ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 29-43.doi: 10.12142/ZTECOM.202102005
• Special Topic • Previous Articles Next Articles
LOU Kaihao1(), YANG Yongjian1, YANG Funing1, ZHANG Xingliang2
Received:
2021-03-11
Online:
2021-06-25
Published:
2021-07-27
About author:
LOU Kaihao (Supported by:
LOU Kaihao, YANG Yongjian, YANG Funing, ZHANG Xingliang. Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data[J]. ZTE Communications, 2021, 19(2): 29-43.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
1 | QUINN P. Global digital OOH media revenues pacing up 13% in 2017, US DOOH advertising expands 10%: PQ media [EB/OL]. (2017⁃09⁃06) [2020⁃01⁃01]. |
2 |
LOU K H, LI S Q, YANG F N, et al. Advertising strategy for maximizing profit using Crowdsensing trajectory data [C]// Security and Privacy in Social Networks and Big Data. Singapore: Springer Singapore, 2020: 395–406. DOI: 10.1007/978-981-15-9031-3_35
doi: 10.1007/978-981-15-9031-3_35 |
3 | NIGAM S, ASTHANA S, GUPTA P. IoT based intelligent billboard using data mining [C]//International Conference on Innovation and Challenges in Cyber Security (ICICCS⁃INBUSH). Greater Noida, India: IEEE, 2016: 107–110 |
4 |
LIU D Y, WENG D, LI Y H, et al. SmartadP: visual analytics of large⁃scale taxi trajectories for selecting billboard locations [J]. IEEE transactions on visualization and computer graphics, 2017, 23(1): 1–10. DOI: 10.1109/TVCG.2016.2598432
doi: 10.1109/TVCG.2016.2598432 |
5 |
HUANG M, FANG Z X, XIONG S L, et al. Interest⁃driven outdoor advertising display location selection using mobile phone data [J]. IEEE access, 2019, 7: 30878–30889. DOI: 10.1109/ACCESS.2019.2903277
doi: 10.1109/ACCESS.2019.2903277 |
6 |
WANG L, YU Z W, YANG D Q, et al. Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data [J]. IEEE transactions on industrial informatics, 2020, 16(2): 1058–1066. DOI: 10.1109/TII.2019.2891258
doi: 10.1109/TII.2019.2891258 |
7 |
ZHENG H Y, WU J. Placement optimization for advertisement dissemination in smart City [J]. IEEE transactions on network science and engineering, 2020, 7(1): 239–252. DOI: 10.1109/TNSE.2018.2805768
doi: 10.1109/TNSE.2018.2805768 |
8 |
GANTI R K, YE F, LEI H. Mobile crowdsensing: current state and future challenges [J]. IEEE communications magazine, 2011, 49(11): 32–39. DOI: 10.1109/MCOM.2011.6069707
doi: 10.1109/MCOM.2011.6069707 |
9 |
LIU J W, SHEN H Y, ZHANG X. A survey of mobile crowdsensing techniques: a critical component for the Internet of Things [C]//25th International Conference on Computer Communication and Networks (ICCCN). Waikoloa, USA: IEEE, 2016: 1–6. DOI: 10.1109/ICCCN.2016.7568484
doi: 10.1109/ICCCN.2016.7568484 |
10 |
CAPPONI A, FIANDRINO C, KANTARCI B, et al. A survey on mobile crowdsensing systems: challenges, solutions, and opportunities [J]. IEEE communications surveys & tutorials, 2019, 21(3): 2419–2465. DOI: 10.1109/COMST.2019.2914030
doi: 10.1109/COMST.2019.2914030 |
11 | KARALIOPOULOS M, KOUTSOPOULOS I, TITSIAS M. First learn then earn: optimizing mobile crowdsensing campaigns through data⁃driven user profiling [C]//Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc ’16). New York, USA: ACM, 2016: 271–280 |
12 | BRACCIALE L, BONOLA M, LORETI P, et al. CRAWDAD dataset roma/taxi [EB/OL]. (2014⁃07⁃17) [2020⁃01⁃01]. |
13 | PIORKOWSKI M, SARAFIJANOVIC⁃DJUKIC N, GROSSGLAUSER M. CRAWDAD dataset epfl/mobility [EB/OL]. (2009⁃02⁃24)[2020⁃01⁃01]. |
14 | ZHENG Y, ZHANG L, XIE X, et al. Mining interesting locations and travel sequences from GPS trajectories [C]//Proceedings 13 of the 18th International Conference on World Wide Web (WWW’09). New York, USA: ACM, 2009: 791–800 |
15 | KRISHNA O, AIZAWA K. Billboard saliency detection in street videos for adults and elderly [C]//25th IEEE International Conference on Image Processing (ICIP). Athens, Greece: IEEE, 2018: 2326–2330 |
16 |
AN T T, CHANG C P, LI Y H, et al. Fog computing architecture⁃based Wi⁃Fi union mechanism for Internet advertising system [C]//International Conference on Applied System Innovation (ICASI). Sapporo, Japan: IEEE, 2017: 1024–1027. DOI: 10.1109/ICASI.2017.7988110
doi: 10.1109/ICASI.2017.7988110 |
17 |
ZHANG Y P, LI Y C, BAO Z F, et al. Optimizing impression counts for outdoor advertising [C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM, 2019: 1205–1215. DOI: 10.1145/3292500.3330829
doi: 10.1145/3292500.3330829 |
18 |
ARIYA SANJAYA I M, SUPANGKAT S H, SEMBIRING J. Citizen reporting through mobile crowdsensing: a smart city case of bekasi [C]//International Conference on ICT for Smart Society (ICISS). Semarang, Indonesia: IEEE, 2018: 1–4. DOI: 10.1109/ICTSS.2018.8549976
doi: 10.1109/ICTSS.2018.8549976 |
19 |
CHEUNG M H, HOU F, HUANG J W. Delay⁃sensitive mobile crowdsensing: Algorithm design and economics [J]. IEEE transactions on mobile computing, 2018, 17(12): 2761–2774. DOI: 10.1109/TMC.2018.2815694
doi: 10.1109/TMC.2018.2815694 |
20 |
CAO B, XIA S C, HAN J W, et al. A distributed game methodology for crowdsensing in uncertain wireless scenario [J]. IEEE transactions on mobile computing, 2020, 19(1): 15–28. DOI: 10.1109/TMC.2019.2892953
doi: 10.1109/TMC.2019.2892953 |
21 |
GONG W, ZHANG B X, LI C. Location⁃based online task assignment and path planning for mobile crowdsensing [J]. IEEE transactions on vehicular technology, 2019, 68(2): 1772–1783. DOI: 10.1109/TVT.2018.2884318
doi: 10.1109/TVT.2018.2884318 |
22 |
MARJANOVIĆ M, ANTONIĆ A, ŽARKO I P. Edge computing architecture for mobile crowdsensing [J]. IEEE access, 2018, 6: 10662–10674. DOI: 10.1109/ACCESS.2018.2799707
doi: 10.1109/ACCESS.2018.2799707 |
23 |
ZHENG Y F, DUAN H Y, YUAN X L, et al. Privacy⁃aware and efficient mobile crowdsensing with truth discovery [J]. IEEE transactions on dependable and secure computing, 2020, 17(1): 121–133. DOI: 10.1109/TDSC.2017.2753245
doi: 10.1109/TDSC.2017.2753245 |
24 |
WANG L, YU Z W, ZHANG D Q, et al. Heterogeneous multi⁃task assignment in mobile crowdsensing using spatiotemporal correlation [J]. IEEE transactions on mobile computing, 2019, 18(1): 84–97. DOI: 10.1109/TMC.2018.2827375
doi: 10.1109/TMC.2018.2827375 |
25 |
WANG J T, WANG Y S, ZHANG D Q, et al. PSAllocator: multi⁃task allocation for participatory sensing with sensing capability constraints [C]//Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. New York, USA: ACM, 2017: 1139–1151. DOI: 10.1145/2998181.2998193
doi: 10.1145/2998181.2998193 |
26 | LIN M, W⁃J HSU, LEE Z Q. Predictability of individuals’ mobility with high⁃resolution positioning data [C]//Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp ’12). New York, USA: ACM, 2012: 381–390 |
27 |
WANG E, YANG Y J, WU J, et al. An efficient prediction⁃based user recruitment for mobile crowdsensing [J]. IEEE transactions on mobile computing, 2018, 17(1): 16–28. DOI: 10.1109/TMC.2017.2702613
doi: 10.1109/TMC.2017.2702613 |
28 |
YUAN Q, CARDEI I, WU J. An efficient prediction⁃based routing in disruption⁃tolerant networks [J]. IEEE transactions on parallel and distributed systems, 2012, 23(1): 19–31. DOI: 10.1109/TPDS.2011.140
doi: 10.1109/TPDS.2011.140 |
29 |
YANG Y J, LIU W B, WANG E, et al. A prediction⁃based user selection framework for heterogeneous mobile Crowdsensing [J]. IEEE transactions on mobile computing, 2019, 18(11): 2460–2473. DOI: 10.1109/TMC.2018.2879098
doi: 10.1109/TMC.2018.2879098 |
30 |
YANG Y J, XU Y B, WANG E, et al. Exploring influence maximization in online and offline double⁃layer propagation scheme [J]. Information sciences, 2018, 450: 182–199. DOI: 10.1016/j.ins.2018.03.048
doi: 10.1016/j.ins.2018.03.048 |
31 |
KHULLER S, MOSS A, NAOR J S. The budgeted maximum coverage problem [J]. Information processing letters, 1999, 70(1): 39–45. DOI: 10.1016/s0020-0190(99)00031-9
doi: 10.1016/s0020-0190(99)00031-9 |
[1] | LIU Junyu, YANG Yongjian, WANG En. BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing [J]. ZTE Communications, 2021, 19(2): 20-28. |
[2] | LIAO Lingxia, Victor C. M. Leung, LAI Chin-Feng. Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues [J]. ZTE Communications, 2017, 15(3): 20-36. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||