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Ding, X.; Tian, W.; Liu, G.; Ji, X. Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks. Encyclopedia. Available online: https://encyclopedia.pub/entry/52444 (accessed on 21 December 2024).
Ding X, Tian W, Liu G, Ji X. Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks. Encyclopedia. Available at: https://encyclopedia.pub/entry/52444. Accessed December 21, 2024.
Ding, Xufei, Wen Tian, Guangjie Liu, Xiaopeng Ji. "Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks" Encyclopedia, https://encyclopedia.pub/entry/52444 (accessed December 21, 2024).
Ding, X., Tian, W., Liu, G., & Ji, X. (2023, December 06). Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks. In Encyclopedia. https://encyclopedia.pub/entry/52444
Ding, Xufei, et al. "Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks." Encyclopedia. Web. 06 December, 2023.
Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks
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This entry is adapted from the peer-reviewed paper 10.3390/s23239411

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. The UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. 

unmanned aerial vehicle (UAV) wireless sensor network (WSN) data collection

1. Introduction

Wireless sensor networks (WSNs), owing to their decentralized control and freeform arrangement, have become prevalent across various applications, including intelligent living, weather monitoring, and health tracking [1][2]. While in regions with sturdy network infrastructure, WSNs can effortlessly link up to the internet and transmit data to the collector [3], in far-flung and inconvenient areas like deserts and plateaus where base stations are not readily deployable, WSNs confront insurmountable hurdles in direct communication with the fusion center [4]. Against this backdrop, unmanned aerial vehicles (UAVs) are emerging as a feasible choice for mobile data collectors for WSNs. Thanks to their pliable deployment and user-friendly control, UAVs can effectively overcome the communication gap and provide a reliable mechanism for WSNs in remote locations [5]. In summary, wireless sensor networks, despite their many merits, are limited in their application in regions where network infrastructure is weak or nonexistent. Fortunately, the deployment of UAVs as mobile data collection tools for WSNs offers a solution to this challenge [6].
WSNs typically consist of a plethora of economical wireless ground sensor nodes (GSNs). In most research on secure sensor networks, there are few studies on the lifespan of UAV-assisted sensor networks. Most papers focus on the energy consumption or communication rate issues of UAVs. However, the energy consumption of these sensors poses a potential threat to the WSN’s lifespan [7]. To combat this issue, researchers have proposed a flexible trajectory design of UAVs, which incorporates a sleep and wake-up mechanism to efficiently gather information and preserve GSNs’ energy consumption [8]. To further elucidate, the sleep and wake-up mechanism in WSNs implies that when the GSN is not engaged in any communication with the UAV, it goes into a state of dormancy to conserve energy. Conversely, when the UAV approaches the GSN, the GSN promptly awakens and begins transmitting information to the UAV. However, given the constant movement of the UAV, it is essential to consider the highly dynamic wireless channels between the UAV and the GSNs to avoid any unexpected packet loss [9]. Therefore, reasonable UAV trajectory planning is an indispensable factor that must be taken into account [10].
In addition, the advent of UAVs has made wireless communications a breeze, thanks to their superior information transmission rates [11] and reduced transmit delay [12]. However, their broadcast characteristics make them a susceptible target for illegal eavesdroppers (Eve) [13]. Fortunately, physical layer security, a promising secure communication technology that is extensively employed, plays a pivotal role in preventing the prying eyes of Eve [14]. But here is the catch: in practice, it is very ideal to assume that the channel state information (CSI) of Eve is completely known [15]. Therefore, it is meaningful to discuss UAV-assisted secure communication when the CSI of Eve is unknown.

2. The Application of UAVs in Secure WSNs

UAVs have a wide application space in WSNs, which cannot directly communicate with the data center. Ref. [16] discuss a UAV-powered WSN, where the UAV transmits energy to the ground sensor through the antenna, and the sensor will send the collected information to the UAV after receiving it. The author minimizes the time required for the UAV to collect information by jointly optimizing the height of the UAV and the antenna beamwidth. In [17], the authors proposed a task offloading mechanism learning algorithm, which can predict the queuing delay of all UAVs, reduce network overhead and increase user satisfaction. Ref. [18] considered a large-scale WSN where some GSN may not be able to upload information for a long time, resulting in insufficient storage capacity. The authors proposed a data collection strategy to minimize the data loss by jointly optimizing the sensor scheduling and the UAV’s trajectory. Refs. [19][20] investigated the energy consumption problem of the UAV-assisted WSN. Zhu et al. [19] proposed a novel optimization algorithm based on a deep reinforcement learning technique that can effectively reduce the UAV’s consumption. Beak et al. [20] model the UAV collecting ground sensor information as a non-convex problem, and optimize the trajectory by the Voronoi diagram to maximize the residual energy after the sensor transmits information.

3. Security Performance in UAV-Enabled WSNs

Since UAVs are more vulnerable to eavesdropping by illegal parties, some recent studies have considered the physical layer security of UAV-assisted WSNs. Ref. [21] investigate a UAV-assisted WSN with multiple eavesdroppers, and considered a downlink secure transmission scheme based on power splitting, where the transmission power of the UAV is divided into information transmission and noise generation. The authors proposed an optimization algorithm to maximize the minimum average secrecy rate. In [22], the authors considered how to improve the quality of service (QoS) of the wireless networks, joint optimization of the video levels selection, power allocation, and a UAV trajectory algorithm is proposed to maximize the ratio of power consumption to video quality. Refs. [23][24] discussed secrecy capacity maximum problem in cache-enabled UAV communications. Ref. [23] investigate a UAV-enabled network with D2D communications, where the UAV and D2D transmitter are equipped with caches that the users can directly obtain high-frequency communication requirements without communicating with the base station. In [24], the caching-equipped UAV is used to replace the small cell to communicate with the user, and the replaced cell is used as the interference source to send interference signals to Eve to improve the security performance of the system.

4. Secrecy Energy Efficiency in UAV-Enabled WSN

To realize the goal of energy-efficient communication while ensuring communication, secrecy energy efficiency (SEE) has increasingly become hot research in UAV-assisted WSNs. Li et al. [25] discuss two main challenges in a UAV-enabled WSN: the UAV’s energy consumption and secure transmission. The authors proposed a low-complexity iterative algorithm to maximize the secrecy energy efficiency. In [26], the authors discussed a multi-carrier multi-UAV enabled WSN, where the UAVs use Cooperative Rate-Splitting (CRS) technique to protect the communication between UAVs and the ground sensors, and proposed a secure resource allocation alternating iterative algorithm to maximize the UAV’s SEE by jointly optimizing the resource allocation and the ground sensors’ association matrix. Refs. [27][28] both introduced the simultaneous wireless information and power transfer (SWIPT) technology when considering the maximization of Secrecy Energy Efficiency, among which Ref. [27] assumed that the users divide the received signal into two parts, which are used for energy collection and information decoding, respectively. Ref. [28] assumed that only known the channel distribution information (CDI) of Eves. In addition, the dual-layer PS receiver architecture is introduced to solve the problem of energy harvesting (EH) circuits’ performance limitation.

References

  1. Amrallah, A.; Mohamed, E.M.; Tran, G.K.; Sakaguchi, K. Optimization of UAV 3D Trajectory in a Post-disaster Area Using Dual Energy-Aware Bandits. IEICE Commun. Express 2023, 12, 403–408.
  2. Bao, T.; Yang, H.C.; Hasna, M.O. Secrecy Performance Analysis of UAV-Assisted Relaying Communication Systems. IEEE Trans. Veh. Technol. 2020, 69, 1122–1126.
  3. Ye, J.; Zhang, C.; Lei, H.; Pan, G.; Ding, Z. Secure UAV-to-UAV Systems With Spatially Random UAVs. IEEE Wirel. Commun. Lett. 2019, 8, 564–567.
  4. Ouyang, J.; Pan, Y.; Xu, B.; Lin, M.; Zhu, W.P. Achieving Secrecy Energy Efficiency Fairness in UAV-Enabled Multi-User Communication Systems. IEEE Wirel. Commun. Lett. 2022, 11, 918–922.
  5. Yao, J.; Xu, J. Secrecy Transmission in Large-Scale UAV-Enabled Wireless Networks. IEEE Trans. Commun. 2019, 67, 7656–7671.
  6. Wang, W.; Tian, H.; Ni, W. Secrecy Performance Analysis of IRS-Aided UAV Relay System. IEEE Wirel. Commun. Lett. 2021, 10, 2693–2697.
  7. Wang, Y.; Chen, M.; Pan, C.; Wang, K.; Pan, Y. Joint Optimization of UAV Trajectory and Sensor Uploading Powers for UAV-Assisted Data Collection in Wireless Sensor Networks. IEEE Internet Things J. 2022, 9, 11214–11226.
  8. Wang, Y.; Hu, Z.; Wen, X.; Lu, Z.; Miao, J. Minimizing Data Collection Time With Collaborative UAVs in Wireless Sensor Networks. IEEE Access. 2020, 8, 98659–98669.
  9. Ebrahimi, D.; Sharafeddine, S.; Ho, P.H.; Assi, C. UAV-Aided Projection-Based Compressive Data Gathering in Wireless Sensor Networks. IEEE Internet Things J. 2019, 6, 1893–1905.
  10. Wei, Z.; Zhu, M.; Zhang, N.; Wang, L.; Zou, Y.; Meng, Z.; Wu, H.; Feng, Z. UAV-Assisted Data Collection for Internet of Things: A Survey. IEEE Internet Things J. 2022, 9, 15460–15483.
  11. Abughalwa, M.; Hasna, M.O. A Secrecy Study of UAV Based Networks With Fountain Codes and FD Jamming. IEEE Commun. Lett. 2021, 25, 1796–1800.
  12. Wu, H.; Li, H.; Wei, Z.; Zhang, N.; Tao, X. Secrecy Performance Analysis of Air-to-Ground Communication With UAV Jitter and Multiple Random Walking Eavesdroppers. IEEE Trans. Veh. Technol. 2021, 70, 572–584.
  13. Pang, X.; Zhao, N.; Tang, J.; Wu, C.; Niyato, D.; Wong, K.K. IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design. IEEE Trans. Commun. 2022, 70, 1140–1152.
  14. Sun, G.; Tao, X.; Li, N.; Xu, J. Intelligent Reflecting Surface and UAV Assisted Secrecy Communication in Millimeter-Wave Networks. IEEE Trans. Veh. Technol. 2021, 70, 11949–11961.
  15. Zhou, Y.; Pan, C.; Yeoh, P.L.; Wang, K.; Elkashlan, M.; Vucetic, B.; Li, Y. Secure Communications for UAV-Enabled Mobile Edge Computing Systems. IEEE Trans. Commun. 2020, 68, 376–388.
  16. Choi, H.H.; Lee, J.R. Joint Optimization of Altitude and Beamwidth for UAV-Powered Wireless Sensor Networks. IEEE Trans. Veh. Technol. 2023, 72, 1279–1284.
  17. Al-Share, R.; Shurman, M.; Alma’aitah, A. A Collaborative Learning-Based Algorithm for Task Offloading in UAV-Aided Wireless Sensor Networks. Comput. J. 2021, 64, 1575–1583.
  18. Wang, X.; Liu, X.; Cheng, C.T.; Deng, L.; Chen, X.; Xiao, F. A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAV-Assisted WSN. IEEE Commun. Lett. 2021, 25, 2333–2337.
  19. Zhu, B.; Bedeer, E.; Nguyen, H.H.; Barton, R.; Henry, J. UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning. IEEE Trans. Veh. Technol. 2021, 70, 9540–9554.
  20. Baek, J.; Han, S.I.; Han, Y. Energy-Efficient UAV Routing for Wireless Sensor Networks. IEEE Trans. Veh. Technol. 2020, 69, 1741–1750.
  21. Fu, H.; Sheng, Z.; Nasir, A.A.; Muqaibel, A.H.; Hanzo, L. Securing the UAV-Aided Non-Orthogonal Downlink in the Face of Colluding Eavesdroppers. IEEE Trans. Veh. Technol. 2022, 71, 6837–6842.
  22. Zhang, Z.; Zhang, Q.; Miao, J.; Yu, F.R.; Fu, F.; Du, J.; Wu, T. Energy-Efficient Secure Video Streaming in UAV-Enabled Wireless Networks: A Safe-DQN Approach. IEEE Trans. Green Commun. Netw. 2021, 5, 1892–1905.
  23. Ji, J.; Zhu, K.; Niyato, D.; Wang, R. Joint Trajectory Design and Resource Allocation for Secure Transmission in Cache-Enabled UAV-Relaying Networks With D2D Communications. IEEE Internet Things J. 2021, 8, 1557–1571.
  24. Zhao, N.; Cheng, F.; Yu, F.R.; Tang, J.; Chen, Y.; Gui, G.; Sari, H. Caching UAV Assisted Secure Transmission in Hyper-Dense Networks Based on Interference Alignment. IEEE Trans. Commun. 2018, 66, 2281–2294.
  25. Li, M.; Tao, X.; Li, N.; Wu, H.; Xu, J. Secrecy Energy Efficiency Maximization in UAV-Enabled Wireless Sensor Networks Without Eavesdropper’s CSI. IEEE Internet Things J. 2022, 9, 3346–3358.
  26. Bastami, H.; Moradikia, M.; Abdelhadi, A.; Behroozi, H.; Clerckx, B.; Hanzo, L. Maximizing the Secrecy Energy Efficiency of the Cooperative Rate-Splitting Aided Downlink in Multi-Carrier UAV Networks. IEEE Trans. Veh. Technol. 2022, 71, 11803–11819.
  27. Yu, H.; Guo, S.; Yang, Y.; Ji, L.; Yang, Y. Secrecy Energy Efficiency Optimization for Downlink Two-User OFDMA Networks with SWIPT. IEEE Syst. J. 2019, 13, 324–335.
  28. Lu, Y.; Xiong, K.; Fan, P.; Ding, Z.; Zhong, Z.; Letaief, K.B. Secrecy Energy Efficiency in Multi-Antenna SWIPT Networks with Dual-Layer PS Receivers. IEEE Trans. Wirel. Commun. 2020, 19, 4290–4306.
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Subjects: Telecommunications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Xufei Ding
,
Wen Tian
,
Guangjie Liu
,
Xiaopeng Ji
View Times: 289
Revisions: 2 times (View History)
Update Date: 07 Dec 2023
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