IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i5p1550147720907052.html
   My bibliography  Save this article

Heuristic mobile data gathering for wireless sensor networks via trajectory control

Author

Listed:
  • Jianxin Ma
  • Shuo Shi
  • Xuemai Gu
  • Fanggang Wang

Abstract

This article focuses on the problem of scheduling the optimal paths of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize the travel distance and balance the energy consumption and the data gathering latency in wireless sensor networks for smart cities. To partition the network for the multiple mobile elements and compute the trajectories of the multiple mobile elements, we utilize the sensor’s communication range and construct a multiple mobile elements scheduling problem. A heuristic mobile data gathering approach is proposed to solve this problem, which includes the following three steps. The sensor nodes are preliminarily partitioned into four levels, and then the clusterheads are further partitioned, and the traveling tour is scheduled for each cluster. After the first two steps, all the sensor nodes are partitioned reasonably for the multiple mobile elements. In the last step, the traveling tour is scheduled for each cluster, and the meeting point of each clusterhead is determined. We compare the proposed heuristic mobile data gathering with the existing approaches. The results indicate that the travel distance and the data gathering latency are reduced significantly, which further validates that the communication range is beneficial to minimize the travel distance.

Suggested Citation

  • Jianxin Ma & Shuo Shi & Xuemai Gu & Fanggang Wang, 2020. "Heuristic mobile data gathering for wireless sensor networks via trajectory control," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720907052
    DOI: 10.1177/1550147720907052
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720907052
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147720907052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bektas, Tolga, 2006. "The multiple traveling salesman problem: an overview of formulations and solution procedures," Omega, Elsevier, vol. 34(3), pages 209-219, June.
    2. Kai Zhou & Mustafa R. Kılınç & Xi Chen & Nikolaos V. Sahinidis, 2018. "An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver," Journal of Global Optimization, Springer, vol. 70(3), pages 497-516, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuan, Shuai & Skinner, Bradley & Huang, Shoudong & Liu, Dikai, 2013. "A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 228(1), pages 72-82.
    2. Rajeev Kumar, 2022. "A Gig Worker-Centric Approach for Efficient Picking and Delivery of Electric Scooters," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-14, January.
    3. Wex, Felix & Schryen, Guido & Feuerriegel, Stefan & Neumann, Dirk, 2014. "Emergency response in natural disaster management: Allocation and scheduling of rescue units," European Journal of Operational Research, Elsevier, vol. 235(3), pages 697-708.
    4. Sebastian Twaróg & Krzysztof Szwarc & Martyna Wronka-Pośpiech & Małgorzata Dobrowolska & Anna Urbanek, 2021. "Multiple probabilistic traveling salesman problem in the coordination of drug transportation—In the context of sustainability goals and Industry 4.0," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
    5. Semih Yalçındağ & Andrea Matta & Evren Şahin & J. George Shanthikumar, 2016. "The patient assignment problem in home health care: using a data-driven method to estimate the travel times of care givers," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 304-335, June.
    6. CASTRO, Marco & SÖRENSEN, Kenneth & VANSTEENWEGEN, Pieter & GOOS, Peter, 2012. "A simple GRASP+VND for the travelling salesperson problem with hotel selection," Working Papers 2012024, University of Antwerp, Faculty of Business and Economics.
    7. Hartmann, Sönke, 2013. "Scheduling reefer mechanics at container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 17-27.
    8. Andrzej Grzybowski, 2009. "A Note On A Single Vehicle And One Destination Routing Problem And Its Game-Theoretic Models," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 3(1), pages 71-76, December.
    9. Emde, Simon & Tahirov, Nail & Gendreau, Michel & Glock, Christoph H., 2021. "Routing automated lane-guided transport vehicles in a warehouse handling returns," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1085-1098.
    10. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Tang, Fang, 2020. "Static rebalancing optimization with considering the collection of malfunctioning bikes in free-floating bike sharing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    11. Mujawar, Sachin & Huang, Simin & Nagi, Rakesh, 2012. "Scheduling to minimize stringer utilization for continuous annealing operations," Omega, Elsevier, vol. 40(4), pages 437-444.
    12. Nourinejad, Mehdi & Zhu, Sirui & Bahrami, Sina & Roorda, Matthew J., 2015. "Vehicle relocation and staff rebalancing in one-way carsharing systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 98-113.
    13. F. Angel-Bello & Y. Cardona-Valdés & A. Álvarez, 2019. "Mixed integer formulations for the multiple minimum latency problem," Operational Research, Springer, vol. 19(2), pages 369-398, June.
    14. Haluk Yapicioglu, 2018. "Multiperiod Multi Traveling Salesmen Problem Considering Time Window Constraints with an Application to a Real World Case," Networks and Spatial Economics, Springer, vol. 18(4), pages 773-801, December.
    15. Peter Reiter & Walter Gutjahr, 2012. "Exact hybrid algorithms for solving a bi-objective vehicle routing problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 19-43, March.
    16. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    17. Mohamed Cissé & Semih Yalçindag & Yannick Kergosien & Evren Sahin & Christophe Lenté & Andrea Matta, 2017. "OR problems related to Home Health Care: A review of relevant routing and scheduling problems," Post-Print hal-01736714, HAL.
    18. Tamás Kalmár-Nagy & Giovanni Giardini & Bendegúz Dezső Bak, 2017. "The Multiagent Planning Problem," Complexity, Hindawi, vol. 2017, pages 1-12, February.
    19. N. A. Arellano-Arriaga & J. Molina & S. E. Schaeffer & A. M. Álvarez-Socarrás & I. A. Martínez-Salazar, 2019. "A bi-objective study of the minimum latency problem," Journal of Heuristics, Springer, vol. 25(3), pages 431-454, June.
    20. Day, Jamison M. & Daniel Wright, P. & Schoenherr, Tobias & Venkataramanan, Munirpallam & Gaudette, Kevin, 2009. "Improving routing and scheduling decisions at a distributor of industrial gasses," Omega, Elsevier, vol. 37(1), pages 227-237, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720907052. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.