[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT’s device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution’s core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture’s optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Li Y, Aghvami AH (2023) Radio resource management for cellular-connected UAV: a learning approach. IEEE Trans Commun. https://doi.org/10.1109/TCOMM.2023.3262826

    Article  Google Scholar 

  2. Wang X, Zhang Y, Shen R, Xu Y, Zheng FC (2020) DRL-based energy-efficient resource allocation frameworks for uplink NOMA systems. IEEE Internet Things J 7(8):7279–7294. https://doi.org/10.1109/JIOT.2020.2982699

    Article  Google Scholar 

  3. Wang Y, Zhou L, Yang G, Guo R, Xia C, Liu Y (2020) Performance and obstacle tracking to natural forest resource protection project: a rangers’ case of Qilian mountain, China. Int J Environ Res Public Health 17(16):5672. https://doi.org/10.3390/ijerph17165672

    Article  Google Scholar 

  4. Lee M, Molisch AF (2018) Caching policy and cooperation distance design for base station-assisted wireless D2D Caching networks: throughput and energy efficiency optimization and tradeoff. IEEE Trans Wireless Commun 17:7500–7514. https://doi.org/10.1109/TWC.2018.2867596

    Article  Google Scholar 

  5. Si G, Xia T, Zhang K, Wang D, Pan E, Xi L (2021) Technician collaboration and routing optimization in global maintenance scheduling for multi-center service networks. IEEE Trans Autom Sci Eng 19(3):1542–1554. https://doi.org/10.1109/TASE.2021.3132694

    Article  Google Scholar 

  6. Lu B, Lin S, Shi J, Wang Y (2019) Resource allocation for D2D communications underlaying cellular networks over Nakagami-$ m $ fading channel. IEEE Access 7:21816–21825. https://doi.org/10.1109/ACCESS.2019.2894721

    Article  Google Scholar 

  7. An X, Fan R, Hu H, Zhang N, Atapattu S, Tsiftsis TA (2022) Joint task offloading and resource allocation for IoT edge computing with sequential task dependency. IEEE Internet Things J 9(17):16546–16561. https://doi.org/10.1109/JIOT.2022.3150976

    Article  Google Scholar 

  8. Sun Y, Xu D, Ng DWK, Dai L, Schober R (2019) Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems. IEEE Trans Commun 67(6):4281–4298. https://doi.org/10.1109/TCOMM.2019.2900630

    Article  Google Scholar 

  9. Chen X, Wu C, Chen T, Zhang H, Liu Z, Zhang Y, Bennis M (2020) Age of information aware radio resource management in vehicular networks: a proactive deep reinforcement learning perspective. IEEE Trans Wireless Commun 19(4):2268–2281. https://doi.org/10.1109/TWC.2019.2963667

    Article  Google Scholar 

  10. Lan Y, Wang X, Wang D, Liu Z, Zhang Y (2019) Task caching, offloading, and resource allocation in D2D-aided fog computing networks. IEEE Access 7:104876–104891. https://doi.org/10.1109/ACCESS.2019.2929075

    Article  Google Scholar 

  11. Xu D, Sun Y, Ng DWK, Schober R (2020) Multiuser MISO UAV communications in uncertain environments with no-fly zones: robust trajectory and resource allocation design. IEEE Trans Commun 68(5):3153–3172. https://doi.org/10.1109/TCOMM.2020.2970043

    Article  Google Scholar 

  12. Zhong R, Liu X, Liu Y, Chen Y, Wang X (2022) Path design and resource management for NOMA enhanced indoor intelligent robots. IEEE Trans Wireless Commun 21(10):8007–8021. https://doi.org/10.1109/TWC.2022.3163422

    Article  Google Scholar 

  13. Li Z, Wang Y, Liu M, Sun R, Chen Y, Yuan J, Li J (2019) Energy efficient resource allocation for UAV-assisted space-air-ground internet of remote things networks. IEEE Access 7:145348–145362. https://doi.org/10.1109/ACCESS.2019.2945478

    Article  Google Scholar 

  14. Dong X, Guo K, Xue G, Yang Y, Xie W, Liu C (2023) Environmental regulation, resource misallocation, and total factor productivity: an empirical analysis based on 284 cities at the prefecture-level and above in China. Int J Environ Res Public Health 20(1):854. https://doi.org/10.3390/ijerph20010854

    Article  Google Scholar 

  15. Wang J, Huang Y, Jin S, Schober R, You X, Zhao C (2018) Resource management for device-to-device communication: a physical layer security perspective. IEEE J Sel Areas Commun 36(4):946–960. https://doi.org/10.1109/JSAC.2018.2825484

    Article  Google Scholar 

  16. Singh K, Wang K, Biswas S, Ding Z, Khan FA, Ratnarajah T (2019) Resource optimization in full duplex non-orthogonal multiple access systems. IEEE Trans Wireless Commun 18(9):4312–4325. https://doi.org/10.1109/TWC.2019.2923172

    Article  Google Scholar 

  17. Zhang Y, Bai K, Pang L, Han R, Li Y, Liang S, ..., Ren G (2018) Multi-dimensional resource optimization for incremental AF-OFDM systems with RF energy harvesting relay. IEEE Trans Veh Technol 68(1):613–627. https://doi.org/10.1109/TVT.2018.2882910

  18. Cao L, Roy S, Yin H (2022) Resource allocation in 5G Platoon Communication: modeling, analysis and optimization. IEEE Trans Veh Technol 72(4):5035–5048. https://doi.org/10.1109/TVT.2022.3223351

    Article  Google Scholar 

  19. Ermoliev Y, Zagorodny AG, Bogdanov VL, Ermolieva T, Havlik P, Rovenskaya E, Obersteiner M (2022) Linking distributed optimization models for food, water, and energy security nexus management. Sustainability 14(3):1255. https://doi.org/10.3390/su14031255

    Article  Google Scholar 

  20. Chen Z, Fang S, Tian Z, Wang M, Jia Y (2022) Resource allocation for OFDM-Based status update systems: a timeliness perspective. IEEE Trans Veh Technol 72(3):3691–3706. https://doi.org/10.1109/TVT.2022.3221452

    Article  Google Scholar 

  21. Peng H, Shen X (2021) Multi-agent reinforcement learning based resource management in MEC- and UAV-Assisted vehicular networks. IEEE J Sel Areas Commun 39:131–141. https://doi.org/10.1109/JSAC.2020.3036962

    Article  Google Scholar 

  22. Zhou S, Cheng Y, Lei X, Peng Q, Wang J, Li S (2022) Resource allocation in UAV-assisted networks: a clustering-aided reinforcement learning approach. IEEE Trans Veh Technol 71(11):12088–12103. https://doi.org/10.1109/TVT.2022.3189552

    Article  Google Scholar 

  23. Gao Z, Eisen M, Ribeiro A (2021) Resource allocation via model-free deep learning in free space optical communications. IEEE Trans Commun 70(2):920–934. https://doi.org/10.1109/TCOMM.2021.3129199

    Article  Google Scholar 

  24. Zhang H, Yang Y, Shang B, Zhang P (2022) Joint resource allocation and multi-part collaborative task offloading in MEC systems. IEEE Trans Veh Technol 71(8):8877–8890. https://doi.org/10.1109/TVT.2022.3174530

    Article  Google Scholar 

  25. Li X, Fan R, Hu H, Zhang N (2022) Joint task offloading and resource allocation for cooperative mobile-edge computing under sequential task dependency. IEEE Internet Things J 9(23):24009–24029. https://doi.org/10.1109/JIOT.2022.3188933

    Article  Google Scholar 

  26. Feng C, Shen Z, Yang Q, Wu W (2022) Two-stage task offloading optimization with large deviation delay analysis in IoT networks. IEEE Trans Commun 70(3):1834–1847. https://doi.org/10.1109/TCOMM.2022.3142284

    Article  Google Scholar 

  27. Wu C, Mu X, Liu Y, Gu X, Wang X (2022) Resource allocation in STAR-RIS-aided networks: OMA and NOMA. IEEE Trans Wireless Commun 21(9):7653–7667. https://doi.org/10.1109/TWC.2022.3160151

    Article  Google Scholar 

  28. Chen R, Chen J, Wang H, Tong X, Xu Y, Qi N, Xu Y (2022) Joint channel access and power control optimization in large-scale UAV networks: a hierarchical mean field game approach. IEEE Trans Veh Technol 72(2):1982–1996. https://doi.org/10.1109/TVT.2022.3210287

    Article  Google Scholar 

  29. Zhang Y, Zhao X, Zhou Z, Qin P, Geng S, Xu C, Yang L (2021) Robust resource allocation for lightweight secure transmission in multicarrier NOMA-assisted full duplex IoT networks. IEEE Internet Things J 9(9):6443–6457. https://doi.org/10.1109/JIOT.2021.3110974

    Article  Google Scholar 

  30. Zhang T, Zhu K, Wang J, Han Z (2021) Cost-efficient beam management and resource allocation in millimeter wave backhaul HetNets with hybrid energy supply. IEEE Trans Wireless Commun 21(5):3291–3306. https://doi.org/10.1109/TWC.2021.3120266

    Article  Google Scholar 

  31. Gupta S, Patel N, Kumar A, Jain NK, Dass P, Hegde R, Rajaram A (Preprint) Adaptive fuzzy convolutional neural network for medical image classification. J Intell Fuzzy Syst (Preprint): 1–17. https://doi.org/10.3233/JIFS-233819

  32. Hong C, Yu J, Zhang J, Jin X, Lee KH (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Industr Inform 15(7):3952–3961. https://doi.org/10.1109/TII.2018.2884211

    Article  Google Scholar 

  33. Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779. https://doi.org/10.1109/TCYB.2014.2336697

    Article  Google Scholar 

  34. Yu J, Tan M, Zhang H, Rui Y, Tao D (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell 44(2):563–578. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  35. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670. https://doi.org/10.1109/TIP.2015.2487860

    Article  MathSciNet  Google Scholar 

  36. Hong C, Yu J, Chen X (2013) Image-based 3D human pose recovery with locality sensitive sparse retrieval. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, pp 2103–2108. https://doi.org/10.1109/TIE.2014.2378735

Download references

Acknowledgements

There is no acknowledgement involved in this work.

Funding

No funding is involved in this work.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering , Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India

    Shivani Gupta

  2. Assistant Professor (SG), Computer Science and Engineering , Jaypee University of Engineering and Technology, Guna, India

    Nileshkumar Patel, Ajay Kumar & Neelesh Kumar Jain

  3. Assistant Professor (SG), Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India

    Pranav Dass

  4. Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, Karnataka, 574110, India

    Rajalaxmi Hegde

  5. Department of Electronics and Communication Engineering, EGS, Pillay Engineering College, Nagapattinam, India

    A. Rajaram

Authors
  1. Shivani Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nileshkumar Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ajay Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neelesh Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pranav Dass
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rajalaxmi Hegde
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A. Rajaram
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors are contributed equally to this work.

Corresponding author

Correspondence to Shivani Gupta.

Ethics declarations

Ethics approval and consent to participate

No participation of humans takes place in this implementation process.

Human and animal rights

No violation of Human and Animal Rights is involved.

Conflict of interest

Conflict of Interest is not applicable in this work.

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

Gupta, S., Patel, N., Kumar, A. et al. Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices. Multimed Tools Appl 83, 82343–82367 (2024). https://doi.org/10.1007/s11042-024-18176-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18176-1

Keywords

Search

Navigation