Skip to main content

Advertisement

SpringerLink
Log in

AI- and IoT-based energy saving mechanism by minimizing hop delay in multi-hop and advanced optical system based optical channels

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

Abstract

Renewable and energy sustainable solutions are the need of the hour in this current era where energy is needed for every single and small task but energy generation resources are limited. The emergence of 6G, IoT (Internet of Things), smart technologies and AI (artificial intelligence) has made it possible to integrate renewable energy solutions to save energy and to preserve green environments by saving energy generation resources. The data are generated from diverse nodes and geographical locations in large amounts, and it is very challenging to manage the transition states of data. Optical fibers and optical channels play an important role in handling the flow of massive data. Innovative solutions are required to handle the transition of data by saving energy optimally. Energy is life, and saving energy is as important as saving life. All the newer technologies are thriving for minimal consumption of energy by exploiting smart solutions and with the advent of renewable solutions for energy generation. In many application scenarios, data must be transmitted to a sink/server within a short span of time as soon as the event takes place. An inter-hop minimal latency is required when data are transferred between hops in a green multi-hop cognitive radio network (GM-CRN), which requires innovative solutions to transmit the data over optical channels by consuming minimal energy. This article focuses on the integration of renewable energy resources in multi-hop optical channels, which not only helps save energy but also optimizes the data transmission rate, throughput and transmission latency. The management of renewable energy generation resources along with inter-hop delay is still a challenging task and this article attempts to provide a solution to address this problem. This paper suggests a mechanism that ensures minimal inter-hop delay and interference avoidance to optimize network utility. The sink allocates licensed channels to the IoT devices with energy harvesting (EH) capability for data transmission by considering the network utility, energy harvesting, and sustainability of energy. The research in this article further investigates a utility optimization and minimal delay problem, which can be decomposed into four deterministic sub-problems, i.e., the device battery management (DBM) problem, data discard control (DDC) problem, sampling rate control (SRC) problem, and interference-free channel allocation (ICA) problem with the aid of Lyapunov Optimization techniques. Finally, an efficient online algorithm is also devised to optimize the network utility by minimal allocation of power to sink nodes without hampering the data transmission speed on optical channels. The results of the proposed mechanism are evaluated by using the standard statistical parameters in a simulated environment, and the proposed mechanism is compared with state-of-the-art techniques to assess the viability of the proposed approach for advanced optical technologies (AOT).

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

Similar content being viewed by others

Data availability

Data can be shared with the researchers on a valid request through their institutional email address.

References

  • Ahmed, I., Iqbal, M.A., Kim, K.: Cooperative energy-efficient communication protocol for energy harvesting wireless sensor networks. IEEE Trans. on Green Comm. and Net. 3(2), 518–532 (2019)

    Google Scholar 

  • Banerjee, A., Paul, A., Maity, S.P.: Joint power allocation and route selection for outage minimization in multihop cognitive radio networks with energy harvesting. IEEE Trans. Cognit. Comm. Networking 4(1), 82–92 (2018)

    Article  Google Scholar 

  • Gao, W., Chen, J., Wang, J., et al.: Energy-efficient power control for multi-user D2D communications with QoS constraints. IEEE Trans. on Comm. 70(12), 4912–4926 (2022)

    Google Scholar 

  • Jiang, Q., Li, Z., Wang, Y., Li, W.: Energy-efficient resource allocation for wireless powered communication networks with power sharing. IEEE Trans. on Comm. 69(1), 473–486 (2021)

    Google Scholar 

  • Kaur, M., Jadhav, A., Akter, F.: Resource selection from edge-cloud for IIoT and blockchain-based applications in industry 4.0/5.0. Sec. Comm. Networks 2022, 1–10 (2022a). https://doi.org/10.1155/2022/9314052

    Article  Google Scholar 

  • Kaur, M., Kadam, S., Hannoon,: N. Multi-level parallel scheduling of dependent-tasks using graph-partitioning and hybrid approaches over edge-cloud. Soft Comput 26, 5347–5362 (2022b)

    Article  Google Scholar 

  • Khan, M.I., et al.: Energy-efficient offloading scheme for multi-user IoT networks with varying QoS requirements. Wireless per. Comm. 130(3), 2123–2143 (2023)

    Google Scholar 

  • Lee, H.G., Lee, Y., Kang, M.: Latency- and energy-aware resource allocation for multi-user MEC networks with energy harvesting. IEEE Comm. Letters 26(11), 2273–2277 (2022)

    Google Scholar 

  • Ren, J., Sun, Y., Li, X., et al.: Dynamic channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Trans. on Wireless Comm. 15(5), 3143–3156 (2016)

    Article  Google Scholar 

  • Wang, N., Li, J., Li, Q., Li, Z.: Joint Energy-Efficient Resource Allocation and Data Collection in Energy Harvesting-Based IoT Networks. IEEE Int. of Things J. 8(1), 53–63 (2021)

    Google Scholar 

  • Wu, X., Liu, X., Zhao, Q., et al.: Energy-efficient resource allocation in hybrid energy harvesting wireless communication systems. IEEE Trans. on Comm. 68(11), 7081–7093 (2020)

    Google Scholar 

  • Wu, R., et al.: Energy-Efficient Beamforming Design for Secure Massive MIMO Systems with Wireless Information and Power Transfer. Wireless Pers. Commun. 128(3), 1927–1946 (2022)

    Google Scholar 

  • Xu, X., Zhang, Y., Wang, J., et al.: On the secure spectral-energy efficiency tradeoff in random cognitive radio networks. IEEE J. Sel. Areas Comm. 34(10), 2706–2722 (2016)

    Article  Google Scholar 

  • Xu, C., Yang, K., Zhou, Y., et al.: End-to-end throughput maximization for underlay multi-hop cognitive radio networks with RF energy harvesting. IEEE Trans. on Wireless Comm. 16(6), 3561–3572 (2017)

    Article  Google Scholar 

  • Yang, S.Y., Chen, X.: Multi-Objective Optimization for Energy-Efficient Scheduling in Industrial IoT. IEEE Trans. on Ind. Infor. 17(6), 4157–4166 (2021)

    Google Scholar 

  • Zhang, D., Zhang, S., Duan, L., et al.: Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE J. Sel. Areas Comm. 34(12), 3552–3565 (2016)

    Article  Google Scholar 

  • Zhang, D., Qiao, Y., She, L., Shen, R., Ren, J., Zhang, Y.: Two time-scale resource management for green internet of things networks. IEEE Internet Things J. 6(1), 545–556 (2018a)

    Article  Google Scholar 

  • Zhang, D., Qiao, Y., She, L., et al.: Two time-scale resource management for green internet of things networks. IEEE Int. of Things J. 6(1), 545–556 (2018b)

    Article  Google Scholar 

  • Zhang, J., Zhu, P., Li, J., He, Y.: Energy-efficient cooperative relaying for optical wireless networks with energy harvesting constraints. IEEE Trans. on Wireless Comm. 20(2), 1222–1235 (2021a)

    Google Scholar 

  • Zhang, S., Chen, X., Chen, W.: Joint optimization of energy consumption and quality-of-service for D2D communications underlaying cellular networks. IEEE Trans. on Veh. Tech. 70(7), 7145–7156 (2021b)

    Google Scholar 

  • Zhang, K., Zhang, Y., Liu, X.: Energy efficiency optimization in wireless-powered backscatter communication networks. IEEE Trans. on Comm. 71(1), 86–99 (2023)

    Google Scholar 

  • Zheng, M., Saad, W., Han, Z., et al.: Energy-efficiency maximization for cooperative spectrum sensing in cognitive sensor networks. IEEE Trans. Green Comm. Net. 1(1), 29–39 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Savitribai Phule Pune University, Pune, India

    Mandeep Kaur

  2. Aspire Research Foundation, Pune, India

    Mandeep Kaur

Authors
  1. Mandeep Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mandeep Kaur.

Ethics declarations

Conflict of interest

None.

Ethical approval

Not applicable for this research study.

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

Kaur, M. AI- and IoT-based energy saving mechanism by minimizing hop delay in multi-hop and advanced optical system based optical channels. Opt Quant Electron 55, 635 (2023). https://doi.org/10.1007/s11082-023-04882-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11082-023-04882-x

Keywords

Search

Navigation