Communication Systems Research Articles

Magneto-optic properties of highly Dy3+-doped GeO2-B2O3 glasses for NIR optical applications

Recent progress in the development of optical communication and high power laser systems in the near-infrared (NIR) region (1.5 to 2.0 μm) increased the demand for magneto-optic (MO) devices employing MO materials. These MO materials are required with lower optical absorption coefficients and higher Verdet constants at spectral range of interest. In this study, two series of highly Dy3+-doped GeO2–B2O3 glasses with and without P2O5 were fabricated using a simple melt-quenching technique. The glasses were characterized through various techniques (Raman, UV–VIS-NIR, and DTA) for the evaluation of characteristic properties with respect to the Dy2O3 concentration and glass composition. Faraday rotation measurement was carried out at 1550 nm to quantify the Verdet constant of the glasses. The Verdet constant values were found to increase linearly with increasing Dy3+ concentration for both the glass systems. The increase in Dy3+ concentration and, consequently, the number of unpaired electrons, leads to an increase in the Verdet constant. The GeO2-B2O3 glass doped with 30 mol% of Dy2O3 exhibited the highest Verdet constant of −5.36 rad/(T⋅m) at 1550 nm. The glasses exhibited a good optical transparency (>70 %) in the region covering from 400 to 2400 nm. The thermo-physical, optical, and polarizability properties of both glass systems were also investigated. The results indicate that the highly Dy3+-doped glasses are attractive for magneto-optics at 1550 nm.

Trustworthy and Scalable Federated Edge Learning for Future Integrated Positioning, Communication, and Computing System: Attacks and Defenses

Trustworthy and Scalable Federated Edge Learning for Future Integrated Positioning, Communication, and Computing System: Attacks and Defenses

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Augmented Reality Control based Energy Management System for Residence

Introduction: The roots of virtual reality (VR) go back to the work of Ivan Sutherland in the 1960s. At the time, virtual reality showed promise in universities and labs, peaking in the early 1990s at a stable level. The latest form of IoT is merging with the Internet of Everything (IOE). Using this technology, users can access any device, device, and machine in the environment through any middleware application or system. In recent years, smart speakers have become a common commercial method for controlling household appliances, solving some of the problems of remote control. For example, speech recognition is difficult in noisy environments and is minimized using noise cancellation. It also takes a long time to determine the current state of the device as the user need to request or issue a command to the smart speaker and need to wait for a response. The evaluation of prototype system found, using a device with gestures and interacting with a virtual 3D device instead of a real device was acceptable in terms of usability. In this article, the Unity and Vuforia are used to create an AR-based IoT virtual switch that can control any device connected to a LAN controller via HTTP requests. Methods: The system is made experimental with the Augmented Reality (AR), the virtual communication system is designed instead of physical mode. It provide the real time based image for active control of Energy management. It consist of virtual switches, it supports the AR based power control system. Results: The proposed system are made evaluated and tested under various load conditions. The virtual image consist of virtual switches to make real time control of power equipment. The proposed system achieve the latency of about 0.06 sec. Conclusion: The advancement in day today activities required with AR based energy management system with proper control measures. The designed system is supportive for such mechanism. The further system is proposed to make more energy saving concept for enhancement in proposed work.

Resource allocation optimization of device-to-device communication using machine learning algorithms

Device-to-device communication is envisioning next-generation wireless communication. The utility of the device-to-device communication model encourages emerging communication systems such as 5G and the Internet of Things. The allocation of resources and channel interference are major challenges in device-to-device communication. This paper proposes machine-learning-based algorithms for resource allocation and optimization of device-to-device communication. The proposed machine learning algorithm is a cascaded support vector machine. The cascaded support vector machine mapped the parameters of CUEs and DUEs. We create an iterative algorithm to achieve low-power, energy-efficient resource allocation with mode selection by formulating a novel optimization problem to maximize energy efficiency using the subtractive form method to solve a fractional objective function. We obtain data samples from a suboptimal algorithm to train the cascaded algorithm and verify the trained algorithm. Our numerical results show that the proposed cascaded machine learning-based transmission algorithm's accuracy reaches about 88%–95% despite its simple structure due to the limitation in computing power. The analysis of results suggests that the proposed algorithm is more efficient than SVM, DSVM, and the reinforcement learning (RL) algorithm.

Implementation of an indoor optical camera communication and localization fusion system using infrared LED markers

Recently, with the development of communication and positioning technologies, progressively higher demands have been placed on the timeliness of indoor communication and the effectiveness of indoor positioning. This paper proposes a low-cost and full-time domain coverage indoor communication and localization fusion system that uses an infrared camera to identify mobile LEDs based on region of interest optical camera communication (RoI-OCC) and calculate three-dimensional (3D) world coordinates based on perspective-n-point (PnP) algorithm. An intermediate delimiter modulation-demodulation method is designed to achieve asynchronous OCC. In processing infrared images, a region-growth-based absolute-directional-mean-difference (RG-ADMD) algorithm is developed to detect infrared targets and recover the scale. Additionally, a local-contrast-method-based Lucas-Kanade (LCM-LK) optical flow estimation algorithm is designed to track infrared targets and determine the centroid coordinates of pixels. In a 5×5 m experimental area, the established system can achieve a target detection recall rate of over 85%, the asynchronous OCC with error-free transmission and an average 3D positioning accuracy of 2.01 cm under a LCM-LK tracking algorithm.

Design and Implementation of a Width-Adjustable Asynchronous FIFO for Cross-Clock Domain Data Transmission

Existing research shows that asynchronous FIFO has been widely used in a variety of complex system designs, such as FPGA-based image processing systems, USB communication systems, and so on. To make the designed asynchronous FIFO more programmable and flexible, so that it can adapt to various application scenarios more quickly, this paper studies and designs an asynchronous FIFO with adjustable input and output bit width. In this study, according to the relationship between 24-bit input data and 32-bit storage space, four 24-bit data are selected to fill three RAM storage spaces at one time. According to the multiple relationships between 32-bit storage space and 128-bit output data, the reading pointer is enlarged by 4 times to realize the function of reading four 32-bit data at one time. This whole process realizes the conversion of data bit width through data splicing. Finally, the 24-bit RGB888 image data is successfully converted into 128-bit data and transmitted to the bus, which successfully solves the problem of data width mismatch.

Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.

Full van der Waals Ambipolar Ferroelectric Configurable Optical Hetero-Synapses for In-Sensor Computing.

The rapid development of visual neuromorphic hardware can be attributed to their ability to capture, store and process optical signals from the environment. The main limitation of existing visual neuromorphic hardware is that the realization of complex functions is premised on the increase of manufacturing cost, hardware volume and energy consumption. In this study, we demonstrated an optical synaptic device based on a three-terminal van der Waals (vdW) heterojunction that can realize the sensing functions of light wavelength and intensity as well as short-term and long-term synaptic plasticity. In the image recognition task, we constructed an optical reservoir neural network (ORNN) and a visible light communication system (VLC) composed of this optical synaptic device. The ORNN has a recognition rate of up to 84.9% for 50 000 color images in 10 categories in the CIFAR-10 color image dataset, and the VLC system can achieve high-speed transmission with an ultra-low power consumption of only 0.4 nW. This work shows that through reasonable design, vdW heterojunction structures have great application potential in low-power multifunctional fusion application tasks such as visual bionics.

Water-to-air unmanned aerial vehicle (UAV) based rolling shutter optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN)

Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted in oceanic research activities. As the number of underwater sensors and AUVs is growing quickly, the bandwidth requirements are increasing accordingly. In this work, we put forward and demonstrate a large field-of-view (FOV) water-to-air unmanned aerial vehicle (UAV) based optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN) for the first time to the best of our knowledge. As the UAVs are embedded with complementary-metal-oxide-semiconductor (CMOS) cameras, there is no need to install OCC receivers (Rxs), reducing the deployment cost. Moreover, the large photo-sensitive area of the CMOS camera can support a large FOV OCC transmission without the need for precise optical alignment. Here, by utilizing the column matrix identification during the rolling shutter pattern decoding in the CMOS image sensor, the scintillation caused by water turbulence can be reduced. Besides, in the outdoor and windy environment, the UAV will experience significant movement caused by the wind making it very difficult to capture stable OCC frames in the CMOS image sensor. Here, we propose and demonstrate utilizing GRU-NN, which is a special realization of the recurrent neural network (RNN) with memory cells capable of learning the time-domain dependent signals. It is shown that the GRU-NN can learn effectively from successive image frames in time-domain and produce correct prediction even under the windy and unstable UAV flying environment. Experimental results reveal that the proposed GRU-NN can outperform the previous pixel-per-symbol labeling neural network (PPS-NN), and also can significantly reduce the computation time when compared with long-short-term-memory-neural-network (LSTM-NN). The proposed system can decode 4-level pulse-amplitude-modulation (PAM4) rolling shutter OCC patterns at data rates of 5.4 kbit/s and 3.0 kbit/s under clear and cloudy water, respectively, fulfilling the pre-forward error correction bit-error rate (pre-FEC BER = 3.8 × 10−3). We also demonstrate that the UAV based OCC system can support data rates of 5.4 kbit/s, 4.2 kbit/s, and 3.0 kbit/s at distances of 2.2 m, 3.2 m, and 4.2 m, respectively, at outdoor and windy environments.

Health burden and economic costs of vector-borne diseases in Italy: the AUSL Romagna case

Abstract Introduction Vector-borne diseases (VBDs) are a serious threat for global health, representing around 17% of communicable diseases and more than 700.000 deaths every year. In Europe, and especially in Italy, this is a subject of growing concern, due to the increase of VBDs outbreaks that need vector control plans. Objectives Evaluate the 2013-2022 trend of VBD outbreaks within the AUSL Romagna area (provinces of Ravenna, Rimini, and Forlì-Cesena, in Emilia-Romagna Region, Italy) and the economic impact caused by hospitalizations and local prevention/disinfestation treatments. Methods We used aggregated and anonymized data from 3 informative systems: communicable disease system (SMI) for VBD prevalence; hospital discharge records (SDO) for hospitalizations, with a contribution of the VBD-related Diagnosis Related Groups (DRG); municipality records regarding the cost of dedicated interventions for vector prevention and disinfestation, communicated to the Emilia-Romagna Region. Results For the area of interest, 479 cases of VBDs were notified from 2013 to 2022, with a slight increasing trend over the years. Through hospital SDOs, 252 cases were identified with a diagnosis of VBD, plus 283 with a VBD-related DRG, with a total expenditure of €9.587.777. The distribution was not uniform between the provinces, Ravenna having the highest expense. The municipality records highlighted expenses for €95.443 for prevention and control interventions against mosquitoes, with a very variable trend over the period considered. Forlì had the highest expense. Conclusions VBDs show a highly variable trend and distribution during time, also in relatively small, sub-regional areas. The Emilia-Romagna regional plan against arboviruses, annually updated, can contribute to reduce this specific health and economic burden, lowering transmission among the population and resulting hospital pressure, thanks to prevention and timely interventions. Key messages • The use of information systems enables monitoring of the impact of arboviruses, as well as the related cost for hospital care and local treatments. • Putting in place an effective and updated plan against arboviruses, like the Emilia-Romagna Region one, helps managing VBDs and mitigating economic costs.

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

Experimental and numerical validation of tape-based metasurfaces in guiding high-frequency surface waves for efficient power transfer

We present an effective method for transmitting electromagnetic waves as surface waves with a tape-based metasurface design. This design incorporates silver square patches periodically patterned on an adhesive tape substrate. Specifically, our study proposes a strategy to enhance the efficiency of power transfer in high-frequency bands by guiding signals as surface waves rather than free-space waves. Both the numerical and experimental results validate the markedly enhanced efficiency in power transfer of high-frequency signals compared to that achieved with conventional methods, such as wireless power transfer and microstrips. Importantly, our metasurface design can be readily manufactured and tailored for various environments. Thus, our study contributes to designing power-efficient next-generation communication systems such as 6G and 7G, which leverage high-frequency signals in the millimeter-wave and terahertz bands.

Tunable Fano resonances at 2 μm waveband based on a single microring resonator

Abstract Development of silicon photonic devices for the 2 μm waveband is urgent since the 2 μm waveband has been considered as a potential communication window for next-generation optical communications. Asymmetric Fano resonances are very promising in the development of devices such as optical switches, sensors, and modulators for the 2 μm waveband. In this paper, a Fano resonance at the 2 μm waveband is theoretically realized by using a silicon-based microring resonator and the thermo-optic effect is employed to tune it. Utilizing this single microring resonator structure instead of the present coupled microring resonator structures, we achieve the Fano resonance with an extinction ratio of 30.5 dB and a slope ratio of 69.3 dB nm−1 at the 2 μm waveband. The metal heater and graphene heater are designed and optimized to achieve the Fano tuning. The thermal-optical tuning efficiencies are 0.46 nm mW−1, 0.61 nm mW−1 and 1 nm mW−1 for the metal heater, the metal heater with air adiabatic slots, and the graphene heater, respectively. This work provides an effective scheme for the formation and tuning of the Fano resonance at the 2 μm waveband, which is conducive to the development of devices and optical communication systems at the 2 μm waveband.

Preparedness and Public Health: an Italian Local Health Authority plan for a mass gathering in 2023

Abstract Issue Mass gatherings represent an important Public Health challenge. The increased number of people present in a specific area puts local health systems under stress (i.e.workload of emergency services, environmental health and food safety) and at the same time it rises the risks of a potential mass casualty incident. Effective planning and disaster preparedness have to be achieved through collaboration among local health authorities(LHA) and other stakeholders (event planners, law enforcement, local transportation services). Problem Every year, for 3 days, the Italian Alpine troops gather in a different city to remember First World War fallen. During those days, the city centre transit is not allowed to car (in specific areas even to emergency vehicles).In May 2023 this event occurred in Udine, a city in the North East of Italy with almost 100.000 inhabitants. For that year at least 300.000 participants were expected. LHA had to reorganize preparing itself to manage a temporary quadrupled catchment area. Results In the previous months, local authorities met local health representatives and other stakeholders in order to define a single emergency response plan. For healthcare services it was decided to: institute first aid points within the city; reorganise patients’ emergency transport system; increase hospitals’ treatment capacity (beds, life support equipment); increase healthcare professional availability; empower drugstores (opening times, drugs procurement); strengthen human and animal health surveillances;activate alternative communication systems. During the event, LHA updated other authorities involved three times a day about hospital and emergency system situation. At the end of the event,a multiprofessional debriefing meeting was held and no critical situations were reported. Lessons Preparedness requires a great commitment in terms of resources and time, but it is confirmed to be essential to guarantee adequate assistance even in extraordinary conditions. Key messages • Mass gathering s events put under stress all the main areas of public health system. • Public health preparedness cannot be effective without a strong involvement of other public and private stakeholders.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

Optimal Design of Smart Antenna Arrays for Beamforming, Direction Finding, and Null Placement Using the Soft Computing Method

ABSTRACTThe urge of modern communication system is to design and development of the smart antennas with adaptive radiation characteristics. The multifold capabilities of fourth‐dimensional antenna arrays can cater that much needed adaptiveness if properly designed. Compared to the conventional arrays, the fourth‐dimensional arrays have one added advantage as the ‘Time’ of all the switched‐on antenna elements can be managed to generate the required amplitude and phase tapering without even using attenuators and phase shifters. However, one inherent limitation of fourth‐dimensional control parameter is the generation of harmonics or sidebands. This article proposes various means of radiation pattern synthesis in fourth‐dimensional linear antenna arrays with pulse shifting, pulse splitting, and a combination of both. First of all, the pulse splitting and shifting techniques are combinedly proposed by reducing the sidelobe levels and sideband levels of the beamforming antenna arrays to enhance directivities and efficiencies. Then, this mathematical proposition of the direction finding fourth‐dimensional arrays is developed. Finally, broad nulls over a specific angle of arrival region are created for jamming and interference mitigation. For all these cases, the sidelobe level and the unwanted higher‐order sideband levels are suppressed to reduce the unwanted interferences and power losses. The optimal time schemes for all the synthesized patterns are generated by proposing a chaos‐based soft computing algorithm. The radiofrequency signals at each radiating array element are processed by the optimal time schemes proposed for specific applications. The outcomes are validated and compared with other state‐of‐the‐art works of this domain to prove the competency of the proposed work. The qualitative and quantitative comparisons presented for beamforming array is aimed for a good improvement over other reported works by targeting ultralow (less than −40 dB) sidelobe and sideband levels. For direction‐finding array, the proposed idea has also targeted ultralow sidelobes for the main as well as steered beam patterns. Furthermore, the null placement over a region has been aimed to cover more area for jamming and sidelobe reduction for interference mitigation. Overall, the optimal designs proposed for these advanced applications are beneficial for cutting‐edge communication systems.

Hardware assurance with silicon photonic physical unclonable functions

In the modern landscape of optical communication networks, ensuring robust security is increasingly critical, particularly for applications requiring seamless integration and minimal attack surfaces. Photonic Physical Unclonable Functions (PUFs) leverage the response from the photonic devices that are prone to inherent physical variations to generate unique and unpredictable signature identifiers which are then utilized by an authentication system for identification or encryption purposes. These photonic PUFs can be cohesively integrated into systems that use optical communication, whereas using electronic PUFs would introduce additional vulnerabilities due to the need for signal-domain conversions between optical and electronic signals. In this paper, we present the design, fabrication, and experimental evaluation of advanced silicon-photonic-based PUFs utilizing Contra-Directional Coupler (CDC) structures. These structures offer a complex design space and are intrinsically sensitive to fabrication-process variations, making them ideal for creating unique and secure responses. We introduce several innovative design enhancements, including randomized corrugation functions, perforated designs, and ring-assisted CDCs, to increase the complexity and unpredictability of the CDC response. Measurement results from the fabricated CDCs demonstrate their capability to achieve an average Hamming distance threshold of over 0.2, effectively distinguishing between legitimate devices and their copies. We rigorously tested these fabricated designs against three different machine-learning-based attack scenarios. The results showed a Hamming distance of over 0.4 with a standard deviation of less than 0.01 at a quantization level of three, using 10,000 samples of challenge-response pairs. These findings underscore the potential of silicon photonic PUFs in enhancing security for optical communication systems of different scales. The integration of such photonic PUFs offers robust and reliable security solutions for applications where traditional electronic methods introduce additional attack surfaces and fail to provide adequate protection.

Low-complexity passive beamforming for reconfigurable intelligent surface aided mmWave communication systems

Low-complexity passive beamforming for reconfigurable intelligent surface aided mmWave communication systems

Cloud-Based Automatic Configuration and Disaster Recovery of Communication Systems Applied in Engineering Training

Network management and troubleshooting require not only a grasp of advanced concepts but also the development of analytical and problem-solving skills. To bridge this gap, this paper introduces a novel network administration system, DRACSC (Spanish acronym for device for automatic recovery and configuration of communication systems), designed for the automatic configuration and disaster recovery of communication equipment. This system transcends the limitations of current hardware and software solutions by combining their advantages, boasting portability, automated functions, and a cloud-based repository as its main features. The DRACSC system, undergoing a comprehensive large-scale evaluation involving diverse user groups across multiple institutions, was tested with 89 users, including students and teachers at educational centers and ICT (Information and Communication Technology) professionals. The benefits of the system were evaluated through a training program based on simulated real-world ICT environments, focusing on both quantitative results on the reduction in time to complete user tasks, as well as qualitative results on the interface and usability of the system. Statistical analysis, including Welch’s t-test on opinion surveys, indicated a significant increase in knowledge and understanding, demonstrating the system’s potential to enhance education and practice. Moreover, the evaluation shed light on the user experience, with positive impacts observed for learning and teaching implications. As a result, the study has verified that the system has the potential to significantly influence network management practices, enhancing both learning and professional application through improved efficiency and usability.