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Game Theory-Based IoT Efficient Power Control in Cognitive UAV

Fadhil Mukhlif1,*, Norafida Ithnin1, Omar B. Abdulghafoor2, Faiz Alotaibi3, Nourah Saad Alotaibi4

1 Information Assurance and Security Research Group (IASRG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
2 Electronic and Telecommunication Department, College of Engineering, The American University of Kurdistan, Iraq
3 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia
4 Computer Department, Faculty of Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

* Corresponding Author: Fadhil Mukhlif. Email: email

Computers, Materials & Continua 2022, 72(1), 1561-1578.


With the help of network densification, network coverage as well as the throughput can be improved via ultra-dense networks (UDNs). In tandem, Unmanned Aerial Vehicle (UAV) communications have recently garnered much attention because of their high agility as well as widespread applications. In this paper, a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal. Further, the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation. The quality of service (QoS) related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’ net utility function. Moreover, an energy efficiency non-cooperative game theory power allocation with pricing scheme (EE-NGPAP) is proposed to obtain an efficient power control within IoT wireless nodes. Further, uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation. Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction, which is regarded to be apt with the 5G networks’ vision. Finally, the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.


Cite This Article

F. Mukhlif, N. Ithnin, O. B. Abdulghafoor, F. Alotaibi and N. Saad Alotaibi, "Game theory-based iot efficient power control in cognitive uav," Computers, Materials & Continua, vol. 72, no.1, pp. 1561–1578, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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