5G Technology Research Articles

5G Internet in Rural Areas: Impacts, Challenges and Sustainable Strategies for Development

Objective: This study aims to investigate the impacts, challenges, and strategies related to the implementation of 5G technology in rural areas, with the goal of promoting sustainable development and digital inclusion. The research also seeks to identify specific benefits of 5G for the agricultural sector and evaluate how its adoption can transform rural communities. Theoretical Framework: This study is based on concepts such as precision agriculture, agricultural automation, and digital inclusion, emphasizing the implications of high-speed connectivity in rural development. Theories related to the economic and social impacts of 5G technology provide a solid foundation for understanding the potential and barriers associated with its implementation. Method: The research adopted a systematic bibliographic review approach, including the selection of theoretical and empirical studies published in the last ten years. Data collection was conducted in recognized academic databases, such as Scopus, IEEE Xplore, and Web of Science, using keywords related to 5G and its application in rural areas. The analysis categorized the data into benefits, challenges, and strategies, allowing for a comparative discussion of the findings. Results and Discussion: The results revealed that, although the implementation of 5G faces significant challenges, such as insufficient infrastructure and high costs, the potential benefits are substantial. The technology can revolutionize precision agriculture through IoT sensors, drones, and artificial intelligence, increasing efficiency and sustainability in agricultural operations. Additionally, 5G's ability to drive economic development, expand digital inclusion, and improve access to essential services such as telemedicine and online education was highlighted. Research Implications: This study provides insights into how 5G technology can be applied to transform rural areas, benefiting sectors such as agriculture, healthcare, and education. The implications include strategies to overcome implementation barriers and the potential to reduce digital exclusion, promoting innovation and sustainability in rural communities. Originality/Value: The research contributes to the literature by offering a comprehensive analysis of the challenges and benefits of 5G in rural areas, highlighting its relevance for digital transformation in the agricultural sector and community development. Its originality lies in the integration of economic, social, and technological dimensions, providing practical recommendations for adopting 5G in remote regions.

Fifth-Generation (5G) Communication in Urban Environments: A Comprehensive Unmanned Aerial Vehicle Channel Model for Low-Altitude Operations in Indian Cities

Unmanned aerial vehicles (UAVs) significantly shape the evolution of 5G and 6G technologies in India, particularly in reconfiguring communication networks. Through their deployment as base stations or relays, these aerial vehicles substantially enhance communication performance and extend network coverage in areas characterized by high demand and challenging topographies. Accurate modelling of the UAV-to-ground channel is imperative for gaining valuable insights into UAV-assisted communication systems, particularly within India’s rapidly expanding metropolitan cities and their diverse topographical complexities. This study proposes an approach to model low-altitude channels in urban areas, offering specific scenarios and tailored solutions to facilitate radio frequency (RF) planning for Indian metropolitan cities. The proposed model leverages the International Telecommunication Union recommendation (ITU-R) for city mapping and utilizes frequency ranges from 1.8 to 6 GHz and altitudes up to 500 m to comprehensively model both line-of-sight (LoS) and non-line-of-sight (NLoS) communications. It employs the uniform theory of diffraction to calculate the additional path loss for non-line-of-sight (NLoS) communication for both vertical and horizontal polarizations. The normal distribution for additional shadowing loss is discerned from simulation results. This study outlined the approach to derive a comprehensive statistical channel model based on the elevation angle and evaluate model parameters at various frequencies and altitudes for both vertical and horizontal polarization. The model was subsequently compared with existing models for validation, showing close alignment. The ease of implementation and practical application of this proposed model render it an invaluable tool for planning and simulating mobile networks in urban areas, thus facilitating the seamless integration of advanced communication technologies in India.

Reconfigurable intelligent surface-aided positioning-centered multi-LED integrated visible light communication and positioning system

This paper investigates a reconfigurable intelligent surface (RIS)-aided integrated visible light communication and positioning (VLCP) system, which aims to reduce sensitivity to line-of-sight (LoS) blockage in visible light and simultaneously achieve capabilities for both visible light communication (VLC) and visible light positioning (VLP). The framework of this system is divided into three parts: positioning, channel state information (CSI) estimation, and data transmission. When the LoS links are present, due to the potential adverse positioning effects RIS caused in this scenario, the RIS only plays a role in assisting the communication. Meanwhile, the positioning process is based on the received signal strength (RSS) information from the LoS transmission. If the LoS links are blocked, the RIS provides added non-line-of-sight (NLoS) positioning signals and achieves subsidiary communications accordingly. The Cramer-Rao lower bound (CRLB) on the positioning result is derived and the CSI estimation is conducted based on the results provided by the positioning schemes to reduce the pilot overhead. The achievable rate is evaluated based on the estimated CSI. The configurations of RIS parameters and the power allocated to positioning and communication are jointly optimized to minimize the CRLB while satisfying constraints on the maximum tolerable outage probability, RIS parameter acquisition, and total transmit power. To address this non-convex optimization problem, we proposed an alternative optimization (AO) algorithm to optimize the RIS assignment matrix and the power coefficient alternatively. We employ the worst-case distribution of the Conditional Value-at-Risk (CVaR) along with successive convex approximation (SCA) techniques to derive feasible solutions of the power coefficient optimization subproblem. Meanwhile, we optimize the RIS assignment matrix based on the principle of maximum received power chosen (MRPC). Simulation results demonstrate the influence of RIS in the positioning process in our proposed VLCP system with or without the presence of LoS links, and meanwhile, the relationship between communication performance and positioning performance is validated through the simulations. From the simulation results, the RIS-aided VLCP system can be verified as a viable option for the upcoming era of sixth-generation (6G) technology.

Editorial

Welcome to the inaugural volume of Comm&Optics Connect, a multidisciplinary journal dedicated to advancing communication and networks across the entire electromagnetic spectrum. This first volume embodies our vision to provide a platform where groundbreaking research in radio, mmWave, THz, and optical technologies converges. The future of communication networks lies in the seamless integration of technologies across the electromagnetic spectrum to support ultra-high speed, ultra-low latency, and massive connectivity. However, realizing this vision requires overcoming significant challenges, such as managing the complexity of heterogeneous networks, ensuring robust security and privacy, and developing energy-efficient solutions. Comm&Optics Connect addresses these challenges by offering a platform for radio, mmWave, THz, and optical fields to share their work and collaboratively tackle these issues. The response to our call for papers reflects a strong interest in contributing to the aims and scope of the journal. The five articles featured in this volume represent the breadth and depth of research that Comm&Optics Connect aims to showcase. These articles cover critical areas such as: • Real-Time Cloud-based IoT for Energy Consumption Monitoring: Damien Campbell et al. present a cloud-based IoT platform for monitoring household electricity consumption, providing valuable insights into usage patterns and identifying opportunities for enhancing energy efficiency. This research exemplifies the journal’s focus on practical deployments. • Free Space Optical Inter-Satellite Links: Shivam Bhutiani and Nupur Shah explore the establishment of free space optical inter-satellite links beyond the Karman line. This work highlights the journal’s interest in cutting-edge communication technologies for space applications, aligning with the increasing demand for high-speed and reliable connectivity in satellite networks. • Enhancing Physical Layer Security: Shen Qian and Meng Cheng delve into physical layer security in lossy untrusted relay networks, focusing on the challenges and opportunities presented by finite blocklength transmissions. This research contributes to the journal’s commitment to exploring security and resilience in communication systems, an area of paramount importance as networks become more complex and vulnerable. • Open Fronthaul Technologies for 5G and Beyond: Hamna Shoukat et al. provide a comprehensive overview of technological trends in open fronthauls for beyond 5G and 6G networks. This work highlights the journal's emphasis on next-generation networks and the critical role of open and interoperable architectures in fostering innovation and flexibility. • Multi-Task Learning for Low-Complexity VLC Systems: Saksham Dewan and Hany Elgala explore the application of multi-task learning to reduce the complexity of visible light communication (VLC) systems. This research exemplifies the journal’s dedication to exploring novel data-driven approaches using deep learning (DL) models for enhanced efficiency and performance. As the Editor-in-Chief of Comm&Optics Connect, I am confident that the journal will become an essential resource for researchers in the field of communications and networks. None of this would be possible without the outstanding and diverse members of the editorial board, the hard-working staff at Scifiniti, and the valuable reviewers. I invite you to join us on this exciting journey as we bridge the gap between cutting-edge research and practical applications, shaping the future of a connected world. We look forward to receiving contributions from all over the world.

Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning

Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (De_Fed_L- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-means_RL). Here the experimental analysis is carried out in terms of number of users of network as well as number of edge server by DoF of 92%, Spectral efficiency of 92%, Energy efficiency of 96%, Signal to noise ratio (SNR) of 85%, Coverage area of 92%, RL training accuracy of 95%, FL training accuracy of 98%.

6G Technology in Intelligent Healthcare: Smart Health and Its Security and Privacy Perspectives

6G Technology in Intelligent Healthcare: Smart Health and Its Security and Privacy Perspectives

UAV Communication in Space–Air–Ground Integrated Networks (SAGINs): Technologies, Applications, and Challenges

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions.

5G Network Optimization using Machine Learning

In the era of 5G technology, predicting coverage areas is crucial for optimizing network performance and ensuring reliable connectivity. This study presents a comprehensive analysis of various machine learning algorithms for predicting 5G coverage based on the RF Signal Data. The target column, Band Width, is used to gauge prediction accuracy across different models. Traditional methods such as Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), XG Boost, Light GBM, AdaBoost, Bayesian Network Classifier models are compared with advanced techniques such as Stacking and Voting Classifiers, as well as Convolutional Neural Networks (CNN). The objective is to identify dominant feature parameters that significantly influence 5G coverage prediction. By implementing a diverse array of models, this research aims to benchmark the performance and accuracy of these algorithms. The comparative analysis highlights the strengths and limitations of each approach, providing valuable insights for network engineers and researchers. The findings suggest that ensemble methods, particularly Stacking and Voting Classifiers, along with CNN, offer superior prediction accuracy and robustness, thereby serving as promising tools for enhancing 5G network planning and deployment.

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

A cross-country network analysis of disease infodemics: Looking through the lens of the COVID-19 pandemic.

Infodemics surrounding pandemics and epidemics have persisted for centuries and continue to impede efforts to promote high vaccination coverage. We explored the complex interplay between COVID-19 vaccination attitudes and COVID-19-related infodemics across Germany, Ghana, India, and New Zealand using the novel network analysis. We conducted an online cross-sectional survey and recruited 1822 participants from the general populations of India (n=411), New Zealand (n=413), Ghana (n=523), and Germany (n=413) to complete COVID-19 infodemic measures and demographics questions. Predictors of less favourable COVID-19 vaccination attitudes included the apocalypse-related infodemic in India, compared to all countries, as well as the not harmful belief of COVID-19 in New Zealand, compared to India and Germany, and in Ghana compared to India. COVID-19 origin-related infodemics were found to positively impact COVID-19 vaccination attitudes in Ghana and New Zealand only. The most influential infodemics in each of the countries studied-Ghana, New Zealand, Germany, and India-were the beliefs in bioweapons, 5G technology, apocalypse, and bat consumption, respectively. Across all countries, there were consistent strong positive connections between the infodemics. Our results suggest that country-specific infodemics significantly impact COVID-19 vaccination attitudes, with variations observed between nations. While the infodemics can reinforce each other and complicate public health efforts during pandemics, understanding the interactions between the central infodemic in each country and others can aid in developing more effective strategies to counter their influences. This highlights the importance of targeted public health interventions that address the unique dynamics of local infodemics during pandemics, epidemics, and Pathogen X outbreaks.

Economic benefits of new broadband network coverage and service adoption: evidence from OECD member states

Abstract A broad-scale rollout and adoption of new broadband networks and services, respectively, are expected to generate innovative services for consumers and create a high potential for productivity increases and economic growth. However, there is no evidence available on the causal impact of both broadband coverage and adoption on economic outcomes, which we measure as gross domestic product (GDP). Moreover, no study has yet simultaneously considered the impact of both new wireline broadband based on fiber-optic technologies and wireless (mobile) broadband based on 3G+/4G technologies. Distinguishing these effects is of crucial relevance for the efficient design of broadband policies. To provide reliable evidence on causal effects, we utilize comprehensive panel data for 32 Organisation for Economic Co-operation and Development (OECD) countries for the years 2002–2020 and panel fixed-effects estimators including instrumental variables estimation. Our results show that both fixed and mobile broadband adoption exert a substantial and significant impact on GDP, while network deployment per se exhibits only minor multiplier-related effects on GDP per capita. Contemporaneous effects of a 1% increase in fixed broadband adoption impact GDP per capita growth in a range of 0.026–0.034%, while a 1% increase in mobile broadband adoption contributes between 0.092% and 0.102%. While the impact of contemporaneous mobile broadband adoption is substantially higher, fixed broadband adoption shows stronger dynamic and cumulative effects, as well as larger effects in later deployment periods. Generally, our results are consistent with the notion that the adoption of technologies to substantial proportions of the population is most important in driving economic growth.

Dynamic Key Replacement Mechanism for Lightweight Internet of Things Microcontrollers to Resist Side-Channel Attacks

5G technology and IoT devices are improving efficiency and quality of life across many sectors. IoT devices are often used in open environments where they handle sensitive data. This makes them vulnerable to side-channel attacks (SCAs), where attackers can intercept and analyze the electromagnetic signals emitted by microcontroller units (MCUs) to expose encryption keys and compromise sensitive data. To address this critical vulnerability, this study proposes a novel dynamic key replacement mechanism specifically designed for lightweight IoT microcontrollers. The mechanism integrates Moving Target Defense (MTD) with a lightweight Diffie–Hellman (D-H) key exchange protocol and AES-128 encryption to provide robust protection against SCAs. Unlike traditional approaches, the proposed mechanism dynamically updates encryption keys during each cryptographic cycle, effectively mitigating the risk of key reuse—a primary vulnerability exploited in SCAs. The lightweight D-H key exchange ensures that even resource-constrained IoT devices can securely perform key exchanges without significant computational overhead. Experimental results demonstrate the practicality and security of the proposed mechanism, achieving key updates with minimal time overhead, ranging from 12 to 50 milliseconds per encryption transmission. Moreover, the approach shows strong resilience against template attacks, with only two out of sixteen AES-128 subkeys compromised after 20,000 attack attempts—a notable improvement over existing countermeasures. The key innovation of this study lies in the seamless integration of MTD with lightweight cryptographic protocols, striking a balance between security and performance. This dynamic key replacement mechanism offers an effective, scalable, and resource-efficient solution for IoT applications, particularly in scenarios that demand robust protection against SCAs and low-latency performance.

Teaching strategies for moral education in colleges and universities based on 5G network and big data technologies

ABSTRACT The necessity to determine optimal teaching strategies for the development of moral education in the context of implementing 5G technologies has motivated the goal of this research: to study the personal aspects of students’ value orientations in the dynamics of their formation depending on the pedagogical strategies of teachers in the context of education based on 5G and big data technologies. The study was conducted at Shandong University of Science and Technology (SDUST), involving 279 instructors and 582 undergraduate students in the full-time program. A survey of the instructor group was carried out using a specially designed questionnaire, and a longitudinal study of students’ value orientations in the first year of undergraduate studies was conducted using the Morphological Test of Life Values (Sopov and Karpushina), This allowed assessing how teaching strategies influence indicators of students’ moral development. Identifying the leading pedagogical strategies of moral education in one of China’s largest universities revealed how general principles are refracted through ethnocultural peculiarities, evaluating the effectiveness of these strategies in shaping the personalities of future professionals who will work with large databases in the 5G network.

Improvement of Internet of Things (IoT) Interference Based on Pre-Coding Techniques over 5G Networks

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Development trend and challenges of communication equipment maintenance industry under 5G Internet

With the wide application of 5G Internet technology, the communication equipment maintenance industry is facing unprecedented changes. This article will delve into the impact of 5G technology on the communication equipment maintenance industry, analyze the industry's development trends and challenges, and propose response strategies to provide reference for the sustainable development of the industry.

Innovative Horizons for Sustainable Smart Energy: Exploring the Synergy of 5G and Digital Twin Technologies

Innovative Horizons for Sustainable Smart Energy: Exploring the Synergy of 5G and Digital Twin Technologies

A Novel 5G Fronthaul Architecture Based on Quantum Security Protection

In the digital age, 5G technology has significantly enhanced global communication capabilities, providing strong support for various industries. However, 5G falls short of achieving comprehensive intelligence within the Internet of Things (IoT), propelling the development of 6G technology. 6G is expected to further enhance network performance by integrating advanced technologies such as AI and quantum communication, while expanding application scenarios to include communication in extreme environments for comprehensive global connectivity. This paper proposes an innovative fronthaul architecture that applies Quantum Key Distribution (QKD) technology to the 5G fronthaul, leveraging its unconditionally secure key generation and distribution mechanism. In this design, costlier Alice devices are placed on the AAU side, while less expensive Bob devices are positioned on the DU side, optimizing deployment to reduce engineering costs and lower deployment barriers. The feasibility of this architecture is demonstrated by calculating the secure key rate. Comparative analysis with existing research is performed to clarify future research directions. These innovations not only enhance 5G network security but also offer new solutions for the security requirements of 6G networks, such as data security, network resilience, and algorithm transparency. Our research offers strategic value for future network security, laying the groundwork for reliable networks.

Intelligent technologies in traffic flow control: a review

With the acceleration of global urbanization, traffic congestion has become a major bottleneck in the development of modern cities, leading to serious economic losses and a decline in the quality of life. To address this challenge, traffic flow prediction techniques have received much attention. This paper reviews the application of three key technologies, including 5G, artificial intelligence (AI), and vehicular networking (V2X) in intelligent traffic management. 5G technology provides a communication foundation with high speed rates, low latency, and large-scale device connectivity, which effectively supports real-time data transmission and communication needs. AI technology, on the other hand, with its powerful data processing capability, is able to make high-precision traffic predictions in dynamic traffic environments. However, the effectiveness of AI models is highly dependent on the support of high-quality data.V2X technology greatly improves road safety and traffic mobility by realizing real-time information exchange between vehicles and infrastructure. However, relying on a single technology alone cannot comprehensively solve complex transportation problems. For this reason, this paper proposes a technology fusion scheme of 5G, AI, and V2X, aiming to optimize the intelligence level of the traffic management system through the complementary advantages of each technology. The study shows that the synergistic application of the technologies can not only effectively alleviate traffic congestion but also improve the overall safety of roads, providing a feasible solution for the future intelligent transportation system in cities.

Analysis and Evaluation of Intel Software Guard Extension-Based Trusted Execution Environment Usage in Edge Intelligence and Internet of Things Scenarios

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios.

An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection

In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.