1. Introduction The Data Mover Challenge 2021/22 is a challenge organized by NSCC Singapore since 2018. The charter of the DMC is to build a platform and bring together experts from industry and academia to test their high performance data transfer solutions across the Data Transfer Nodes testbed set up in various countries connected by 100G and beyond international networks. The challenge focuses on optimizing point-to-point data transfers between sites – a crucial step forward in advancing research collaboration and sharing. Participants from all over the world will compete by deploying the best software tools on Data Transfer Nodes that are set up within existing international networks across the globe. 2. The Challenge DMC 2021 will run from 1 August to 31 October 2021. Participating teams will be given 5 days to deploy and run data transfer tests based on given scenarios: - 3 days to set up the software - 2 days to run the data transfer and demonstrate software to judges A video conference zoom session will be set up for participants to present their solution and results with interview from the judges prior to the data transfer demonstration test. 3. Network Topology 4. Data Mover Challenge Competition Schedule 7 teams will take part in the competition. And Raysync will run the data transfer tests from 27th Sep to 1st Oct. The winning team’s leader will be invited to attend SupercomputingAsia 2022 https://www.scasia.org/ in Mar 2021 in Singapore for an award presentation and solution showcase. 5. About Raysync Solution Basing upon UDP’s high-speed transfer protocol, Raysync breaks through the bottleneck of large file transfer which satisfies enterprises’ needs by building an intelligent transfer platform that processes and integrates massive data at an unprecedented speed. Our solutions enable you to send files of any size or format at full line speed, hundreds of times faster than FTP, while ensuring secure and reliable delivery. For more info and pricing packages, please visit www.raysync.io.

About this event Tired of sharing files with slow internet? Join our free webinar and live demo sessions to learn how Raysync offers you a high-speed solution that is 200 times faster than your traditional FTP transfer methods, utilizing up to 96% of your bandwidth that fulfil your demand efficiently! Date: 11AM - 12PM, 18th August 2021 In this webinar, you may learn: - Who we are? - Robust HPC & Raysync - Introducing Raysync: The Fast File Transfer Solution - A patented tranmission protocol utilizing up to 96% of your bandwidth and transfer files at long-distance across borders at maximum speed. - A complete enterprise solution for secure file-sharing, collaboration and management. - Product & Interactive Demo: - Demo: Transnational transfer between different locations - Demo: Download/Upload tests from participants - Showcasing the Admin Console & User Interface - Q&A + Prize Giveaways Win prize giveaways that worth $3599 during our interactive session: - 1x Raysync Enterprise License with Unlimited users - 2x Raysync SMB License with maximum 50 users - 10x Touch 'n Go Cash Credits worth RM20 More info regarding Raysync: We’re proud that Raysync - our Cross-Border, High-Performance and Large File Transmission Enterprise Solution, is able to tackle your needs. With its industry-leading core technology in the transmission engine, Raysync is able to transfer your files blazingly fast, in fact, 80-90% faster than your conventional FTP that fulfils your demand efficiently. Massive Small File Transfer Raysync is designed with a new data access technology to make sure that the upload speed for your small file transfers can reach up to 4,981 files per second and a download speed of 5293 files per second!! This translates to a transfer speed that is 200 times faster than FTP and 2 times quicker than your read/write speed on your local drives! This dramatically improves data transmission efficiency, stability, and effectively reduces data latency. Transfer Speed Acceleration Upgrade Raysync’s ultra-high-speed transmission operation is simple, with the transmission engine activated, it will allow the FTP transmission speed to be increased by a thousand times, achieving a speed ratio of 100:1 second. Based on the new UDP protocol and congestion control mechanism, our Raysync team utilise the new ACK algorithm to quickly recover any packet loss and avoid congestion queues, which greatly increases the transmission speed and maintains stability. Cross-Border Secure File Transfer Raysync adopts an advanced transmission technology that is unaffected by network delay and packet loss, making it more stable and efficient than the traditional file transmission technologies such as FTP, HTTP or CIFS. Raysync is also designed to be user-friendly and easy-to-deploy supporting cross-platform operations, free from file size and network type restrictions, thus enabling large-scale, and cross-border TB-levels large file transfers. Highlighted Features: - High-Speed Transfer: The unique transmission optimization protocol in Raysync provides businesses with the best network experience with 99.9% availability. - User-Friendly Interface: Standardized equipment is easy to install and supports bypass deployment to greatly reduce implementation costs. - Flexibility to Expand: The newly added networking point has zero impact on the original network structure and has superior scalability that help resolves the expansion of branches at any time. - Secure Data: Users can set passwords freely and encrypt them with asymmetric RSA/AES algorithm. The operation is blazingly fast and extremely secure while maintaining low consumption of system resources.

Big data transfer is becoming a new driving force for economic and social development, and is increasingly affecting economic operations, lifestyles and national governance capabilities. The security of big data movement has been improved to the level of national security. Based on the challenges and problems facing big data transfer security and the development of big data security technology, we put forward the following 5 opinions for the development of big data security technology. 5 Opinions for Big Data Transfer Security Technology 1. Build an integrated big data security defense system from the perspective of an overall security Security is a prerequisite for development. It is necessary to comprehensively improve the security of big data security technology, and then establish a comprehensive three-dimensional defense system that runs through the cloud management of big data applications to meet the needs of both countries. Big data strategy and it's market application. First, it is necessary to establish a security protection system covering the entire data life cycle, from collection to transfer, storage, processing, sharing, and final destruction. It is necessary to fully utilize data source verification, encryption of large-scale data transfer, encrypted storage in non-relational databases, privacy protection, data transaction security, prevention of data leakage, traceability, data destruction, and other technologies. The second is to enhance the security defense capabilities of the big data platform itself. It should introduce authentication for users and components, fine-grained access control, security audits for data operations, data desensitization, and other such privacy protection mechanisms. It is necessary to prevent unauthorized access to the system and data leakage while increasing attention to the inherent security risks involved in the configuration and operation of big data platform components. It is necessary to enhance the ability to respond to emergency security incidents that occur on the platform. Finally, it uses big data analysis, artificial intelligence, and other technologies to automatically identify threats, prevent risks and track attacks, and transition from passive defense to active detection. Ultimately, the goal is to enhance the security of big data from the bottom up and enhance the ability to defend against unknown threats. 2. Starting from attack defense, strengthen the security protection of big data platforms Platform security is the cornerstone of big data system security. From an earlier analysis, we can see that the nature of cyberattacks against big data platforms is changing. Enterprises are facing increasingly serious security threats and challenges. Traditional defensive surveillance methods will find it difficult to keep up with this change in the threat landscape. In the future, research on the security technology of big data transfer platforms should not only solve operational security issues but also design innovative big data platform security protection systems to adapt to the changing nature of cyber attacks. In terms of security protection technology, both open source and commercial big data platforms are in a stage of rapid development. However, the cross-platform security mechanism still has shortcomings. At the same time, the development of new technologies and new applications will reveal platform security risks that are not yet known. These unknown risks require all parties in the industry to start from the offensive and defensive aspects, invest more in the security of the big data platform, and pay close attention to the development trend of big data network attacks and defense mechanisms. It is necessary to establish a defense system suitable for and build a more secure and reliable big data platform. 3. Use key links and technologies as breakthrough points to improve the data security technology system In the big data environment, data plays a value-added role, its application environment is becoming more and more complex, and all aspects of the data life cycle are facing new security requirements. Data collection and traceability have become prominent security risks, and cross-organizational data cooperation is extensive, leading to confidentiality protection requirements that trigger multi-source aggregate computing. At present, technologies such as sensitive data identification, data leakage protection, and database security protection are relatively mature, while confidentiality protection in multi-source computing, unstructured database security protection, data security early warning, emergency response, and traceability of data leakage incidents, still relatively weak. Actively promote the development of industry-university-research integration, and accelerate the research and application of key technologies such as ciphertext calculations to improve computing efficiency. Enterprises should strengthen support for data collection, calculation, traceability, and other key links; Strengthen data security monitoring, early warning, control, and emergency response capabilities; Take data security key links and key technology research as a breakthrough; Improve the big data security technology system; To promote the healthy development of the entire big data industry. 4. Strengthen the investment in the industrialization of privacy protection core technologies, while considering the two important priorities of data use and privacy protection In the big data application environment, data usage and privacy protection will naturally conflict. Homomorphic encryption, secure multi-party computing, and anonymization technologies can strike a balance between the two and are ideal technologies to solve the privacy challenges in the application of big data. The advancement of core privacy protection technologies will inevitably greatly promote the development of big data applications. Currently, the core problem of privacy protection technology is efficiency, and its problems include high computing costs, high storage requirements, and lack of evaluation standards. Some researches, in theory, have not been widely used in engineering practice. It is very difficult to deal with privacy security threats such as multiple data sources or statistics-based attacks. In the big data environment, personal privacy protection has become a topic of much concern, and with the increasing demand for privacy protection in the future, it will drive the development and industrial application of dedicated privacy protection technologies. To improve the level of privacy protection technology in the big data environment, we must encourage enterprises and scientific research institutions to study privacy protection algorithms such as homomorphic encryption and secure multi-party computing, and at the same time promote data desensitization, audit applications, and other technical methods. 5. Pay attention to the research and development of big data security review technology and build a third-party security review system At present, the state has formulated a series of major decision-making arrangements for big data security. The government promotes the deep integration of big data and the real economy and emphasizes the need to effectively protect national data security. The National Informatization Plan puts forward an implementation plan for the big data security project. It is foreseeable that the government's supervision of big data security will be further strengthened in the future, the legislative process related to data security will be further accelerated, big data security supervision measures and technical means will be further improved, and the disciplinary work of big data security supervision will be further strengthened.

Facing the challenges and threats related to the security of big data transfer, the industry has conducted targeted practices and investigations on security protection technologies. This article focuses on three aspects of the development of big data security technology: platform security, data security, and privacy protection. How to securely improve big data transfer? Technologies related to platform security, data security, and privacy protection are improving, allowing us to solve big data security issues and challenges. However, to respond to new methods of cyber attacks, protect new data applications, and meet increased privacy protection requirements, higher standards and functions will be required. Improve platform security In terms of platform technology, centralized security configuration management and security mechanism deployment can meet the security requirements of the current platform. However, vulnerability scanning and attack monitoring technologies for big data platforms are relatively weak. In terms of technologies for defending platforms from network attacks, current big data platforms still use traditional network security measures to defend against attacks. This is not enough for the big data environment. In the big data environment, the extensible defense boundary is vulnerable to attack methods that cover up the intrusion. Besides, the industry pays little attention to potential attack methods that may come from the big data platform itself. Once new vulnerabilities appear, the scope of the attack will be huge. Improve data security In terms of data security, data security monitoring and anti-sabotage technologies are relatively mature, but data sharing security, unstructured database security protection, and data violation traceability technologies need to be improved. Currently, there are technical solutions for data leakage: technology can automatically identify sensitive data to prevent leakage; The introduction of artificial intelligence and machine learning makes the prevention of violations move toward intelligence; The development of database protection technology also provides a powerful way to prevent data leakage guarantee. The ciphertext calculation technology and the data leakage tracking technology have not yet been developed to the extent, which they can meet the needs of practical applications, and it is still difficult to solve the confidentiality assurance problem of data processing and the problems related to tracking data flow. Specifically, the ciphertext calculation technology is still in the theoretical stage, and the calculation efficiency does not meet the requirements of practical applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Improve privacy security In terms of privacy protection, technological development clearly cannot meet the urgent need for privacy protection. The protection of personal information requires the establishment of a guarantee system based on legal, technical, and economic methods. Currently, the widespread use of data desensitization technology poses challenges to multi-source data aggregation and may lead to failure. So far, there are few practical application case studies for emerging technologies such as anonymization algorithms, and there are other common problems with this technology, such as low computational efficiency and high overhead. In terms of computing, continuous improvement is needed to meet the requirements of protecting privacy in a big data environment. As mentioned earlier, the conflict between big data applications and personal information protection is not just a technical issue. Without technical barriers, privacy protection still requires legislation, strong law enforcement, and regulations to collect personal information for big data applications. Establish a personal information protection system that includes government supervision, corporate responsibility, social supervision, and self-discipline of netizens.

The ability to effectively use big data to gain value comes down to the ability of organizations to run analytical applications on the data, usually in the data lake. Assume that the challenges of capacity, speed, diversity, and accuracy are solved-measuring data readiness. The data is ready to pave the way for predictive analysis. Data readiness is built on the quality of the big data infrastructure used to support business and data science analysis applications. For example, any modern IT infrastructure must support data migration associated with technology upgrades, integrated systems, and applications, and can transform data into required formats and reliably integrate data into a data lake or enterprise data warehouse ability. 3 Challenges Faced by Big Data Transfer Technology Three big challenges facing big data technology, so why do so many big data infrastructures collapse early in the implementation life cycle? All this goes back to the last of McKinsey’s big data offer in 2011: “As long as the right policies and driving factors are formulated”. Some reasons why big data projects cannot be started are as follows: 1. Lack of skills Despite the increase in machine learning, artificial intelligence, and applications that can run without humans, the imagination to drive big data projects and queries is still a data scientist. These "promoters" referred to by McKinsey represent skills that are in great demand on the market and are therefore rare. Big data technology continues to affect the recruitment market. In many cases, big data developers, engineers, and data scientists are all on-the-job, learning processes. Many high-tech companies are paying more and more attention to creating and training more data-related positions to use the principles of big data. It is estimated that by 2020, 2.7 million people will be engaged in data-related jobs, and 700,000 of them will be dedicated to big data science and analysis positions to highly competitive and expensive employees. 2. Cost The value of the big data analytics industry is nearly $125 billion, and it is only expected to grow. For big data implementation projects, this means expensive costs, including installation fees and regular subscription fees. Even with the advancement of technology and the reduction of barriers to entry, the initial cost of big data may make the project impossible. Investment may require traditional consulting, outsourcing analysis, internal staffing, and storage and analysis software tools and applications. Various cost models are either too expensive, or provide the functionality of the minimum viable product, and cannot provide any actual results. But first, a company that wants to properly implement big data must prioritize architecture and infrastructure. 3. Data integration and data ingestion Before performing big data analysis, data integration must be performed first, which means that various data need to be sourced, moved, transferred, and provisioned into big data storage applications using technologies that ensure security. Control during the entire process. Modern integration technologies that connect systems, applications, and cloud technologies can help organizations produce reliable data gateways to overcome data movement problems. Companies striving to modernize their systems and deploy strategies to integrate data from various sources should tend to adopt a B2B-led integration strategy that ultimately drives the development of partner ecosystems, applications, data storage, and big data analysis platforms To provide better business value.

Nowadays, a large amount of analog and digital data is transmitted between global commercial networks in the form of data transfer. What is data transfer? Data transfer，is the transfer of data from one digital device to another digital device. This transfer takes place through point-to-point data streams or channels. These channels may have been in the form of copper wires before, but are now more likely to become part of the wireless network. As we all know, data transfer methods can refer to digital data, and the effectiveness of data transfer depends to a large extent on the amplitude and transfer speed of the carrier channel. The amount of data transferred in a given period is the data transfer rate, which specifies whether the network can be used for tasks that require complex data-intensive applications. Network congestion, delays, server health, and insufficient infrastructure can cause data transfer rates to be lower than standard levels, thereby affecting overall business performance. High-speed data transfer rates are essential for handling complex tasks such as online streaming and large file transfers. The importance of content delivery networks in data transfer High-quality delivery of websites and applications to as many locations around the world requires infrastructure and expertise to achieve low-latency, high-performance reliability, and high-speed data transfer delivery. Professional content delivery networks provide multiple advantages, including seamless and secure distribution of content to end-users, no matter where they are. The content delivery network uses complex node systems strategically located around the world to deliver content through more efficient use of network resources, thereby reducing the load on the enterprise's central server. Higher data rate conversion can improve user experience and increase reliability. By using intelligent routing and adaptive measures to find the best and most successful path in the case of network congestion, bottlenecks can be avoided-indicating that the amount of data flowing into network resources is too much to be processed. Faster data transfer FTP and HTTP are common methods of the file transfer. For example, FTP can be used to transfer files or access online software archives. HTTP is a protocol used to indicate how messages are defined and transmitted. It also determines the actions taken by web browsers and servers in response to various commands. HTTP requests are identified as stateless protocols, which means they have no information about previous requests. ISPs provide a limited level of bandwidth for sending and receiving data communications, which can lead to excessive slowdowns that enterprises cannot afford. As Raysync big data transfer, small file transfer is more than 5,000 per second, and millions of files can be listed in 5 minutes. The same file-second transfer speed can reach 20,000 per second, which is more than 100 times faster than traditional FTP.

If you just recorded a home video or created the final hybrid tape , then you will undoubtedly be eager to share it with your friends and family. Depending on the size and number of files you need to send, this may be a problem. For example, Gmail only allows you to attach files up to 25MB to emails. Not to mention the fact that large files lurking in the "Sent" folder will quickly take up your storage space quota! If you need to send large files online, there are many good ways to avoid trouble. We highlight the 12 best files transfer methods, most of which are free . Use a VPN What is the relationship between VPN and sharing large files? Some Internet service providers use broadband traffic management to regulate upload bandwidth . Using a VPN as our first choice ExpressVPN means that your ISP cannot determine the type of file you are uploading, so in theory, traffic shaping cannot be applied to your account. Use specialized services The new generation of file transfer service is browser-based and has built-in proprietary technology to accelerate the upload of large files. Raysync is one of them and specializes in transferring large files via the cloud. But the advantages are clear: Raysync is faster, more secure and more efficient than other transfer methods. In addition, Raysync offers a free trial opportunity. Use file compression One of the easiest ways to solve the problem of sending large files is to use file compression software, such as the cross-platform program 7-Zip. This is especially convenient if you have multiple files because you can put them in a folder and then compress them all at once. 7-Zip is suitable for Windows, Mac, and Linux, and can compress files into regular ZIP format, and it's slightly more efficient than 7ZIP. Most major operating systems can extract ZIP files without any other software. 7-Zip also allows you to set a password to protect the files, so you can share them safely. Please keep in mind that although uploading very large files may time out. Express 20TB external hard drive The fastest way to transfer massive large files is not via the Internet, but to use disk drives and messengers. All large cloud providers can use hard drives to transfer large amounts of data. Microsoft Azure charges a nominal fixed fee, which means that it costs approximately US$75 per storage device, but you must be prepared to provide your drives. This is similar to Amazon Web Services' import/export disk, while Google uses a third party. The WD My Book Duo external hard drive is priced at $749.99 and has a capacity of 20TB , making it the largest and most cost-effective device in its class. On a consumer-grade broadband line, it will take more than 500 hours to transmit the contents of a 20TB external hard drive on a 100Mb dedicated line, and it is expected to last more than a month and is only used for uploading. Remember to keep a copy of the file and encrypt the hard drive to be sent. 5. Google Drive Although Gmail messages can only contain attachments up to 25MB, when the file is too large, Google can give you the option to place it in Google Drive and send a link for sharing. Gmail users can share files and folders up to 10GB. Considering that Google's free tier can provide you with 15GB of storage space, so you can share large files repeatedly for absolutely free . Google allows you to choose to create a link that can be shared with anyone or just to create a link to the person who sent you an email with the link. 6. FTP Although FTP may be outdated compared to cloud services such as Dropbox and Google Drive, it is still one of the most reliable methods for uploading and downloading files. All operating systems support FTP, and many websites and add-ons support uploading and downloading from your browser, such as FireFTP. Windows and Mac users can also use the free desktop FTP client Cyber duck. The only disadvantage is that you need to have access to remote servers . Many companies like DriveHQ provide some free storage space , and the price is comparable to cloud storage providers. 7. Mediafire True to its name, MediaFire is a pioneer. Sign up for a free account and you will get 10GB of storage space. Connect your Facebook and Twitter accounts, install mobile apps, and refer friends to get up to 40GB of bonus space. You can upload files directly from your computer or the web and generate a link that will allow others to download the file from the MediaFire website. The paid subscription costs US$3.75 per month and includes 1TB of storage space, a 20GB file size limit, and the elimination of annoying verification codes and advertisements. Another convenient advanced feature is a one-time link, which ensures that once the recipient downloads the files, they can no longer be accessed. 8. Hightail Hightail has considered the needs of business users. After registration, you can create special "spaces" for various files and projects, which can then be shared with others. The convenient "PipPoints" function can even be used to take notes on documents while you and others are working on them. The free Lite version of Hightail only allows files up to 100MB to be shared. The Pro subscription is priced at US$12 per month, which includes unlimited workspace and supports files up to 25GB. There is also no limit to the number of people who can access a file at any given time. 9. WeTransfer WeTransfer is one of the simplest services for sharing large files. With just a few clicks, the website will automatically send you the files, which can be downloaded for 7 days. Everything is also very user-friendly, with a step-by-step wizard to guide you through the upload process. You can transfer up to 2GB on a free account, but for US$12 per month or US$120 per year , you can upgrade to WeTransfer Plus, which can transfer up to 20GB of files at a time, and 1TB Storage. Besides, you can choose to set a password to download the file-in addition, and customize the background and email as needed. 10. Resilio synchronization This handy tool used to be BitTorrent Sync, which uses the BitTorrent protocol and can directly synchronize files between devices. This peer-to-peer connection can be used for two or more devices, such as phones and desktop PCs. Resilio Sync also supports generating secure links to allow your contacts to download files from your folder. This naturally means that your device must be online at the time to be accessible. The software itself is provided for free, and there is no limit to how much data you can transfer or store. Note: Only "Personal Sync" is free. Sync Home adds more features and is priced at US$60 , while Sync for Family can accommodate up to 5 family members and retails for US$100 . 11. Send Anywhere Send Anywhere is suitable for almost every platform you can think of and can transfer files up to 10GB completely free. Which is suitable for Windows, macOS, Linux and Amazon Kindle, as well as plugins for WordPress and Outlook. The browser widget allows you to share files up to 10GB, but the paid service allows you to share files without restrictions. 12. Dropbox Sign up for this cloud storage service and you can share all files moved into the Dropbox folder using a web link. Some operating systems allow you to do this by right-clicking, while other operating systems may require you to log in to the site and click the "share" link. Most importantly, the person you send the link to does not have to be a Dropbox user-they just need to download the file from the site. Dropbox has a free tier that gives you 2GB of storage space, but you can earn more by referring friends to use the service-or by signing up for Dropbox Plus for $9.99 a month to increase the limit to 2TB. The latter also allows you to better control files, including version control and remote device wipe and can set download passwords. Conclusion To find a flexible solution with security and speed, cloud storage is the only way to go. Cloud solutions such as Raysync, Google Drive, Dropbox, and the like provide amazingly flexible pricing and even free storage.

1. MQTT data encryption transfer algorithm An improved MQTT protocol data transfer encryption algorithm MQTT-EA is proposed. In this algorithm, the IoT device side and the server-side randomly generate their private keys, then notify each other of their private keys and combine them into the final session master key through the algorithm, which is encrypted and decrypted by DES to transmit secure data. The attack on the data transfer process by adversaries A and B is simulated, and it is verified that MQTT-EA is safe under the premise that the session key generation algorithm is not leaked. 2. Summary of key protocols and application scenarios of time-sensitive networks With the development of information technology, there is an increasing demand for scenarios where things and things are the main body of communication, such as factory automation control, automatic driving, etc. The requirements for data transfer delay of this type of communication far exceed the controllable range of traditional Ethernet. , The time-sensitive network came into being. Time-sensitive networks are based on standard Ethernet, providing standardized technologies for deterministic information transfer, minimizing jitter through time-aware scheduling mechanisms, and providing reliable data transfer guarantees for time-sensitive applications. Through the description of relevant international standards for time-sensitive networks, the core features and mechanisms are introduced, and application scenarios such as in-vehicle networks, industrial Internet, avionics networks, and mobile fronthaul networks are analyzed and researched. 3. Design of LoRa's Remote Distributed Agricultural Environment Monitoring System To solve the problems of complex networking of traditional Internet of Things, short transfer distance, and high power consumption, and agricultural environment monitoring system based on LoRa technology is proposed. The system uses the peripheral functions of the STM32 microcontroller to drive the sensors to monitor a variety of environmental data and uses the LoRa wireless communication module to build a data transfer network. The summary node in the data transfer network receives all the data transmitted from the monitoring node, then packs the data and uploads it to the server through the General Packet Radio Service communication network. The upper computer developed by the C language can realize the monitoring data Real-time display and save. After testing, the system can accurately monitor agricultural environmental data in real-time, is stable and reliable, and can meet the needs of agricultural environmental monitoring.