Unmanned aerial vehicles towards future Industrial Internet: Roles and opportunities

Abstract

Unmanned Aerial Vehicles (UAVs) are gaining increasing attention in many fields, such as military, logistics, and hazardous site mapping. Utilizing UAVs to assist communications is one of the promising applications and research directions. The future Industrial Internet places higher demands on communication quality. The easy deployment, dynamic mobility, and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet. Therefore, UAVs are considered as an integral part of Industry 4.0. In this article, three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized. Then, the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented. According to the current research, it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent. Finally, the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.

Keywords

Unmanned aerial vehicles (UAVs) / UAV-assisted communications / Industrial Internet

References

[1] B.P. Tice, Unmanned aerial vehicles: the force multiplier of the 1990s, Airpower J. 5(1) (1991) 41-55.

[2] A. Faust, I. Palunko, P. Cruz, R. Fierro, L. Tapia, Automated aerial suspended cargo delivery through reinforcement learning, Artif. Intell. 247 (2017) 381-398.

[3] K. Han, A. Garcia, I. Leo, M. Campo, C. Muhammad, L. Ortiz-Valles, G. Donohue, H. Redeiss, L. Sherry, Unmanned aerial vehicle (uav) cargo system: senior design capstone project,in: Proceedings of the 2004 IEEE Systems and Information Engi-neering Design Symposium, 2004, IEEE, 2004, pp. 121-130.

[4] H. Cao, Y. Liu, X. Yue, W. Zhu, Cloud-assisted uav data collection for multiple emerg-ing events in distributed wsns, Sensors 17 (8) (2017).

[5] P. Boccardo, F. Chiabrando, F. Dutto, F. Tonolo, A. Lingua, Uav deployment exer-cise for mapping purposes: evaluation of emergency response applications, Sensors 15 (7) (2015) 15717-15737.

[6] E. Honkavaara, H. Saari, J. Kaivosoja, I. Pölönen, T. Hakala, P. Litkey, J. Mäky-nen, L. Pesonen, Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight uav spectral camera for precision agriculture, Remote Sens. 5 (10) (2013) 5006-5039.

[7] P. Tokekar, J. Vander Hook, D. Mulla, V. Isler, Sensor planning for a symbiotic uav and ugv system for precision agriculture, IEEE Trans. Robot. 32 (6) (2016) 1498-1511.

[8] D. Gómez-Candón, A. De Castro, F. López-Granados, Assessing the accuracy of mo-saics from unmanned aerial vehicle (uav) imagery for precision agriculture purposes in wheat, Precis. Agric. 15 (1) (2014) 44-56.

[9] S. Siebert, J. Teizer, Mobile 3d mapping for surveying earthwork projects using an unmanned aerial vehicle (uav) system, Autom. Constr. 41 (2014) 1-14.

[10] F. Nex, F. Remondino, Uav for 3d mapping applications: a review, Appl. Geom. 6(1)(2014) 1-15.

[11] S. Hayat, E. Yanmaz, R. Muzaffar, Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint, IEEE Commun. Surv. Tutor. 18 (4)(2016) 2624-2661.

[12] B. Li, Z. Fei, Y. Zhang, Uav communications for 5g and beyond: recent advances and future trends, IEEE Int. Things J. 6(2) (2019) 2241-2263.

[13] D.I. Insights,Global drone market report 2022-2030, Tech. Rep, 2022.

[14] B. Bajic, A. Rikalovic, N. Suzic, V. Piuri,Industry 4.0 implementation challenges and opportunities: a managerial perspective, IEEE Syst. J. 15 (1) (2021) 546-559.

[15] M. Gundall, M. Strufe, H.D. Schotten, P. Rost, C. Markwart, R. Blunk, A. Neumann, J. Grießbach, M. Aleksy, D. Wübben,Introduction of a 5g-enabled architecture for the realization of industry 4.0 use cases, IEEE Access 9 (2021) 25508-25521.

[16] H. Huang, A.V. Savkin, Deployment of heterogeneous uav base stations for optimal quality of coverage, IEEE Int. Things J. 9 (17) (2022) 16429-16437.

[17] X. Zhang, H. Zhao, J. Wei, C. Yan, J. Xiong, X. Liu, Cooperative trajectory design of multiple uav base stations with heterogeneous graph neural networks, IEEE Trans. Wirel. Commun. 22 (3) (2023) 1495-1509.

[18] J. Lyu, Y. Zeng, R. Zhang, T.J. Lim, Placement optimization of uav-mounted mobile base stations, IEEE Commun. Lett. 21 (3) (2017) 604-607.

[19] Z. Wei, M. Zhu, N. Zhang, L. Wang, Y. Zou, Z. Meng, H. Wu, Z. Feng, Uav-assisted data collection for Internet of things: a survey, IEEE Int. Things J. 9 (17) (2022) 15460-15483.

[20] X. Wang, M.C. Gursoy, T. Erpek, Y.E. Sagduyu, Learning-based uav path planning for data collection with integrated collision avoidance, IEEE Int. Things J. 9 (17)(2022) 16663-16676.

[21] R. Delaney, A. Shakoor, C.F. Watts,Comparing unmanned aerial vehicle (uav), ter-restrial lidar, and brunton compass methods for discontinuity data collection, in:IAEG/AEG Annual Meeting Proceedings 2018, vol. 1, Springer, San Francisco, Cali-fornia, 2019, pp. 267-273.

[22] N.H. Mahmood, G. Berardinelli, E.J. Khatib, R. Hashemi, C. De Lima, M. Latva-aho, A functional architecture for 6g special-purpose industrial iot networks, IEEE Trans. Ind. Inform. 19 (3) (2023) 2530-2540.

[23] E. Yaacoub, M.-S. Alouini, A key 6g challenge and opportunity—connecting the base of the pyramid: a survey on rural connectivity, Proc. IEEE 108 (4) (2020) 533-582.

[24] S. Chen, Y.-C. Liang, S. Sun, S. Kang, W. Cheng, M. Peng, Vision, requirements, and technology trend of 6g: how to tackle the challenges of system coverage, capacity, user data-rate and movement speed, IEEE Wirel. Commun. 27 (2) (2020) 218-228.

[25] M. Shafi, A.F. Molisch, P.J. Smith, T. Haustein, P. Zhu, P. De Silva, F. Tufvesson, A. Benjebbour, G. Wunder, 5g: a tutorial overview of standards, trials, challenges, deployment, and practice, IEEE J. Sel. Areas Commun. 35 (6) (2017) 1201-1221.

[26] Y. Zeng, R. Zhang, T.J. Lim, Wireless communications with unmanned aerial vehi-cles: opportunities and challenges, IEEE Commun. Mag. 54 (5) (2016) 36-42.

[27] J. Liu, Y. Shi, Z.M. Fadlullah, N. Kato, Space-air-ground integrated network: a sur-vey, IEEE Commun. Surv. Tutor. 20 (4) (2018) 2714-2741.

[28] 3GPP, Enhancement for Unmanned Aerial Vehicles, 2019, 3rd Generation Partner-ship Project (3GPP).

[29] S. Katikala, Google project loon, InSight Rivier Acad. J. 10 (2) (2014) 1-6.

[30] Facebook, Connecting the world from the sky, Tech. Rep, 2014.

[31] Ieee Standard Definitions of Terms for Antennas, IEEE Std 145-1993 1993, pp. 1-32.

[32] A. Al-Hourani, S. Kandeepan, S. Lardner, Optimal LAP altitude for maximum cover-age, IEEE Wirel. Commun. Lett. 3(6) (2014) 569-572.

[33] M. Alzenad, A. El-Keyi, F. Lagum, H. Yanikomeroglu, 3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage, IEEE Wirel. Commun. Lett. 6(4) (2017) 434-437.

[34] L. Wang, H. Zhang, S. Guo, D. Yuan, Deployment and association of multiple uavs in uav-assisted cellular networks with the knowledge of statistical user position, IEEE Trans. Wirel. Commun. 21 (8) (2022) 6553-6567.

[35] C.H. Liu, X. Ma, X. Gao, J. Tang, Distributed energy-efficient multi-uav navigation for long-term communication coverage by deep reinforcement learning, IEEE Trans. Mob. Comput. 19 (6) (2020) 1274-1285.

[36] C. Zhang, L. Zhang, L. Zhu, T. Zhang, Z. Xiao, X.-G. Xia, 3d deployment of multiple uav-mounted base stations for uav communications, IEEE Trans. Commun. 69 (4)(2021) 2473-2488.

[37] V. Sharma, M. Bennis, R. Kumar, UAV-assisted heterogeneous networks for capacity enhancement, IEEE Commun. Lett. 20 (6) (2016) 1207-1210.

[38] M. Dai, N. Huang, Y. Wu, J. Gao, Z. Su, Unmanned-aerial-vehicle-assisted wireless networks: advancements, challenges, and solutions, IEEE Int. Things J. 10 (5) (2023) 4117-4147.

[39] M. Mozaffari, W. Saad, M. Bennis, M. Debbah, Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of things communications, IEEE Trans. Wirel. Commun. 16 (11) (2017) 7574-7589.

[40] D. Zhai, C. Wang, R. Zhang, H. Cao, F.R. Yu, Energy-saving deployment optimization and resource management for uav-assisted wireless sensor networks with noma, IEEE Trans. Veh. Technol. 71 (6) (2022) 6609-6623.

[41] B. Omoniwa, B. Galkin, I. Dusparic, Optimizing energy efficiency in uav-assisted net-works using deep reinforcement learning, IEEE Wirel. Commun. Lett. 11 (8) (2022) 1590-1594.

[42] F. Zeng, Z. Hu, Z. Xiao, H. Jiang, S. Zhou, W. Liu, D. Liu, Resource allocation and trajectory optimization for qoe provisioning in energy-efficient uav-enabled wireless networks, IEEE Trans. Veh. Technol. 69 (7) (2020) 7634-7647.

[43] L. Xie, J. Xu, R. Zhang, Throughput maximization for uav-enabled wireless powered communication networks, IEEE Int. Things J. 6(2) (2019) 1690-1703.

[44] X. Yuan, H. Jiang, Y. Hu, A. Schmeink, Joint analog beamforming and trajectory planning for energy-efficient uav-enabled nonlinear wireless power transfer, IEEE J. Sel. Areas Commun. 40 (10) (2022) 2914-2929.

[45] H. Yan, Y. Chen, S.-H. Yang, Uav-enabled wireless power transfer with base station charging and uav power consumption, IEEE Trans. Veh. Technol. 69 (11) (2020) 12883-12896.

[46] M. Mozaffari, W. Saad, M. Bennis, M. Debbah, Optimal transport theory for cell association in UAV-enabled cellular networks, IEEE Commun. Lett. 21 (9) (2017) 2053-2056.

[47] C. Zhan, Y. Zeng, Completion time minimization for multi-uav-enabled data collec-tion, IEEE Trans. Wirel. Commun. 18 (10) (2019) 4859-4872.

[48] Y. Liu, J. Yan, X. Zhao, Deep reinforcement learning based latency minimization for mobile edge computing with virtualization in maritime uav communication net-work, IEEE Trans. Veh. Technol. 71 (4) (2022) 4225-4236.

[49] H. Hu, K. Xiong, G. Qu, Q. Ni, P. Fan, K.B. Letaief, Aoi-minimal trajectory planning and data collection in uav-assisted wireless powered iot networks, IEEE Int. Things J. 8(2) (2021) 1211-1223.

[50] C. Zhan, H. Hu, X. Sui, Z. Liu, D. Niyato, Completion time and energy optimization in the uav-enabled mobile-edge computing system, IEEE Int. Things J. 7(8) (2020) 7808-7822.

[51] H. Guo, X. Zhou, Y. Wang, J. Liu, Achieve load balancing in multi-uav edge comput-ing iot networks: a dynamic entry and exit mechanism, IEEE Int. Things J. 9 (19)(2022) 18725-18736.

[52] S. Zhang, H. Zhang, Q. He, K. Bian, L. Song, Joint trajectory and power optimization for UAV relay networks, IEEE Commun. Lett. 22 (1) (2018) 161-164.

[53] M.M. Azari, F. Rosas, K.-C. Chen, S. Pollin, Ultra reliable uav communication using altitude and cooperation diversity, IEEE Trans. Commun. 66 (1) (2018) 330-344.

[54] Y. Han, L. Liu, L. Duan, R. Zhang, Towards reliable uav swarm communication in d2d-enhanced cellular networks, IEEE Trans. Wirel. Commun. 20 (3) (2021) 1567-1581.

[55] M. Mozaffari, W. Saad, M. Bennis, M. Debbah, Unmanned aerial vehicle with un-derlaid device-to-device communications: performance and tradeoffs, IEEE Trans. Wirel. Commun. 15 (6) (2016) 3949-3963.

[56] M. Mozaffari, W. Saad, M. Bennis, M. Debbah, Drone small cells in the clouds: de-sign, deployment and performance analysis, in: 2015 IEEE Global Communications Conference (GLOBECOM), 2015, pp. 1-6.

[57] X. Pang, W. Mei, N. Zhao, R. Zhang, Intelligent reflecting surface assisted interfer-ence mitigation for cellular-connected uav, IEEE Wirel. Commun. Lett. 11 (8) (2022) 1708-1712.

[58] D. Wu, Z. Yang, P. Zhang, R. Wang, B. Yang, X. Ma, Virtual-reality inter-promotion technology for metaverse: a survey, IEEE Int. Things J. (2023) 1, https://doi.org/10.1109/JIOT.2023.3265848.

[59] H. Zhang, M. Huang, H. Zhou, X. Wang, N. Wang, K. Long, Capacity maximization in ris-uav networks: a ddqn-based trajectory and phase shift optimization approach, IEEE Trans. Wirel. Commun. 22 (4) (2023) 2583-2591.

[60] Y. Zeng, R. Zhang, T.J. Lim, Throughput maximization for UAV-enabled mobile relaying systems, IEEE Trans. Commun. 64 (12) (2016) 4983-4996.

[61] K. Meng, Q. Wu, S. Ma, W. Chen, K. Wang, J. Li, Throughput maximization for uav-enabled integrated periodic sensing and communication, IEEE Trans. Wirel. Commun. 22 (1) (2023) 671-687.

[62] Y. Pan, X. Da, H. Hu, Y. Huang, M. Zhang, K. Cumanan, O.A. Dobre, Joint optimiza-tion of trajectory and resource allocation for time-constrained uav-enabled cognitive radio networks, IEEE Trans. Veh. Technol. 71 (5) (2022) 5576-5580.

[63] S. Ahmed, M.Z. Chowdhury, Y.M. Jang, Energy-efficient uav-to-user scheduling to maximize throughput in wireless networks, IEEE Access 8 (2020) 21215-21225.

[64] C. Wang, D. Deng, L. Xu, W. Wang, Resource scheduling based on deep reinforce-ment learning in uav assisted emergency communication networks, IEEE Trans. Commun. 70 (6) (2022) 3834-3848.

[65] X. Lin, V. Yajnanarayana, S.D. Muruganathan, S. Gao, H. Asplund, H.L. Maattanen, M. Bergstrom, S. Euler, Y.P.E. Wang, The sky is not the limit: LTE for unmanned aerial vehicles, IEEE Commun. Mag. 56 (4) (2018) 204-210.

[66] W. Khawaja, I. Guvenc, D.W. Matolak, U.-C. Fiebig, N. Schneckenburger, A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles, IEEE Commun. Surv. Tutor. 21 (3) (2019) 2361-2391.

[67] H. Zhang, J. Zhang, K. Long, Energy efficiency optimization for noma uav network with imperfect csi, IEEE J. Sel. Areas Commun. 38 (12) (2020) 2798-2809.

[68] Y. Li, H. Zhang, K. Long, Joint resource, trajectory, and artificial noise optimization in secure driven 3-d uavs with noma and imperfect csi, IEEE J. Sel. Areas Commun. 39 (11) (2021) 3363-3377.

[69] P.K. Sharma, D.I. Kim, UAV-enabled downlink wireless system with non-orthogonal multiple access, in: 2017 IEEE Globecom Workshops (GC Wkshps), 2017, pp. 1-6.

[70] Z. Feng, L. Ji, Q. Zhang, W. Li, Spectrum management for mmwave enabled uav swarm networks: challenges and opportunities, IEEE Commun. Mag. 57 (1) (2019) 146-153.

[71] C. Fan, C. She, H. Zhang, B. Li, C. Zhao, D. Niyato, Learning to optimize user as-sociation and spectrum allocation with partial observation in mmwave-enabled uav networks, IEEE Trans. Wirel. Commun. 21 (8) (2022) 5873-5888.

[72] P. Susarla, B. Gouda, Y. Deng, M. Juntti, O. Silvén, A. Tölli, Learning-based beam alignment for uplink mmwave uavs, IEEE Trans. Wirel. Commun. 22 (3) (2023) 1779-1793.

[73] B. Jiang, J. Li, G. Yue, H. Song, Differential privacy for industrial Internet of things: opportunities, applications, and challenges, IEEE Int. Things J. 8 (13) (2021) 10430-10451.

[74] D. Wu, M. Sun, P. Zhang, Y. Tu, Z. Yang, R. Wang, Personalized secure demand-oriented data service toward edge-cloud collaborative iot, IEEE Int. Things J. 10 (1)(2023) 378-390.

[75] A.-R. Sadeghi, C. Wachsmann, M. Waidner,Security and privacy challenges in in-dustrial Internet of things, in:2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), 2015, pp. 1-6.

[76] Z. Yang, R. Wang, D. Wu, H. Wang, H. Song, X. Ma, Local trajectory privacy pro-tection in 5g enabled industrial intelligent logistics, IEEE Trans. Ind. Inform. 18 (4)(2022) 2868-2876.

[77] M. Yahuza, M.Y.I. Idris, I.B. Ahmedy, A.W.A. Wahab, T. Nandy, N.M. Noor, A. Bala, Internet of drones security and privacy issues: taxonomy and open challenges, IEEE Access 9 (2021) 57243-57270.

[78] S.J. Russell, Artificial Intelligence a Modern Approach, Pearson Education, Inc., 2010.