With the support of wireless network, tourists in tourism areas could enjoy various tourism information search and smart tourism–related services. However, due to the limited capacity of wireless networks, in peaking seasons, tourism area crowding in local areas could result in emergency and temporary wireless network congestion. While increasing infrastructure investment (e.g., densifying base stations) is desiring for peak seasons, it can be a waste of resource for significantly shrunk tourist arrival in off seasons. In response to the temporary network congestion offloading demand, this paper proposes an on-demand coverage solution based on unmanned aerial vehicle (UAV) base stations. Firstly, taking the air-to-ground channel characteristic into account, we define the effective coverage radius, based on which the optimal altitude of UAV BS is derived. Then, to tackle the inherent challenge of irregular tourist distribution issue in tourism areas, an automatic UAV BS deployment algorithm is designed to determine the minimal number of UAV BSs and their two-dimensional coordinates simultaneously. Simulation results show that the proposed solution could realize efficient UAV BS on-demand deployment.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
See past news in China: (1) Tourists gather in Hongyadong, an Internet celebrity attraction: there is no signal when the phone is crowded, https://society.huanqiu.com/gallery/9CaKrnQhKdS; (2) 600,000 people in West Lake and 400,000 people in Shenzhen Bay! No cell phone signal, toilet lined up for 1 h, https://news.hexun.com/2019-05-03/197056356.html; (3) A scenic spot in Wuhan issued a red warning for tourists overwhelmed with mobile phones without signal, https://hb.qq.com/a/20180715/005536.htm
Al-Hourani A, Kandeepan S, Jamalipour A (2014) Modeling air-to-ground path loss for low altitude platforms in urban environments. In: 2014 IEEE Global communications conference, pp 2898–2904. https://doi.org/10.1109/GLOCOM.2014.7037248
Al-Hourani A, Kandeepan S, Lardner S (2014) Optimal lap altitude for maximum coverage. IEEE Wirel Commun Lett 3 (6):569–572. <GotoISI>://WOS:000209681700006. V43la Times Cited:40 Cited References Count:9
Buhalis D, O’Connor P (2005) Information communication technology revolutionizing tourism. Tour Recreat Res 30(3):7–16
Bureau NC (2019) Tourism: Overview of the cultural tourism market of the “national day golden week” in 2019
Byun J, Kim BW, Ko CY, Byun JW (2017) 4g lte network access system and pricing model for iot mvnos: spreading smart tourism. Multimed Tools Appl 76(19):19665–19688. https://doi.org/10.1007/s11042-016-3369-3
Chen R, Li X, Sun Y, Li S, Sun Z (2019) Multi-uav coverage scheme for average capacity maximization. IEEE Commun Lett 1–1. https://doi.org/10.1109/LCOMM.2019.2962774
Cheng ST, Chen Y, Horng GJ, Wang CH (2013) Using cellular automata to reduce congestion for tourist navigation systems in mobile environments. Wirel Pers Commun 73(3):441–461. https://doi.org/10.1007/s11277-013-1196-7
Du Z, Deng Y, Guo W, Nallanathan A, Wu Q (2019) Green deep reinforcement learning for radio resource management: architecture, algorithm compression, and challenge. arXiv:1910.05054
Du Z, Sun Y, Guo W, Wu Q, Xu Y, Zhang J (2018) Data-driven deployment and cooperative self-organization in ultra-dense small cell networks. IEEE Access 6:22839–22848
Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N (2017) Scenic route planning for tourists. Pers Ubiquit Comput 21(1):137–155. https://doi.org/10.1007/s00779-016-0971-3
Gössling S, Hall CM (2006) Uncertainties in predicting tourist flows under scenarios of climate change. Clim Change 79(3):163–173. https://doi.org/10.1007/s10584-006-9081-y
Gretzel U, Sigala M, Xiang Z, Koo C (2015) Smart tourism: foundations and developments. Electron Mark 25(3):179–188. https://doi.org/10.1007/s12525-015-0196-8
Guo Z, Wei Z, Feng Z, Fan N (2017) Coverage probability of multiple uavs supported ground network. Electron Lett 53(2):885–887
Katircioglu S, Cizreliogullari MN, Katircioglu S (2019) Estimating the role of climate changes on international tourist flows: evidence from mediterranean island states. Environ Sci Pollut Res 26(14):14393–14399. https://doi.org/10.1007/s11356-019-04750-w
Khuwaja AA, Chen Y, Zhao N, Alouini M, Dobbins P (2018) A survey of channel modeling for uav communications. IEEE Commun Surv Tutor 20(4):2804–2821. https://doi.org/10.1109/COMST.2018.2856587
Li X, Yao H, Wang J, Xu X, Jiang C, Hanzo L (2019) A near-optimal uav-aided radio coverage strategy for dense urban areas. IEEE Trans Veh Technol 68(9):9098–9109. https://doi.org/10.1109/TVT.2019.2927425
Li Y, Cai L (2017) Uav-assisted dynamic coverage in a heterogeneous cellular system. IEEE Netw 31(4):56–61
Lim TY (2012) Designing the next generation of mobile tourism application based on situation awareness. In: 2012 Southeast asian network of ergonomics societies conference (SEANES), pp 1–7
Ma Z, Ai B, He R, Wang G, Niu Y, Zhong Z (2019) A wideband non-stationary air-to-air channel model for uav communications. IEEE Trans Veh Technol 1–1. https://doi.org/10.1109/TVT.2019.2961178
Mozaffari M, Saad W, Bennis M, Debbah M (2016) Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Commun Lett 20:1647–1650
Pan B, Zheng C, Song F (2019) A comparison of the development of tourism information technologies between China and the united states. Inf Technol Tour 21(1):1–6. https://doi.org/10.1007/s40558-018-0131-x
Parroco AM, Scuderi R (2012) Short term dynamics of tourist arrivals: What do italian destinations have in common?. In: Gaul WA, Geyer-Schulz A, Schmidt-Thieme L, Kunze J (eds) Challenges at the interface of data analysis, computer science, and optimization. Springer, Berlin, pp 577–584
Ribeiro FR, Silva A, Barbosa F, Silva AP, Metrôlho JC (2018) Mobile applications for accessible tourism: overview, challenges and a proposed platform. Inf Technol Tour 19(1):29–59
Ruan L, Wang J, Chen J, Xu Y, Yang Y, Jiang H, Zhang Y, Xu Y (2018) Energy-efficient multi-uav coverage deployment in uav networks: a game-theoretic framework. China Commun 15(10):194–209
Sharma PK, Kim DI (2019) Coverage probability of 3-d mobile uav networks. IEEE Wirel Commun Lett 8(1):97–100. https://doi.org/10.1109/LWC.2018.2859923
Smirnov AV, Kashevnik AM, Ponomarev A (2017) Context-based infomobility system for cultural heritage recommendation: Tourist assistant—tais. Pers Ubiquit Comput 21(2):297–311. https://doi.org/10.1007/s00779-016-0990-0
Sun Y, Du Z, Xu Y, Zhang Y, Jia L, Anpalagan A (2018) Directed-hypergraph-based channel allocation for ultradense cloud d2d communications with asymmetric interference. IEEE Trans Veh Technol 67(8):7712–7718. https://doi.org/10.1109/TVT.2018.2839352
Zhu Q, Jiang K, Chen X, Zhong W, Yang Y (2018) A novel 3d non-stationary uav-mimo channel model and its statistical properties. China Commun 15(12):147–158
This work was supported by the Philosophy and Social Science Projects in Universities of Jiangsu Province under Grant No. 2020SJA0790 and the 13th Five-Year Plan of Jiangsu Educational Science under Grant No. C-c/2020/03/20.
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Yin, L., Zhang, N. & Tang, C. On-demand UAV base station deployment for wireless service of crowded tourism areas. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-020-01515-y
- Wireless communication
- Network congestion
- Unmanned aerial vehicle (UAV)
- Base station automatic deployment
- On-demand coverage