PDF (1.7 MB)
Collect
Submit Manuscript
Article | Open Access

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

Syed Kamran Haider1,2Abbas Ahmed2Noman Mujeeb Khan2Ali Nauman3()Sung Won Kim3()
College of Internet of Things (IoT) Engineering, Hohai University, Changzhou, 213001, China
Department of Electrical and Electronics Engineering, Beaconhouse International College, Islamabad, 44000, Pakistan
School of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38428, Republic of Korea
Show Author Information

Abstract

In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm for inter-cluster dialogue, our framework orchestrates CHs into an efficient communication chain. Through comparative analysis, the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing (CCMAR) and the energy-efficient cluster-based dynamic algorithms (ECCRA). Specifically, it showcases an impressive 15% increase in energy conservation and a 20% reduction in data transmission time, highlighting its advanced performance. Furthermore, this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN, emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

References

[1]

M. Nabati, M. Maadani, and M. A. Pourmina, “AGEN-AODV: An intelligent energy-aware routing protocol for heterogeneous mobile ad-hoc networks,” Mob. Netw. Appl., vol. 27, no. 2, pp. 576–587, 2022. doi: 10.1007/s11036-021-01821-6.

[2]

M. Samir, S. Sharafeddine, C. M. Assi, T. M. Nguyen, and A. Ghrayeb, “UAV trajectory planning for data collection from time-constrained IoT devices,” IEEE Trans. Wirel. Commun., vol. 32, no. 1, pp. 34–46, 2024.

[3]

R. Dogra, S. Rani, H. Babbar, and D. Krah, “Energy-efficient routing protocol for next-generation application in the Internet of Things and wireless sensor networks,” Wirel. Commun. Mob. Comput., vol. 2022, no. 1, pp. 1–10, 2022. doi: 10.1155/2022/8006751.

[4]

M. A. Jamshed, K. Ali, Q. H. Abbasi, M. A. Imran, and M. Ur-Rehman, “Challenges, applications, and future of wireless sensors in Internet of Things: A review,” IEEE Sens. J., vol. 22, no. 6, pp. 5482–5494, 2022. doi: 10.1109/JSEN.2022.3148128.

[5]

T. Rault, A. Bouabdallah, and Y. Challal, “Energy efficiency in wireless sensor networks: A top-down survey,” Comput. Netw., vol. 67, no. 1, pp. 104–122, 2014. doi: 10.1016/j.comnet.2014.03.027.

[6]

J. Wang, Y. Gao, W. Liu, A. K. Sangaiah, and H. -J. Kim, “Energy efficient routing algorithm with mobile sink support for wireless sensor networks,” Sensors, vol. 19, no. 7, pp. 1494, 2019. doi: 10.3390/s19071494.

[7]

S. Poudel and S. Moh, “Medium access control protocols for unmanned aerial vehicle-aided wireless sensor networks: A survey,” IEEE Access, vol. 7, no. 7, pp. 65728–65744, 2019. doi: 10.1109/ACCESS.2019.2917948.

[8]

A. Ranjha and G. Kaddoum, “URLLC facilitated by mobile UAV relay and RIS: A joint design of passive beamforming, block length, and UAV positioning,” IEEE Internet Things J., vol. 8, no. 6, pp. 4618–4627, 2020.

[9]

S. Kumar, P. R. Gautam, T. Rashid, A. Verma, and A. Kumar, “Division algorithm-based energy-efficient routing in wireless sensor networks,” Wireless Pers. Commun., vol. 122, no. 3, pp. 2335–2354, 2022.

[10]

L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UAV communication networks,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 1123–1152, 2016. doi: 10.1109/COMST.2015.2495297.

[11]
S. K. Haider, M. A. Jamshed, A. Jiang, and H. Pervaiz, “An energy efficient cluster-heads re-usability mechanism for wireless sensor networks,” in 2019 IEEE Int. Conf. Commun. Workshops (ICC Workshops), Shanghai, China, 2019, pp. 1–6.
[12]

P. A. Neves and J. J. P. C. Rodrigues, “Internet protocol over wireless sensor networks, from myth to reality,” J. Commun., vol. 5, pp. 189–196, 2010. doi: 10.4304/jcm.5.3.189-196.

[13]
A. Razaque, M. Abdulgader, C. Joshi, F. Amsaad, and M. Chauhan, “P-LEACH: Energy efficient routing protocol for Wireless Sensor Networks,” in Proc. 2016 IEEE Long Island Syst., Appl. Technol. Conf. (LISAT), Farmingdale, NY, USA, 2016, pp. 1–5.
[14]

M. A. Jamshed, F. Héliot, and T. W. C. Brown, “A survey on electromagnetic risk assessment and evaluation mechanism for future wireless communication systems,” IEEE J. Electromagn., RF Microw. Med. Biol., vol. 4, no. 1, pp. 24–36, 2020. doi: 10.1109/JERM.2019.2917766.

[15]
S. K. Haider, M. A. Jamshed, A. Jiang, H. Pervaiz, and Q. Ni, “UAV-assisted cluster-head selection mechanism for wireless sensor network applications,” in 2019 UK/China Emerg. Technol. (UCET), Glasgow, UK, 2019, pp. 1–2.
[16]

T. Pal, R. Saha, and S. Biswas, “Sink mobility-based energy efficient routing algorithm variants in WSN,” Int. J. Wirel. Inf. Netw., vol. 29, no. 3, pp. 373–392, 2022. doi: 10.1007/s10776-022-00557-8.

[17]

C. Zhu, S. Wu, G. Han, L. Shu, and H. Wu, “A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink,” IEEE Access, vol. 3, no. 1, pp. 381–396, 2015. doi: 10.1109/ACCESS.2015.2424452.

[18]

D. Popescu, C. Dragana, F. Stoican, L. Ichim, and G. Stamatescu, “A collaborative UAV-WSN network for monitoring large areas,” Sensors, vol. 18, no. 12, pp. 4202, 2018. doi: 10.3390/s18124202.

[19]

R. Velmani and B. Kaarthick, “An efficient cluster-tree based data collection scheme for large mobile wireless sensor networks,” IEEE Sens. J., vol. 15, no. 4, pp. 2377–2390, 2015. doi: 10.1109/JSEN.2014.2377200.

[20]

S. Sasirekha and S. Swamynathan, “Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network,” J. Commun. Netw., vol. 19, no. 4, pp. 392–401, 2017. doi: 10.1109/JCN.2017.000063.

[21]

Z. Wang, R. Liu, Q. Liu, J. S. Thompson, and M. Kadoch, “Energy-efficient data collection and device positioning in UAV-assisted IoT,” IEEE Internet Things J., vol. 11, no. 2, pp. 1122–1139, 2024.

[22]

R. Ramya and T. Brindha, “A comprehensive review on optimal cluster head selection in WSN-IoT,” Adv. Eng. Softw., vol. 171, no. 1, pp. 103170, 2022. doi: 10.1016/j.advengsoft.2022.103170.

[23]

D. Ebrahimi, S. Sharafeddine, P. -H. Ho, and C. Assi, “UAV-aided projection-based compressive data gathering in wireless sensor networks,” IEEE Internet Things J., vol. 6, no. 2, pp. 1893–1905, 2019. doi: 10.1109/JIOT.2018.2878834.

[24]
M. A. Jamshed, W. U. Khan, H. Pervaiz, M. A. Imran, and M. Ur-Rehman, “Emission-aware resource optimization framework for backscatter-enabled uplink NOMA networks,” in 2022 IEEE 95th Veh. Technol. Conf. (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1–5.
[25]
M. C. M. Thein and T. Thein, “An energy efficient cluster-head selection for wireless sensor networks,” in Proc. 2010 Int. Conf. Intell. Syst., Modell. Simul., Liverpool, UK, 2010, pp. 287–291.
[26]

W. Zhou, “Energy efficient clustering algorithm based on neighbors for wireless sensor networks,” (in Chinese), J. Shanghai Univ., vol. 15, no. 2, pp. 150–153. 2011. doi: 10.1007/s11741-011-0712-1.

[27]
H. M. Abdulsalam and L. K. Kamel, “W-LEACH: Weighted low energy adaptive clustering hierarchy aggregation algorithm for data streams in wireless sensor networks,” in IEEE Int. Conf. Data Min. Workshops, Sydney, NSW, Australia, 2010, pp. 1–8.
[28]

J. Baek, S. I. Han, and Y. Han, “Energy-efficient UAV routing for wireless sensor networks,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1741–1750, 2019. doi: 10.1109/TVT.2019.2959808.

[29]

T. Kim and D. Qiao, “Energy-efficient data collection for IoT networks via cooperative multi-hop UAV networks,” IEEE Trans. Vehicular Technol., vol. 69, no. 11, pp. 13796–13811, 2020. doi: 10.1109/TVT.2020.3027920.

[30]

Z. Ullah, F. Al-Turjman, and L. Mostarda, “Cognition in UAV-Aided 5G and beyond communications: A survey,” IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 3, pp. 872–891, 2020. doi: 10.1109/TCCN.2020.2968311.

[31]

J. Gu, T. Su, Q. Wang, X. Du, and M. Guizani, “Multiple moving targets surveillance based on a cooperative network for multi-UAV,” IEEE Commun. Mag., vol. 62, no. 4, pp. 82–89, 2024.

[32]

Z. Ding, L. Shen, H. Chen, F. Yan, and N. Ansari, “Energy-efficient relay-selection-based dynamic routing algorithm for IoT-oriented software-defined WSNs,” IEEE Internet Things J., vol. 7, no. 9, pp. 9050–9065, 2020. doi: 10.1109/JIOT.2020.3002233.

[33]

Z. Liu, Y. Cao, P. Gao, X. Hua, D. Zhang and T. Jiang, “Multi-UAV network assisted intelligent edge computing: Challenges and opportunities,” China Commun., vol. 31, no. 3, pp. 258–278, 2024.

[34]

B. Zhu, E. Bedeer, H. H. Nguyen, R. Barton, and J. Henry, “Joint cluster head selection and trajectory planning in UAV-aided IoT networks by reinforcement learning with sequential model,” IEEE Internet Things J., vol. 12, no. 14, pp. 12071–12084, 2024.

[35]

M. M. Azari, F. Rosas, and S. Pollin, “Cellular connectivity for UAVs: Network modeling, performance analysis, and design guidelines,” IEEE Trans. Wirel. Commun., vol. 18, no. 7, pp. 3366–3381, 2019. doi: 10.1109/TWC.2019.2910112.

[36]

P. Chithaluru, S. Kumar, A. Singh, A. Benslimane, and S. K. Jangir, “An energy-efficient routing scheduling based on fuzzy ranking scheme for Internet of Things,” IEEE Internet Things J., vol. 9, no. 10, pp. 7251–7260, 2022. doi: 10.1109/JIOT.2021.3098430.

[37]

S. Sotheara, I. Hikari, L. Jiang, and S. Shimamoto, “Priority-based data gathering framework in UAV-assisted wireless sensor networks,” IEEE Sens. J., vol. 16, no. 14, pp. 5785–5794, 2016. doi: 10.1109/JSEN.2016.2568260.

[38]

G. Arya, A. Bagwari, and D. S. Chauhan, “Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN communication,” IEEE Access, vol. 10, no. 1, pp. 9340–9356, 2022. doi: 10.1109/ACCESS.2022.3142082.

[39]

M. Mir, M. Yaghoobi, and M. Khairabad, “A new approach to energy-aware routing in the Internet of Things using improved grasshopper metaheuristic algorithm with Chaos theory and fuzzy logic,” Multimed. Tools Appl., vol. 82, no. 4, pp. 5133–5159, 2023. doi: 10.1007/s11042-021-11841-9.

[40]

S. Verma and N. S. Mitra, “An efficient routing approach to improve the performance of IoT node for 5G communication applications,” Int. J. Innov. Res. Sci., Eng. Technol. (IJIRSET), vol. 11, no. 4, pp. 3746–3750, 2022.

[41]

S. K. Haider et al., “Energy efficient UAV flight path model for cluster head selection in next-generation wireless sensor networks,” Sensors, vol. 21, no. 24, pp. 1424–1436, 2021. doi: 10.3390/s21248445.

[42]

S. K. Haider, A. Nauman, M. A. Jamshed, A. Jiang, S. Batool and S. W. Kim, “Internet of drones: Routing algorithms, techniques and challenges,” Mathematics, vol. 10, no. 9, pp. 1488–1510, 2022. doi: 10.3390/math10091488.

Computers, Materials & Continua
Pages 4085-4110
Cite this article:
Haider SK, Ahmed A, Khan NM, et al. AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance. Computers, Materials & Continua, 2024, 80(3): 4085-4110. https://doi.org/10.32604/cmc.2024.052997
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return