IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01102-1.html
   My bibliography  Save this article

Data allocation optimization for sensor information of internet of things

Author

Listed:
  • Shi Wang

    (Harbin University of Commerce)

Abstract

As an emerging hot technology at home and abroad, the Internet of Things combines data characteristics with the advantages of distributed real-time database information storage management, and data distribution strategy as the key technology of data storage scheme is the focus of research. According to the mass, spatial–temporal correlation, access imbalance and continuous variability of sensor information in the Internet of Things, a time-domain based data allocation model is needed to adapt to it, so as to design a dynamic data allocation strategy based on adaptive time-domain load feedback. According to the data characteristics, the static data distribution problem is reduced to a simple linear programming problem, and the adaptive time domain is used to feedback the load information. Finally, the dynamic load threshold function is set to realize the dynamic data distribution. For the allocation of global scalar data, this paper uses integer linear programming for modeling, and proposes a global data allocation algorithm (GDP) based on RODP algorithm. GDP algorithm can quickly solve the allocation problem of scalar data in the whole program within the polynomial time complexity. Finally, the numerical experiments in the program cycle body show that the proposed strategy has better performance in terms of short time domain load balancing and system data migration than similar algorithms. Simulation experiments are carried out on two sets of benchmark programs respectively. The experimental results show that the global data allocation algorithm and the iterative optimal data allocation algorithm proposed in this paper are superior to the greedy strategy based data allocation algorithm in terms of access delay and energy consumption for all the test programs.

Suggested Citation

  • Shi Wang, 2021. "Data allocation optimization for sensor information of internet of things," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 790-800, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01102-1
    DOI: 10.1007/s13198-021-01102-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01102-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01102-1?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kexin Bi & Kwangil An & Xiang Li, 2020. "A Resource Optimization Allocation Strategy for China’s Shipbuilding Industry Green Innovation System," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-25, June.
    2. Wei Jiang & Huiqiang Wang & Bingyang Li & Haibin Lv & Qingchuan Meng, 2020. "A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477199, January.
    3. Abdu Salam & Qaisar Javaid & Masood Ahmad, 2020. "Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET," Complexity, Hindawi, vol. 2020, pages 1-20, September.
    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. Abdu Salam & Qaisar Javaid & Masood Ahmad & Ishtiaq Wahid & Muhammad Yeasir Arafat, 2023. "Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things," Future Internet, MDPI, vol. 15(8), pages 1-24, August.
    2. Abdu Salam & Qaisar Javaid & Masood Ahmad, 2021. "Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.

    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:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01102-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.