” Int. J. Distrib. Sensor Netw., vol. 9, no. 8, Aug. 2013, Art. no. 476313.
28.
L. Yang, Y.-Z. Lu, Y.-C. Zhong, X.-G. Wu, and S.-J. Xing, “A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks,” Wireless Netw., vol. 22, pp. 1007–1021, Apr. 2016.
29.
D. Lin and Q. Wang, “An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs,” IEEE Access, vol. 7, pp. 49894–49905, 2019.
30.
X. Yan, C. Huang, J. Gan, and X. Wu, “Game theory-based energy-efficient clustering algorithm for wireless sensor networks,” Sensors, vol. 22, no. 2, p. 478, Jan. 2022.
