Skip to main content
SpringerLink
Log in

Joint task assignment and path planning for truck and drones in mobile crowdsensing

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Utilization of drones (also known as unmanned aerial vehicles) can largely improve the performance of mobile crowdsensing (MCS) such that the drones can serve as mobile participants for task executions and therefore increase the service range. In this paper, we introduce a truck-drones MCS model by jointly considering the use of truck and drones for task executions. In this model, drones are scheduled to launch from the truck, perform one or more tasks, and rendezvous with the truck for battery swap while the truck can simultaneously serve as a mobile hub and also a mobile task executor. The design objective is to minimize the total cost for the truck and drones’ movement, as well as the driver’s payment in the MCS process. This problem is formulated as a mixed integer linear programming problem. We further propose an efficient algorithm based on variable neighborhood search to achieve efficient joint task assignment and path planning. Numerical results show the high efficacy of the proposed algorithm and also the benefit of joint use of truck and drones for mobile crowdsensing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Gong W, Zhang B, Li C (2018) Task assignment in mobile crowdsensing: Present and future directions. IEEE Netw 32(4):100–107

    Article  Google Scholar 

  2. Gong W, Zhang B, Li C (2019) Location-based online task assignment and path planning for mobile crowdsensing. IEEE Trans Veh Technol 68(2):1772–1783

    Article  Google Scholar 

  3. Ji G, Yao Z, Zhang B, Li C (2022) Quality-driven online task-bundling-based incentive mechanism for mobile crowdsensing. IEEE Trans Veh Technol 71(7):7876–7889

    Article  Google Scholar 

  4. Otto A, Agatz N, Campbell J, Golden B, Pesch E (2018) Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks 72(4):411–458

    Article  MathSciNet  Google Scholar 

  5. Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P (2019) A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE Commun Surv Tutorials 21(3):2419–2465

    Article  Google Scholar 

  6. Wang Z, Zhang B, Li C (2021) Joint path planning of truck and drones for mobile crowdsensing: Model and algorithm. Proceedings of IEEE GLOBECOM 2021:1–6

    Google Scholar 

  7. Avellar GS, Pereira GA, Pimenta LC, Iscold P (2015) Multi-UAV routing for area coverage and remote sensing with minimum time. Sensors 15(11):27783–27803

    Article  Google Scholar 

  8. Jaimes LG, Calderon JM (2020) An UAV-based incentive mechanism for crowdsensing with budget constraints. Proceedings of IEEE CCNC 2020:1–6

    Google Scholar 

  9. Zhang B, Liu CH, Tang J, Xu Z, Ma J, Wang W (2017) Learning-based energy-efficient data collection by unmanned vehicles in smart cities. IEEE Trans Industr Inf 14(4):1666–1676

    Article  Google Scholar 

  10. Liu CH, Piao C, Tang J (2020) Energy-efficient UAV crowdsensing with multiple charging stations by deep learning. Proceedings of IEEE INFOCOM 2020:199–208

    Google Scholar 

  11. Zhou Z, Feng J, Gu B, Ai B, Mumtaz S, Rodriguez J, Guizani M (2018) When mobile crowd sensing meets UAV: Energy-efficient task assignment and route planning. IEEE Trans Commun 66(11):5526–5538

    Article  Google Scholar 

  12. Barka E, Kerrache CA, Lagraa N, Lakas A (2018) Behavior-aware UAV-assisted crowd sensing technique for urban vehicular environments. Proceedings of IEEE CCNC 2018:1–7

    Google Scholar 

  13. Wang B, Sun Y, Liu D, Nguyen HM, Duong TQ (2019a) Social-aware UAV-assisted mobile crowd sensing in stochastic and dynamic environments for disaster relief networks. IEEE Trans Veh Technol 69(1):1070–1074

  14. Chung SH, Sah B, Lee J (2020) Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. Comput Oper Res 123:105004

    Article  MathSciNet  MATH  Google Scholar 

  15. Murray CC, Chu AG (2015) The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies 54:86–109

    Article  Google Scholar 

  16. Kitjacharoenchai P, Ventresca M, Moshref-Javadi M, Lee S, Tanchoco JM, Brunese PA (2019) Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. Comput Ind Eng 129:14–30

    Article  Google Scholar 

  17. Kitjacharoenchai P, Min BC, Lee S (2020) Two echelon vehicle routing problem with drones in last mile delivery. Int J Prod Econ 225:107598

    Article  Google Scholar 

  18. Murray CC, Raj R (2020) The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones. Transportation Research Part C: Emerging Technologies 110:368–398

    Article  Google Scholar 

  19. Ha QM, Deville Y, Pham QD, Hà MH (2018) On the min-cost traveling salesman problem with drone. Transportation Research Part C: Emerging Technologies 86:597–621

    Article  Google Scholar 

  20. Moshref-Javadi M, Hemmati A, Winkenbach M (2020) A truck and drones model for last-mile delivery: A mathematical model and heuristic approach. Appl Math Model 80:290–318

    Article  MathSciNet  MATH  Google Scholar 

  21. Das DN, Sewani R, Wang J, Tiwari MK (2020) Synchronized truck and drone routing in package delivery logistics. IEEE Trans Intell Transp Syst

  22. Wang D, Hu P, Du J, Zhou P, Deng T, Hu M (2019b) Routing and scheduling for hybrid truck-drone collaborative parcel delivery with independent and truck-carried drones. IEEE Internet Things J 6(6):10483–10495

  23. Yao J, Ansari N (2020) Online task allocation and flying control in fog-aided Internet of drones. IEEE Trans Veh Technol 69(5):5562–5569

    Article  Google Scholar 

  24. Sawik T (2016) A note on the Miller-Tucker-Zemlin model for the asymmetric traveling salesman problem. Bulletin of the Polish Academy of Sciences Technical Sciences 64(3)

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61872331, 61531006, 61471339, the Natural Sciences and Engineering Research Council (NSERC) of Canada (Discovery Grant RGPIN-2018-03792), and the InnovateNL SensorTECH Grant 5404-2061-101. An early version of this paper has been presented at IEEE GLOBECOM 2021.

Author information

Authors and Affiliations

  1. Research Center of Ubiquitous Sensor Networks, University of Chinese Academy of Sciences, Beijing, 100049, China

    Zijia Wang & Baoxian Zhang

  2. Information and Communication Department, Army Academy of Armored Forces, Beijing, 100072, China

    Yangxia Xiang

  3. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Cheng Li

Authors
  1. Zijia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baoxian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yangxia Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baoxian Zhang.

Ethics declarations

Conflict of interest/competing interest statement

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Zhang, B., Xiang, Y. et al. Joint task assignment and path planning for truck and drones in mobile crowdsensing. Peer-to-Peer Netw. Appl. 16, 1668–1679 (2023). https://doi.org/10.1007/s12083-022-01389-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-022-01389-2

Keywords

Search

Navigation