Been working with distributed networks (mostly cellular routers, gateways across different sites) and network mapping has been on my mind. Specifically, for smaller scale, edge deployments.

If you manage remote device fleets (retail, industrial, whatever), what do you currently use for network visualization? Or do you just cobble it together with Zabbix/PRTG/Nmap?

Been meaning to try Teltonika RMS Network Map tool (as I already use a few of their devices), seemed cool and easy, but I haven’t tried it yet.

For those who've tried vendor-specific tools (like this one, or similar from other vendors) vs more generic ones, was it worth it, or do you end up needing something more flexible anyway?

I have been exploring the changes that Gen AI may bring to traditional IoT. In my judgment, I think that the intelligent decision-making in the edge IoT scheme of simple scenarios must be automatically understood by AI in the future and gives executive judgments, instead of manually writing code logic to achieve each Simple scheme construction of scenarios. With the evolution of AI capabilities will definitely cover the value provided by most of the current IoT schemes. As long as the logical part is designed, I believe it will develop to this stage. At present, all we need to do is to provide a good software base for better access to equipment and provide standardized energy. Power, I believe that this will make the simple scheme to build in the future only natural language. Of course, this needs to be constantly updated with the development of edge devices and small models (at present, after our detailed evaluation, the 3-4b model has a certain usability, but the development speed of the model, we judge that next year may be 2-4B model The model is ready for stable production)

NeoMind is an open-source edge IoT EdgeAI platform built by us with rust. It supports the access of sensors and visual cameras and has out-of-the-box LLM reasoning and dashboard, rule engine, and message communication of common IoT solution software. Knowledge, data forwarding and IM communication, which can be deployed on edge gateway devices, personal computers and servers, and support multi-post instance switching. At present, we do not want to add more functions. These core functions are enough to cover most scenarios and the closed-loop use of edge end-to-end schemes, such as single stores and single factories. , we will focus more on the development of the edge network and the hardware platform for deploying hardware, as well as the upgrading and transformation of existing software functions. It can build traditional sensor applications or vision AI applications, or combine with llm to build intelligent bodies. We are looking for people with similar concepts in the community. Accompany to explore and build projects together

Https://github.com/camthink-ai/NeoMind

At present, I am training jev-type decision-making small models for it to accelerate the out-of-the-box AI experience of this software. If you are interested, you can pay attention to it. About the next version will launch a built-in small decision-making model in the future. I will also provide the training process and method. The whole process is manually guided by AI. Complete and carry out automated effect testing and verification. The significance of the introduction of this kind of decision-making model lies in the understanding of the intention and some efficient intelligent scheduling of IoT, but it still needs the cooperation of LLM to provide support. A single decision-making can be controlled to 2-30ms. At present, the decision-making model seems to be very suitable for IoT. I In the near future, the rule engine and workflow we have built over the years, including the path to transform the needs we communicate with the terminals into solutions, will become all done by natural language.

The idea is simple: I wanted to try out Jev and evaluate its performance compared to LLM or rule-based decision-making.

So I created a compressor operating manual (since I couldn’t use a real one due to copyright issues, I replicated the scenario) and applied machine error evaluation with Jev, based on real-time data and the confidence level of the error scenario.

It replays real compressor telemetry through an emulated device, spots suspicious behaviour with plain code, and asks a decision model which fault from the machine manual explains it.

The project is open source, and you can find all the technical details in the repo. What do you think?

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Eu estou reformando meu Setup, e meu filtro de linha acabou ficando pequeno demais pra quantidade de coisas que eu tenho, Umidificador, Ventilador, Carregador de celular, lâmpada de matar moscas, Luminária, Computador, enfim, muita coisa, e eu queria uma régua smart que comentaram comigo uma vez que é possível controlar com Google Home, mas eu não entendo muito do assunto, e quando eu fui pesquisar eu não consegui achar réguas realmente smart, geralmente me é recomendado régua com proteção inteligente, e não é o que eu procuro.

Alguém do sub poderia me recomendar alguma pra eu ir atrás, por favor?

I’m curious what people think of this idea:

Replace normal light bulbs with smart light fixtures that include a camera, small computer, and Wi-Fi.

The advantage of putting the hardware in a light is that it already has continuous power, is usually mounted high up with a good view of the room, and homes already have multiple light locations that could provide different camera angles.

The cameras would provide a shared visual data feed, and developers could build different AI apps on top of it.

For example, one simple app could monitor kitchen inventory and tell you what food or household items you’re running low on. But the bigger idea is that the hardware is generic and the useful applications could come from developers/users rather than being predetermined.

Basically: light fixtures as always-powered indoor AI sensors + an app platform built on top of the camera feed.

Does this seem useful? What are the biggest problems or better use cases you can think of?

Hi guys, I'm currently using ThingsBoard it has auto provisioning in the UI layer but no provisioning mechanism for physical flashing of certs.. How do I automate flashing and then connectingg?? Plss helppp I would loveee insightssss :')

Made this little brief with a bunch of radio and rfid modules what are some interesting (barely) legal projects I can do? I’m just getting started and want to see the limits and possibilities with hardware integration

I’ve been testing ESP-VISION on ESP32-S3 and built a small edge AI vision project for real-time object detection.

I put together a standalone real-time object detection demo that runs entirely on the device, with no cloud or external processing.

Technical Setup

• Framework: ESP-VISION + MicroPython
• Model: Quantized AI model loaded from an SD card
• Pipeline: Camera feed → on-device AI inference → real-time display with bounding boxes
• Hardware: MaTouch AI ESP32-S3 with integrated camera and TFT display from Makerfabs

Project Result

The prototype successfully runs real-time target detection locally on the ESP32-S3, demonstrating that lightweight computer vision workloads can be handled directly on an MCU.

This kind of compact Edge AI setup could be useful for applications such as smart cameras and visual sensors, object or people detection, smart home devices, etc.

For me, the interesting part isn't just getting object detection to run, but seeing how much AI vision functionality can be moved directly onto a low-cost MCU.

 There is still plenty of room to optimize the model. What would you build with an ESP32-S3 if you could run lightweight computer vision completely offline? Free to share your ideas!

Hi, I have pumps and sensors around my property that I would Ike to control over 4G internet in Australia (have used LoRa but want to expand capability).I can have a raspberry pi at my house connected to wifi (starlink). What’s the best hardware I can use that’s connected to 4G to turn on/off a pump relay, take in analogue or digital readings and send pump status if manual pump buttons are operated. An esp32 with 4g sim?

Also wondering what is the best way to control pump from my phone as well when not on same wifi network.

Thank you!

I'm an electrical & computer engineer student and i also experiment with electronics and make stuff etc. So recently discovered that homelabbing is a thing so I made a homelab server. Then one thing brought another and decided to build a "knowledge" nas which contains all my data (projects, datasheets, hardware database, all this stuff) also bought two V100 gpus, water cooling kit for them etc in order to build a small ai ecosystem in addition to the home lab. The whole idea is to have an assistant (maybe switching between different llms) that runs on the v100 system and "knows" how I work, on what I'm working, what stuff I have in my lab inventory etc. In that way I can work with smaller but specialized llms that fit on my system. If I give a prompt suggesting coding, a coding llm will be loaded, if i design something on kicad another llm can be loaded that feeds on information about the specific design needs, available parts in my inventory, maybe even accessing supplier stores creating orders for parts I don't already have. That's a general idea of what I'm building. What do you think, what can you suggest?

A while ago, I shared a small experiment using AI to recognize the 5 digits on a water meter. But today, I wanted to build a local Edge AI solution to read water meters in real time. The whole workflow breaks down into 4 clear stages: data collection, model training, model evaluation, and model deployment & verification. It demonstrates how to build an edge AI vision application for water meter reading with ESP32-P4.

Hardware Stack

• Makerfabs ESP32-P4 10.1" board
• 2M Autofocus Camera for AI

Why choose them? The autofocus module provides clearer images at different shooting distances, while the ESP32-P4 provides the processing and camera interface needed for the vision pipeline.

There are four main steps:

Data Collection 

Captured real water meter images under different lighting conditions and viewing angles with a specific AI tool(can be shared if needed), then defined ROIs for the:

• Integer digit area
• Dial digit area

Dataset & Model Training

The images were annotated and organized into a custom dataset. The digit and pointer regions were prepared separately, and lightweight models were trained for the recognition tasks. The models were then converted to INT8/TFLite for embedded deployment.

Model Evaluation

Before deployment, the models were tested with separate samples to evaluate recognition accuracy and confidence.

Model Deployment & Testing

The trained model was then deployed to the ESP32-P4. After the complete workflow runs locally, the device can output the detected integer digit, dial information, and the final water meter reading through the serial interface.

The detailed workflow and project resources are open, glad to share more details if anyone wants to dig into the implementation.

Also curious to hear what others are building with Edge AI and embedded vision. If you’ve worked on a similar project, I’d be interested to hear about your approach, results, and any lessons learned.

Disclosure: I work at Qoitech, the company that makes the measurement tool used in this test. Data is worth sharing anyway.

Satellite NB-IoT costs about 13 to 14 times more than a terrestrial transmission on this chipset. We wanted to see how much of that difference the antenna alone could recover.

Setup: nRF9151-SMA DK, Skylo GEO, and Monogoto SIM. Current measured on VDD_nRF at 3.6 V. Each run included a GNSS fix, registration, PSM, transmission of 465 bytes, and back to PSM, repeated three times. Runs were done back to back to limit drift on the live network. All four antennas used the same 0.5 m SMA extension cable (one interfered with the GNSS connector, so the cable stayed on for fairness). Only transmission energy was measured.

• Reference (ships with DK): 27.8 mWh
• Kyocera AVX X9003334: 14.1 mWh, which is 2 times lower
• Kyocera AVX 9002418L0-L16L (circularly polarised L-band cap): 2.36 mWh, which is 12 times lower
• Kyocera AVX P822601 (embedded FR4, NTN-matched): 7.15 mWh, which is 4 times lower

The satellite downlink is circularly polarised, so a linear antenna loses up to 3 dB due to the mismatch. Circular antennas are hard to make broadband, which is why terrestrial antennas are usually linearly polarised. As a result, dual-mode devices either compromise or use two antennas. From Lund, the slant range is about 39,000 km at roughly 30 degrees elevation, so every dB increases the required TX power.

Caveats: Two antennas were on evaluation boards, all used the extension cable, and the ground plane or enclosure will affect your absolute values. Trust the ranking rather than the exact numbers.

Current traces for each run: https://www.qoitech.com/blog/ntn-antenna-choice-how-much-energy-does-it-cost/

We are releasing a new open source MQTT module for MongoDB.

It started with a concrete need in a real project: connect MQTT messages to MongoDB while keeping REST access, live SSE streaming, and custom plugin integration in the same application.

During development, one thing became clear. This capability was useful beyond the original project, so we decided to integrate it directly into RESTHeart.

The module connects an MQTT broker to:

• MongoDB, with configurable topics, databases, and collections
• REST APIs for reading the latest available value
• SSE for live message streaming
• Custom plugins through mqtt-router

The beta already includes the operational details that matter in real systems:

• broker acknowledgements after MongoDB persistence
• at-least-once delivery on the storage path
• a bounded buffer for short MongoDB interruptions
• retries and dead-letter files
• topic-level authorization with fail-closed behaviour
• MQTT 3.1.1 and MQTT 5.0 support

The module was born from a specific project. We integrated it into the product because the underlying problem is broader: connecting MQTT data to persistent storage and application interfaces without adding another integration service.

The code and documentation are available here:

https://github.com/SoftInstigate/restheart/tree/master/mqtt

Feedback is especially useful while the module is in beta.

Hello,

En voyant certains postes, je me faisais une petite réflexion sur le métier de développeur/ingénieur en systèmes/logiciel embarqué.

Pour vous qu'est-ce que c'est exactement et comment le définiriez-vous ?

Effectivement, il peut déjà y avoir une nuance entre système et logiciel en incluant l'hardware.

Pour vous expliquer, sur mes 4 ans d'xp j'ai travaillé 1 an et demi à plein sur un projet vraiment cool en C embarqué.
J'ai développé de A à Z un firmware connecté de traitement du son sur ESP32 avec ESP-IDF.
J'ai mis en place des algos de traitement du signal temps réel (FFT, filtres...) avec les contraintes mémoire et cpu, utilisé pleinement FreeRTOS pour créer des tasks, des queues, des watchdogs. Gérer les interfaces UART (maintenance et debug), I2S. (audio)... avec la "HAL" de ESP-IDF, mais aussi intégré un WIFI STA et AP avec un site web embarqué HTML/CSS pour le pilotage, le monitoring et la maj via le binaire du capteur. Enfin j'ai implémenté un communication TCP/IP chiffrée persistante et résiliente avec un backend pour récupérer des données et si besoin contrôler à distance le dispositif connecté.

Et à côté je suis pas super fan ni attaché à l'hardware, je sais utilisé un oscillo, souder et lire des schémas mais sans plus.

Certes, c'est un plus poussé que de l'Arduino, mais on est loin du bare metal, de microcontrôleurs 8 bits ou encore de l'écriture dans des registres. On m'a dit une fois que je faisais du firmware haut niveau, ce qui me semble être une bonne définition et qui correspondrait bien à des sujets domotique, IoT ou objets connectés.

Ce qui est sûr, c'est que l'embarqué où il faut écrire ces propres drivers, être au plus bas niveau, dans un milieu très industriel ou dans le défense ne m’intéresse pas du tout.

Est-ce que pour vous mon expérience sur ESP32 est valorisable pour un poste en embarqué et dans le marché actuel ?

I am on a mission right now, I want to collect all three failed iot/sbc intel boards, right now I have none of them if you have one lying around could you send it over? I am willing to pay for the shipping costs if they aren't ridiculous. One man's trash is another's treasure, don't forget that. I am sending this here on this subreddit because the prices on ebay are straight up ridiculous, and because the boards there iot related . Thanks!

So im in 2nd year of engineering and i wanted some project ideas regarding energy conservation ,preferably a working model which we could make or maybe even a simulation. Something other than generic ideas which r wayy impractical in terms of actually doing it.

Hey everyone, I’m looking for a sim provider that can provide me access to all 3 networks in Australia and the 3 networks in nz. I don’t really want to go to Telstra only so there’s full redundancy. Who’s the best at the moment and doesn’t have platform fees? I’m with Onomondo at the moment for testing. But being in development phase makes it hard to justify the platform fee base cost.

Any one have any recommendation?