Average Response Time Research Articles

Research on high performance web service construction method based on JavaScript asynchronous programming technique

Abstract In the face of massive concurrent user access in the era of big data, how to build high-performance web services has become one of the difficulties to be solved by network applications. This paper utilizes Node.js architecture to fully utilize the event-based programming and non-blocking I/O characteristics of JavaScript language to improve the CPU utilization of the web service side. It also proposes the strategy of predicting user-accessed resources for cache replacement through a Gaussian mixture model to further optimize the performance of high-concurrency web services. The performance of the high concurrency web framework constructed using Node.js technology plays excellent, the performance of multi-threaded Node.js is better than single-threaded, and compared with Apache architecture, the average response time, request-response rate, and data throughput test of Node.js architecture in high concurrency scenarios is more advantageous than Apache architecture. The Gaussian mixture clustering model is effective in dealing with data with high-dimensional features, and the average accuracy of the four clustering processes of each algorithm is, in descending order, GMM (79.67%), BIRCH (78.09%), and K-means (77.52%). In addition, the cache replacement strategy based on Gaussian Mixture Model is more effective, with an accuracy rate of nearly 80% and a byte hit rate of nearly 45% when the cache capacity reaches 214KB, which are both higher than the traditional cache replacement strategy with the same cache capacity.

Digital Technology-Driven Transformation of Teaching Model of Digital Animation Major and Enhancement of Students’ Artistic Literacy

Abstract Digital technology, at the center of the fourth technological revolution, is currently transforming all aspects of people’s lives and learning in an unprecedented manner. In light of the ongoing advancements in education technology, this study introduces an enhanced C-ADDIE teaching model, which is competence-oriented and builds on the foundation of the traditional ADDIE model while also catering to the needs of students majoring in digital animation. The enhanced fuzzy clustering algorithm establishes a portrait of the student’s behavior, followed by the introduction of a multi-attention mechanism that builds a digital professional student performance prediction model and a supplementary teaching system. We are developing a teaching model that integrates digital technology with the C-ADDIE model. The testing of the teaching system reveals that the grade prediction accuracy ranges from 88% to 98%, with an average prediction accuracy of 93.02%. The average response time is 1.5ms, the CPU usage remains stable between 5% and 23%, and the overall performance of the teaching system is good. The results of the teaching experiment show that the average grade of the teaching mode of this paper is 5.28 points higher than the average grade of the traditional teaching mode, and at the same time, it also has a significant effect on the improvement of knowledge cultivation, artistic expression and creativity, and artistic aesthetic cultivation of artistic literacy (P<0.05). This study’s results contribute to the transformation of teaching in digital animation and offer a new research idea to enhance students’ artistic literacy.

Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis

Abstract With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.

Research on active coordination control mechanism of provincial and local AGC in power system based on deep learning technology

Abstract AGC is the main means to maintain the active power balance of the power system and ensure the system frequency quality. In this paper, deep learning techniques are used to optimize AGC active coordinated control. The linearized model simplifies the dynamic system at the operating point, and in the AGC coordinated control system, the model parameters are estimated using the recursive least squares method. The discrete reinforcement learning DQN algorithm is used to construct the AGC optimization model, and the continuous reinforcement learning PPO algorithm is used to propose the optimization strategy for the AGC active coordinated control, which solves the problems of discretization error as well as dimensional catastrophe. The AGC active coordinated control test is being carried out at power station A in the northwest region of China as the study site. After the optimization, the average regulation rate, average response time, and average regulation accuracy of Generation Station A are improved to 0.4995, 0.7835, and 0.8082, respectively, and the comprehensive FM performance index is improved by 22.08% compared with that before the optimization. The economic benefits indexes such as FM capacity and FM mileage compensation revenue fee are improved, and the average response time qualification rate and regulation rate in October-December after optimization are improved by 5.87% and 6.38%, respectively, compared with the pre-optimization period.

Response Characteristics of a ZnO Arrester Under Nanosecond Electromagnetic Pulse

Zinc oxide arresters, a major type of overvoltage protection equipment in power systems, can suppress transient lightning overvoltage, operation overvoltage, and other types of power surges. However, the response characteristics and protection effects under nanosecond pulses, such as those in a high-altitude electromagnetic pulse conduction region, remain unclear. Therefore, the surge protection performance of arresters should be properly understood, and high-performance arresters should be developed. In this study, critical parameters, including overshoot peak voltage, residual voltage, response time, and voltammetry characteristics of a representative 10 kV ZnO arrester under nanosecond pulses, were obtained using a double exponential pulse current injection source. The overshoot peak voltage of this arrester under a nanosecond pulse is 2.19 times of lightning impulse residual voltage, and the residual voltage ratio under a nanosecond pulse is 4.31. The average response time is approximately 45 ns, and the response time is independent of the nanosecond pulse amplitude. Furthermore, an electromagnetic transient model of this type of lightning arrester is established based on the CIGRE guidelines, as well as a method to determine the key parameters. The model can simultaneously account for the response characteristics of the small current region and the conduction region of the arrester, which properly agrees with the experimental results.

Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks

Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real‐time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study’s findings indicate that the PSO + BP model’s average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems.

Construction of Cloud Computing Resource Allocation and Ideological and Political Education Platform Based on the Theme of Party History and Party Building

Abstract Party history and party-building education is an important carrier of party-building work in colleges and universities, and it is also an important content of cultivating and promoting socialist core values while party-building work is an effective way and the main hand of ideological and political work of students in colleges and universities. This paper takes the large amount of data generated in the process of ideological and political education resource allocation on the theme of party history and party building as the research object and builds a mapping relationship between tasks and ideological and political education resources according to certain strategies based on cloud computing resource allocation. A platform for allocating resources for ideological and political education has been designed and developed, which is based on a time series clustering algorithm and constructs resource overviews, sequence collections, and clustering modules. The functions of student behavior data collection and overview, feature calculation and selection, behavior time series clustering, and group feature analysis are realized, which enhances the efficiency of the ideological and political education resource platform. Functional and performance tests of the platform were completed, and the average system response times of the three modules were 75.05, 80.39, and 69.95ms, which were able to meet the actual application standards. With the help of the platform, the behavioral record data of 6,927 platform users were evaluated and practiced, and the user group classification and group characterization were carried out according to the output of the platform, which provided scientific suggestions for the distribution of ideological and political teaching resource services.

Design of Cloud Scheduling Simulation System for IoT Resources Based on Digital Intelligent Environment

With the popularization of intelligent devices and the development of internet technology, the Internet of Things technology is receiving increasing attention. However, how to effectively schedule and manage resources in the Internet of Things environment has become an increasingly important issue. In order to solve this problem, a cloud scheduling simulation system for IoT resources in a digital intelligent environment has emerged. This system utilizes cloud computing and IoT technology to simulate the behavior of intelligent devices and deep learning technology, achieving efficient resource scheduling. In addition, experimental data shows that the accuracy of the IoT resource cloud scheduling simulation system based on deep learning algorithm combined with digital intelligent environment is over 70%; the average task processing time is within 0.3 seconds; the average task response time is within 0.6 seconds. From this, it can be seen that combining deep learning algorithms with the system can quickly respond and solve problems, improving the reliability and stability of the system. The application range of this system is very wide, which can be used to optimize resource scheduling in many fields such as production, and can also be applied in fields such as smart home, smart medical, and intelligent transportation. With the continuous development of technology, this system would also be further improved and expanded to better serve people's lives and work.

Exploring the Development Path of Collaborative Education between College Civics Courses and Daily Civics Education Combined with the Theory of Three-Whole Parenting

Abstract As times evolve, ideological and political education in higher education has emerged as a central focus of reforms in this field, marking the synergistic development of these programs as an inevitable trend. This paper addresses the reform requirements of ideological and political education in the contemporary era. It proposes a development path for integrating Civic and Political Programs with ideological and political education in higher education institutions, based on the ‘three-whole-parenting’ theory. Additionally, we construct a comprehensive evaluation system using fuzzy comprehensive evaluation methods to assess the effectiveness of this integrative approach. The systems designed achieved an average response time below 1800ms, indicating strong performance. Student satisfaction with the synergistic path ranges from 4 to 5 points, with an average attitude score of 3.04 and a comprehensive evaluation score for quality of 4.53. This paper demonstrates that the development path for college Civics courses, based on the three-whole-person parenting theory, is effective, ensuring scientifically planned and comprehensively implemented synergistic education.

Application of AI technology in personalized recommendation system for financial services

Abstract How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.

Design and optimization of the Teaching evaluation system based on artificial intelligence

Abstract In today’s highly developed information society, the construction work of educational informatization has become the focus of the future development of major universities. This paper combines artificial neural networks and UML software modeling technologies to design a teaching evaluation system using artificial neural network technology. The system requirements are analyzed from three aspects: system feasibility, functional requirements, and neural network evaluation function. The proposed system performance requirements are based on these three aspects. The neural network approximation problem can be solved using the gradient descent method based on the BP neural network model. After a series of operations, such as data preprocessing, the Design of the teaching evaluation system is completed, realizing the joint role of teaching and neural network evaluation module, and at the same time, proposing a system optimization decision for the teaching quality aspect. The teaching evaluation system designed in this study passed the test as the average response time of the whole system was 94.366 seconds when the number of users was 100, and there was no phenomenon such as transaction unresponsiveness. After the use of the teaching system, the feedback from the students’ use shows that the number of computer science students who mastered more than 80% of the system concepts is 42, and the rest of the knowledge is concentrated in the number of people who mastered 40% to 80%.

Automatic fabrication system of optical micro-nanofiber based on deep learning

<sec>The wide range, high precision, and dynamic real-time measurement of micro-/nanofiber diameter are crucial for achieving low loss transmission and controlling dispersion in the preparation process of micro-nanofiber. In view of the problems of small diameter regulation range, complex operation and long-time consumption of the existing preparation methods, the automatic detection system of micro-nanofiber is realized based on deep learning neural network algorithm in this work. The image segmentation method in computer vision is used to make high-quality multi-scale micro-/nanofiber datasets, and the improved YOLOv8-FD (You Only Look Once version 8-Fiber Detection) algorithm based on small target detection is used to automatically detect the diameter of micro-nanofiber.</sec><sec>Through image segmentation and identification of the target of single pixel size in the microscopic image, the diameter detection of micro-nanofiber is finally realized. In this process, the real-time diameter of micro-nanofiber is obtained through image information, and then the micro-nanofiber small target is accurately segmented to achieve the precise detection of mAP<sup>IoU</sup><sup>=50</sup> = 0.975 and mAP<sup>IoU</sup><sup>=50—95</sup> = 0.765 on the micro-nanofiber multi-scale target dataset with extremely high accuracy. The algorithm-based construction of a high-precision micro-nanofiber automatic preparation system enables real-time accurate segmentation of fiber edges, calculation of fiber diameter, and feedback to the control system for achieving automated preparation of fibers with arbitrary diameters. Additionally, it facilitates the detection of micro-nanofiber in a range from 462 nm to 125 μm. The average response time for reasoning is 9.6 ms, and the detection error is kept below 2.95%.</sec><sec>In addition, compared with other micro-/nanofiber diameter detection methods based on optical imaging and mode cutoff, this method shows advantages of high precision, high speed and arbitrary diameter preparation for diameter detection based on deep learning neural networks. The system is very suitable for high-precision real-time measurement and automatic and accurate preparation of micro-/nanofibers, thereby providing a novel method of developing micro-nanofiber devices with low-loss transmission and adjustable dispersion. </sec>

A Game Theoretic Approach to Computation Offloading Strategy Optimization for Non-cooperative Users in Mobile Edge Computing

Computation offloading from a user equipment (UE, also called mobile user, mobile subscriber, or mobile device) to a mobile edge cloud (MEC) provides an effective way to virtualize an ordinary smart mobile device (e.g., smartphone, tablet, handheld computer, wearable device, and personal digital assistant) into a formidable equipment, which is able to provide more and stronger functionalities than that of a laptop or a desktop computer. It is conceivable that there can be several MECs with different processing capabilities in a geographic area, and each MEC many serve many UEs with endless sequences of computation tasks, various application characteristics, and diversified communication requirements and bandwidths. Furthermore, the mobile users are competitive and selfish, which means that computation offloading strategy optimization needs to be carried out for each individual mobile user to optimize the performance of only his applications. In this paper, we conduct a mathematical study of computation offloading strategy optimization for non-cooperative users in mobile edge computing by using a game theoretic approach. The main contributions of this paper can be summarized as follows. We establish an M/G/1 queueing model to characterize multiple heterogeneous UEs and MECs, so that the average response time of all offloadable and non-offloadable tasks generated on a UE can be calculated analytically and the optimal computation offloading strategy of a UE can be defined rigorously. We construct a non-cooperative game framework for a mobile edge computing environment, in which each player (i.e., a UE) can selfishly minimize his payoff by choosing an appropriate strategy in his strategy space. We prove the existence of the Nash equilibrium of the above game. We develop algorithms to find the Nash equilibrium, including an algorithm to find the best response of a mobile user and an iterative algorithm to find the Nash equilibrium. We demonstrate numerical examples and data of our game, including numerical data for the Nash equilibrium and numerical data for the convergence of the Nash equilibrium. To the best of the author's knowledge, this is the first paper that effectively investigates computation offloading strategy optimization for multiple, heterogeneous, and competitive mobile users and multiple heterogeneous mobile edge clouds by using a non-cooperative game approach. Hence, the paper makes noticeable contributions towards the understanding of a competing mobile edge computing environment and its stabilization.

Biometric lock with facial recognition implemented with deep learning techniques

The increased criminal activity associated with unauthorized entry into facilities has become a global concern. Traditional mechanical locks suffer from drawbacks such as key loss, theft, duplication risks, and time-consuming operation. Therefore, biometrics has been explored as a key to accessing a restricted area. However, some challenges still need to be solved in developing such systems, including user registration, response speed, maintainability, and the ability to distinguish between real and fake individuals. This paper proposes and develops a biometric lock system (BLS) whose opening is performed by recognizing a person?s face. It solves the challenges of re-training, antispoofing, real-time response, and works inside an embedding system. The proposed BLS overcomes these challenges using a pre-trained network called FaceNet for feature extraction and coding into 128-dimensional vectors.We use the characteristic vector provided by FaceNet and a cosine distance to recognize the persons. It also incorporates ResNet18 + remote photoplethysmography (rPPG) to avoid spoofing. The architecture was implemented in a BLS, demonstrating an impressive false acceptance rate of 0% under varying lighting conditions, with an average response time of 1.68 seconds from facial detection to door opening. The BLS has easy maintainable devices, providing enhanced security by accurately identifying individuals and preventing unauthorized access. The system can distinguish between real and fake people without using specialized hardware. Making it a versatile solution suitable for homes, offices, and commercial spaces. The results underscore the potential efficacy of our proposed BLS in mitigating security challenges related to unwarranted access to restricted facilities.

Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuum

As an increasing number of Distributed Machine Learning (DML) tasks are hosted on cloud platforms in the edge-cloud continuum, Data Centers (DCs) with massive data and computational requirements have become one of the world’s largest energy consumers, leading to significant carbon emissions. Reducing energy consumption and carbon emissions is an extremely crucial and challenging issue for the sustainable development of cloud service providers. While utilizing renewable energy can help reduce the carbon emissions of DCs, the intermittent and unstable nature still causes DCs to rely heavily on high-carbon brown energy. For the resource-intensive and delay-tolerant DML tasks, this paper introduces multi-renewable energy in the geo-distributed continuum to address this issue, the spatiotemporal complementarity maximizes the renewable energy utilization and compensates for time-dependent energy differences with geographic advantages. Additionally, considering the dynamic differences in carbon intensity and electricity prices across distributed DCs in the continuum, we propose an energy and carbon-aware algorithm called ECMR for scheduling heterogeneous virtual machine creation tasks of DML among multi-clouds in different time zones. It is demonstrated that compared with the baseline methods, the ECMR significantly reduces the total power consumption, energy cost, and carbon emission of data centers while maintaining an acceptable service quality. The utilization of renewable energy in data centers has been significantly improved to 90.8% by flexibly leveraging the spatiotemporal complementarity of multi-renewable energy. Compared with existing competing algorithms, the proposed method exhibits significant improvements with an achieved average response time of 12.6 ms, and a task failure rate of 1.25%.

Deep reinforcement learning task scheduling method based on server real-time performance.

Server load levels affect the performance of cloud task execution, which is rooted in the impact of server performance on cloud task execution. Traditional cloud task scheduling methods usually only consider server load without fully considering the server's real-time load-performance mapping relationship, resulting in the inability to evaluate the server's real-time processing capability accurately. This deficiency directly affects the efficiency, performance, and user experience of cloud task scheduling. Firstly, we construct a performance platform model to monitor server real-time load and performance status information in response to the above problems. In addition, we propose a new deep reinforcement learning task scheduling method based on server real-time performance (SRP-DRL). This method introduces a real-time performance-aware strategy and adds status information about the real-time impact of task load on server performance on top of considering server load. It enhances the perception capability of the deep reinforcement learning (DRL) model in cloud scheduling environments and improves the server's load-balancing ability under latency constraints. Experimental results indicate that the SRP-DRL method has better overall performance regarding task average response time, success rate, and server average load variance compared to Random, Round-Robin, Earliest Idle Time First (EITF), and Best Fit (BEST-FIT) task scheduling methods. In particular, the SRP-DRL is highly effective in reducing server average load variance when numerous tasks arrive within a unit of time, ultimately optimizing the performance of the cloud system.

Increased number of dispatches in emergency medical services correlates to response time extension.

This study investigated the correlation between the number of emergency medical service (EMS) dispatches and response time extension. In addition, we conducted a simulation to assess the potential for reducing response times by relocating the ambulance based on the number of dispatches. This retrospective observational study analyzed data on patients treated with EMS between May 1 and June 25, 2021, in an urban area (Chiba City, Japan). Spearman's rank correlation tests were used to analyze the correlations among the number of dispatches, response time extension, and ambulance distance. We created a heat map to visualize the number of dispatches and distribution of emergency case occurrences, and simulated the relocation of the EMS team with the lowest number of dispatches to the closest EMS team with the highest number of dispatches. In total, 7915 emergency cases were included. The median response time across all dispatches was 9 min, whereas that for the response time extension cases was 12 min. There was a significant positive correlation between the increased number of dispatches, response time extension (r = 0.94, p < 0.0001), and ambulance distance (r = 0.95, p < 0.0001). The relocation simulation significantly shortened the average response time from 13 min and 30 s to 12 min and 11 s (9.9% decrease, p < 0.0001). An increased number of dispatches significantly increased the response time extension cases and ambulance distance. Our simulation suggests that EMS relocation can potentially shorten the response time. While increased dispatches influence the response time extension, optimal EMS allocation may improve response times.

A Study on Recognizing and Communicating Metaphors in Picture Book Creation in the Digital Age

Abstract Picture book creation has a rich metaphorical connotation, which is becoming more and more popular along with the development of digitalization. In this paper, the textual metaphors of picture book creation are extracted by combining the textual feature extraction model of the BERT pre-training model and HAN network, the ResNet50 network extracts the illustration metaphors of picture book creation, and the extracted metaphorical features are multimodal fused and recognized with the help of fully connected layer. In addition, a picture book metaphor dataset was constructed, and after analyzing the basic features of picture book metaphors, the effect of the constructed multimodal metaphor recognition was explored, and the recognition and communication of metaphors in picture book creation was studied. The results showed that the recognition accuracy in picture book creation ranged from 0.67 to 0.77, and the high degree of metaphor recognition in picture book creation showed a positive correlation with the average response time of communication, with the regression equation RT=138.45*IdentificationStd+1045.The results of the analysis of the metaphorical picture book creation pictures with textual references showed that the degree of communication comprehension (M=22.38. Std.=3.68), communication liking (M=94.86, Std.=13.58) and reaction time (M=82.44s, Std.=14.15s). Picture books facilitate better interactive communication through their metaphorical expression.

Intelligent Green Building Design and Management Based on the Internet of Things: An Empirical Study on Low Carbon Development

This article explores the application of Internet of Things technology in intelligent green buildings through empirical research, and analyzes its practical effects in improving building energy efficiency and reducing carbon emissions. The study adopted data collection and analysis methods, and the experimental results showed that IoT technology can effectively optimize building energy management and achieve low-carbon development goals. This article designed four experiments to explore the application of Internet of Things technology in the design and management of intelligent green buildings. In the energy consumption comparison experiment, after the implementation of the Internet of Things system, power consumption decreased from 1000 kWh/day to 800 kWh/day, water usage decreased from 650 liters/day to 500 liters/day, and natural gas consumption decreased from 400 meters ³/ Days reduced to 300 meters ³/ Heaven. In the indoor environmental quality monitoring experiment, after implementing the Internet of Things system, the average indoor temperature was maintained at 22°C, humidity was 50%, and CO2 concentration was reduced to 600 ppm. In the response efficiency testing experiment of the automation control system, the average response time of the system was 2 seconds, and the response accuracy reached 90%. In the final economic benefit analysis experiment, the total savings within one year after implementing the Internet of Things system reached 12267 US dollars. From the data conclusion, it can be seen that IoT technology can not only improve the energy and environmental management efficiency of buildings, but also have significant economic benefits, providing strong support for the development of green buildings.

A Sensor-Based Data Acquisition System for Soil Parameters to Determine Suitable Crops

Soil parameters monitoring is significant in sustainable crop and food production. The standard strategy of soil parameters monitoring in developing and underdeveloped nations uses manual labor, resulting in wrong decisions in soil management. Inaccurate measurements due to sensor miscalibration or low sensor quality can lead to incorrect soil management decisions and negatively impact crop yield and environmental sustainability. Due to the mentioned challenges, this work aims to develop a Sensor-based Data Acquisition System for Soil Parameters that will enable users to observe various soil parameters like temperature, humidity, water level and soil pH. The system was developed using the combination of hardware and software components. The hardware component comprises of sensory and processing parts. The study calibrates sensors using known pH, moisture, and temperature values for specific crops to grow in Nigeria. The system will aid farmers in determining suitable crops for their farmland and increasing crop yield. The system collects data through a network of sensors installed in the soil and wirelessly transmits the data to a cloud-based server. The collected data is then analyzed and visualized in through a web-based dashboard, providing farmers with information about the state of their soil. The performance evaluation of the system was carried out using response time and accuracy. The average response time of the system was 4 seconds, and the percentage error for temperature and humidity readings when compared to weather forecast readings were 8.20% and 5.08%, respectively. The results show that the proposed system can provide accurate and reliable measurements of soil parameters and can be easily deployed and operated by small-scale farmers. Using this system can result in improved crop yields, reduced wastage, and better overall efficiency in agricultural operations