{"id":807,"date":"2023-08-18T13:14:00","date_gmt":"2023-08-18T11:14:00","guid":{"rendered":"https:\/\/itsonix.laolaweb.com\/?p=807"},"modified":"2026-05-13T13:15:45","modified_gmt":"2026-05-13T11:15:45","slug":"iot-data-transmission-with-a-poor-wi-fi-connection","status":"publish","type":"post","link":"https:\/\/itsonix.eu\/en\/blog\/iot-datenuebertragung-bei-schlechter-wlan-verbindung\/","title":{"rendered":"IoT \u2013 Data Transmission with a Poor Wi-Fi Connection"},"content":{"rendered":"

In a connected world where devices are increasingly communicating with one another, the Internet of Things (IoT) is at the heart of this technological revolution. In this article, our experts take a look at the challenges the Internet of Things (IoT) faces when it encounters an unstable Wi-Fi connection in an industrial setting.<\/p>\n\n\n\n

An excellent summary of the IoT comes from McKinsey<\/a>:<\/p>\n\n\n\n

\u201eThe Internet of Things (IoT) refers to physical objects equipped with sensors and actuators that communicate with computer systems via wired or wireless networks\u2014enabling the physical world to be digitally monitored or even controlled.<\/em>\u201c<\/p>\n\n\n\n

IoT devices are used in a wide variety of ways across different industries and sectors. Common use cases include, for example, smart automation, industrial monitoring and control, transportation and logistics processes, and energy management tasks.<\/p>\n\n\n\n

Monitoring plays an important role in many of these use cases. In such cases, IoT devices are often installed near the data source being monitored. However, this placement is not always ideal for strong Wi-Fi reception.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

While it is possible to resolve these issues with, for example, additional routers, better hardware, or mobile network connections, such measures would significantly increase hardware costs. The scalability of an IoT application depends heavily on keeping hardware costs under control. A more productive alternative would be to focus on the reliable transmission of the monitored data. To ensure efficient implementation, therefore, the computing power of the IoT device and the type of data being transmitted would need to be taken into account.<\/p>\n\n\n\n

Problem Statement<\/h2>\n\n\n\n

There are various causes of connection problems. Our IoT devices are installed in large halls with various metal structures. These metal structures can weaken Wi-Fi signals. In addition, there are other sources of interference, such as other Wi-Fi networks, electronic devices, and machinery. Another factor contributing to potential connection issues may be the number of IoT devices installed.<\/p>\n\n\n\n

Traditional solutions, such as using more routers or better hardware, are costly and cannot fully resolve connectivity issues. To ensure optimal audio capture while minimizing background noise, IoT devices must be installed in locations that are, however, suboptimal for a good Wi-Fi connection.<\/p>\n\n\n\n

We have developed an IoT device that records sounds and sends this audio data to a computer system for predictive maintenance. The IoT device contains standard microcontroller components such as a CPU, RAM, and various interfaces. It is also equipped with a sound chip, a microphone, and a light barrier. The device is designed to record the sounds of a conveyor belt or a sorting machine and transmit the data to a cloud system via MQTT. In the cloud, we plan to calculate maintenance forecasts for the respective parts of the conveyor belt or sorter.<\/p>\n\n\n\n

The photoelectric sensor helps us identify when the conveyor belt or sorter starts up and assign the recorded audio data to the corresponding machine component. While this does result in additional data that must be transmitted to the cloud, this data consists only of a measured time stamp, which needs to be transmitted only every few minutes.<\/p>\n\n\n\n

The recorded raw data amounts to several gigabytes per day. This makes it extremely impractical to send the entire raw data set to the computer system. Cost considerations, as well as the message limits imposed by the cloud system, require a reduction in the volume of data. The cloud system has set a message size limit of 4 kB and a limit on the number of messages.<\/p>\n\n\n\n

However, it is crucial to minimize the loss of audio data during transmission, as the maintenance prediction algorithm relies heavily on this data.<\/p>\n\n\n\n

To ensure cost-effective scalability, an innovative approach to data transmission is required. By optimizing data compression, adjusting the transmission rate, and efficiently tracking messages, we are able to overcome these challenges. In this way, we reliably deliver well-founded data for maintenance predictions in the cloud, even in demanding environments.<\/p>\n\n\n\n

Solution<\/h2>\n\n\n\n

Given the challenging installation locations of our IoT devices, the unreliable Wi-Fi connection, and the limited budget for MQTT messages, it is necessary to reduce the amount of data to be transmitted. By applying the Fast Fourier Transform (FFT) to the recorded audio data and further compressing it, we were able to achieve a significant reduction. We succeeded in compressing the data to such an extent that only one message per second per device needs to be sent.<\/p>\n\n\n\n

The FFT and compression calculations are performed exclusively on one core of the IoT device. With only two CPU cores, the second core handles other important tasks, such as managing the Wi-Fi connection, generating MQTT messages for the cloud system, time synchronization, and task monitoring. To effectively address the challenges associated with the reliable transmission of audio data, we must take the following limitations and circumstances into account:<\/p>\n\n\n\n

    \n
  1. Limited device resources: Our IoT device operates within limited processing power and memory constraints<\/li>\n\n\n\n
  2. Location-Related Disconnections: Regular connection interruptions are caused by suboptimal device placement in the environment<\/li>\n\n\n\n
  3. Network Overload: The presence of similar IoT devices using the same Wi-Fi network can contribute to connectivity overload<\/li>\n\n\n\n
  4. Message Tracking: Minimizing unnecessary retransmissions of data that has already been successfully transmitted is crucial for optimizing the overall efficiency of data transmission.<\/li>\n\n\n\n
  5. Impact of Data Volume: High data transfer rates increase the likelihood of connection interruptions<\/li>\n\n\n\n
  6. Stability Through Consistency: Consistent data transfers reduce the likelihood of connection drops<\/li>\n<\/ol>\n\n\n\n

    While the first three points are non-negotiable due to budget constraints and environmental conditions, we can use the design of our transmission algorithm to reduce the likelihood of connection interruptions.<\/p>\n\n\n\n

    The cloud system confirms the receipt of messages, making it easy to track received messages on the IoT device. Our tests show that out-of-order acknowledgments are almost never received, which makes message tracking efficient.<\/p>\n\n\n\n

    We use a circular buffer structure to temporarily store messages. This architecture stores a series of fixed-size elements. It facilitates the efficient insertion and removal of data by overwriting the oldest data when the buffer reaches its capacity. In our context, this buffer provides space for 5 minutes\u2019 worth of data\u2014a sufficient size that has been confirmed in testing. If this buffer proves insufficient in the event of connection interruptions, this indicates a larger environmental issue. In such cases, it is better to accept the data loss and initiate a new transmission. Attempting to recover the missing data is unlikely to be successful in these situations.<\/p>\n\n\n\n

    We reduce the likelihood of connection interruptions by maintaining a steady data transmission rate. Let's consider the following situation:<\/p>\n\n\n\n

    If a sensor experiences a connection interruption and then attempts to send the collected data immediately after the connection is restored, the risk of another connection interruption increases. This situation can also lead to connectivity issues for neighboring sensors due to Wi-Fi network congestion. By transmitting data continuously in consistent batches, we stabilize Wi-Fi connections and reduce the likelihood of connection losses. Thus, instead of one message, two messages are transmitted consecutively, which ensures a steady data volume and increases the likelihood of uninterrupted data transmission. After the data stored during the interruption has been transmitted, the transmission rate drops back to one message per second.<\/p>\n\n\n\n

    With this strategy for transferring our recorded data to the cloud system, we minimize data loss and can analyze the data to generate reliable maintenance forecasts.<\/p>\n\n\n\n

    Conclusion<\/h2>\n\n\n\n

    An effective solution is achieved through a thorough analysis of the problem at hand. In our case, we recognize the potential for data compression and the constant nature of data output. By leveraging these factors, we develop an algorithm for data transmission that reduces the likelihood of connection interruptions while also preventing the IoT device from becoming overloaded with tasks.<\/p>","protected":false},"excerpt":{"rendered":"

    In einer vernetzten Welt, in der Ger\u00e4te zunehmend miteinander kommunizieren, steht das Internet der Dinge (IoT) im Mittelpunkt dieser technologischen Revolution. In diesem Beitrag werfen unsere Xperten einen Blick auf die Herausforderungen, denen das Internet der Dinge (IoT) gegen\u00fcbersteht, wenn es auf eine instabile WLAN-Verbindung im Industriekontext trifft. Eine hervorragende Zusammenfassung des IoT stammt von […]<\/p>\n","protected":false},"author":4,"featured_media":809,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[22],"tags":[],"class_list":["post-807","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation-space"],"acf":[],"yoast_head":"\nIoT \u2013 Daten\u00fcbertragung bei schlechter WLAN-Verbindung - IT Sonix - Ihr Partner f\u00fcr individuelle Softwarel\u00f6sungen<\/title>\n<meta name=\"description\" content=\"it Sonix Blog: IoT \u2013 Daten\u00fcbertragung bei schlechter WLAN-Verbindung\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsonix.eu\/en\/blog\/iot-data-transmission-with-a-poor-wi-fi-connection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"IoT \u2013 Daten\u00fcbertragung bei schlechter WLAN-Verbindung - 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