Improving Solar Energy-Harvesting Wireless Sensor Network (SEH-WSN) with Hybrid Li-Fi/Wi-Fi, Integrating Markov Model, Sleep Scheduling, and Smart Switching Algorithms

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes.

Authors

Institutions

Publication Details

Journal
Technologies
Published
2025-09-29
DOI
https://doi.org/10.3390/technologies13100437
Citations
2
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving Solar Energy-Harvesting Wireless Sensor Network (SEH-WSN) with Hybrid Li-Fi/Wi-Fi, Integrating Markov Model, Sleep Scheduling, and Smart Switching Algorithms

Heba Allah Gamal, Ayman Haggag, Ali M. El‐Rifaie, Ahmed A. F. Youssef et al.
2 citations
Technologies
Energy Harvesting in Wireless Networks
1.46
article

Improving Solar Energy-Harvesting Wireless Sensor Network (SEH-WSN) with Hybrid Li-Fi/Wi-Fi, Integrating Markov Model, Sleep Scheduling, and Smart Switching Algorithms

Heba Allah Gamal, Ayman Haggag, Ali M. El‐Rifaie, Ahmed A. F. Youssef, Hisham A. Hamad, Mostafa A. R. Eltokhy
article en
2 citations

Abstract

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes.

TechnologiesVol. 13(10)
American University of the Middle East (KW), Helwan University (EG)
Openalex Percentile: Top 15%
Energy Harvesting in Wireless Networks
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.