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Quantum enhanced optical sensors in data optimization for huge communication network

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Abstract

The Internet of Things (IoT)-based limited WSN (wireless sensor network) has garnered much interest and advancement over the past several years to enhance resource utilization and service delivery. IoT demands a more robust network for communication and an appropriate location for an energy-efficient WSN for data transport across heterogeneous devices. In addition to the large area that needs to be covered and the restricted communication range of the sensors, WSNs with a single sink may not be appropriate in applications like smart cities. Therefore, Multi-sink WSN solutions appear appropriate for these kinds of applications. Although they boost network speed, the lifespan of the network, and energy usage, multi-sink WSNs are becoming more common. This study leverages deep learning architectures to create a unique resource allocation approach for wireless sensor IoT networks that is both energy-efficient and data-optimized. In this work, the integration of the network’s energy efficiency, data optimization and optical wireless communication is processed. In this paper, we present Modified Bat for Node Optimization, a novel approach that uses reinforcement-based Q-learning approaches to improve energy efficiency and resource allocation in Multi-sink Wireless Sensor Networks (WSNs). Additionally, routing with multiple hops is necessary for the WSNS to gather information from sensor nodes and transmit it to the sinking node for decision-making. A reliable and energy-efficient way of communicating data between sensor nodes is provided by Optical Wireless Communication, which leverages RF technology. The best power distribution and relay selection are accomplished using the suggested strategy. Lowering total power transmission and achieving Quality of Service requirements improve resource allocation and relay selection. The simulation outcomes show that the proposed model outperforms traditional ones regarding throughput, energy efficiency, quality of service, frequency efficiency, and network longevity.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Research of Technological Important Programs in the city of Lüliang, China (No. 2022GXYF18); Teaching Reform and Innovation Project of Higher Education Department of Shanxi Province, No. J20221157.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Yuncheng Advanced Normal College, Yuncheng, 044029, Shanxi, China

    Lin’e Gao

  2. Department of Computer Science and Technology, Lvliang University, Lvliang, 033000, Shanxi, China

    Yahui Nan

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  1. Lin’e Gao
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Contributions

LG: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing. YN: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

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Correspondence to Yahui Nan.

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Gao, L., Nan, Y. Quantum enhanced optical sensors in data optimization for huge communication network. Opt Quant Electron 56, 422 (2024). https://doi.org/10.1007/s11082-023-06064-1

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