The Internet of Things ecosystem is growing unchecked, and open source software plays a leading role. From IoT platforms to data tools and AI models for the edgeThere's a range of options that allows you to build powerful solutions with reasonable costs and great flexibility. If you're considering which technology to adopt, here's a complete and practical guide with the most relevant information.
Before we get into the subject, it's important to clarify some concepts. “Open source” is not exactly the same as “free software”And the choice between an open-source framework or a custom platform can make all the difference in the security, cost, and evolution of your project. Furthermore, if you work with resource-constrained devices, you'll also be interested in knowing which lightweight AI models perform well at the edge.
Free software vs. open source software
The terms are often confused, but they have important nuances. Free software prioritizes user freedomsThe freedom to run, study, modify, and redistribute the program, with the four freedoms of the FSF as its central focus. Open source, on the other hand, emphasizes the availability of the source code for viewing, modifying, and distributing. although licenses may impose conditions about redistribution or changes and do not always seek the same goals as the free software movement.
In other words, The focus of free software is on user rightsWhile open source focuses on open code and collaboration, there are overlaps, of course, but their philosophies and objectives are not identical.
What exactly is an IoT platform?
An IoT platform is the backbone that connects devices, data, and applications. It provides the infrastructure and building blocks to develop your service: communication with the devicesManagement and provisioning, security, cloud integration, and analytics tools, among others. In essence, It allows you to focus on business functionalities without reinventing the wheel in connectivity and operation.
Lightweight AI models for the IoT edge
In edge implementations with optimized memory, battery, and CPU, Compact and efficient models make all the differenceSome that stand out for their balance between size and capabilities are: Meta Llama 3.1 8B (multilingual efficiency and performance), GLM-4-9B-0414 (function calls and integration with tools) and Qwen2.5-VL-7B (multimodal intelligence with vision). These approaches are key to enabling local decisions without always depending on the cloud.
Open source IoT platforms and related topics
DeviceHive
DeviceHive offers an M2M framework for connecting devices and managing cloud services with a focus on Big Data. It includes a very simple web panel. to create networks, apply security rules and monitoring, plus sample projects and an online testing instance. Their proposal covers the bridge between cloud, embedded systems and mobile apps in a fairly balanced way.
ThingSpeak
ThingSpeak, closely linked to MathWorks, stands out for its analytics using MATLAB on near real-time sensor data. Among its functions These include live stream aggregation and analysis, recording of public channels for private use, channel sharing, visualizations, updates via REST API and MQTT, online MATLAB tools, and alerts with TimeControl triggered by events.
mainflux
Mainflux is a Golang stack that shines for its connectivity and management. Supports HTTP, MQTT, WebSocket and CoAPIt incorporates device provisioning and management, Docker deployment, and Kubernetes orchestration. security through customizable API keys and JWT with reachand helps reduce OPEX. It can be installed on-premises, in a hybrid environment, or in the cloud.
Thinger.io
Thinger.io is a cloud platform with deployment via Docker containers. It integrates a wide variety of hardware. (Arduino IDE, Linux, Sigfox, ARM Mbed boards), offers a user-friendly management console, streaming to websockets, real-time data dashboards, iOS and Android mobile apps and IFTTT automations for multiple devices. Although its visualization isn't the most visually appealing, Their "reactive programming" approach is their greatest strength..
Google Cloud IoT
Although it is not open source as such, Google's platform provides useful pieces for connected IoT architectures. Implement an MQTT bridge For connectivity, it connects to an external proxy network balancer. IoT apps can consume data via Pub/Sub or use Dataflow's MQTT connector, while the system offers key management services: credentials, authentication and authorizationDevice configuration and registration, rules engine, and update management are also common features. Digital twins, alerts and statistics in its ecosystem.
OpenRemote
OpenRemote facilitates integration for integrators, manufacturers, and administrations with protocols such as HTTP, SNMP, MQTT, or Bluetooth. Your strengths They include map and asset views, an asset model with user-defined types, model-adjustable agent protocols, a graphical interface for IF-THEN rules, dashboards, interconnection between Edge instances and a central and multi-tenant management. It is applicable to fleetsenergy, collective management, buildings, cities, airports or healthcare.
Open source or custom platform?
Although many open platforms may seem similar on paper, the choice depends on objectives, budget, risks, and team. Open source saves time and provides flexibilityHowever, it is not without its challenges: contributions of uneven quality, delicate maintenance and security, and increasingly critical data privacy. costs are not always low and a set of standard features that may not perfectly match your goals. Furthermore, They are not usually for inexperienced users..
When opting for a custom platform, the main argument is control. Have complete mastery of design, security, and functionality It allows integration with existing systems, seals end-to-end protection, and optimizes performance, reliability, and scalability. This approach, when well executed, maximizes return and prepare your solution to grow in a rapidly changing environment.
eManager solution and suite of tools
eManager was created as an industrial IoT controller for software professionals who need versatile, multi-protocol hardware where to easily implement projects. Their repository compiles a Top 8 list of acquisition, visualization, and storage software, designed for put real-world cases into production Quick.
Acquisition and visualization
Chirpstack provides a complete open-source stack for LoRaWAN networks with message translation, cloud integration, health management, inventory, activation of sensors and sending of data to devicesIt provides a web interface for users, organizations, apps, and devices, and exposes gRPC and REST APIs. Data can flow via MQTT/HTTP and be written in InfluxDB.
Node-RED, for its part, is the Swiss Army knife of IoT for orchestrating flows with little code. Its visual programming makes it easy to connect hardware, APIs, and services and transform them in real time, making it a top choice in Industry 4.0. If you're looking for agility without a steep learning curve, It's a must-have..
The TICK stack combines Telegraf (metrics and events agent), Kapacitor (real-time processing) and Chronograf (InfluxData interface), with InfluxDB as its foundation. Together they form an end-to-end solution to capture, monitor, visualize and automate on time series with heavy writing and query load.
Grafana completes the visualization circle with rich dashboards and alerts. It's perfect for leveraging InfluxDB data. and other sources, building attractive dashboards without complications. If you need to detect patterns or anomalies naked eyeHere's your ally.
Databases
InfluxDB, within the TICK stack, is optimized for time series: Lots of writing, quick queriesMonitoring, metrics, sensors, and real-time analytics. When every second counts, its architecture makes the difference.
MariaDB inherits the best of MySQL and adds improvements: caching for complex queriesNew connection management, cluster access, and support for advanced hierarchies and structures. If you're coming from the MySQL ecosystem, You will find it familiar and powerful.
PostgreSQL is the benchmark in open source relational databases, with high concurrency, varied data types, object orientation and cross-platform compatibility. It supports SQL for relational data and JSON for non-relational data., which makes it extremely flexible in hybrid projects.
SQLite is ideal for embedded systems and modest hardware: lightweight, efficient and fast for uncomplicated local storage. When the device is the primary concern and resources are limited, It gets you out of more than one tight spot..
What's new in the suite
The eManager range has updated its software to Node-RED 2.1.4 and Yocto Dunfell, also incorporating the UPnP protocol and other improvements. You gain in performance, update security, debugging, and loggingrefining the experience for demanding industrial deployments. If you want the finer details, consult the official documentation and stay up to date with their newsletter.
Open source: what it entails and why it matters
We call open source any tool whose source code is published and can be viewed, modified, and redistributedThis enables collaborative and transparent development where the community contributes bug fixes, new features, and quality improvements. Licenses typically permit these uses. to promote innovation and adaptation to specific needs.
There are plenty of examples: Linux in operating systems, Mozilla Firefox in browsers, or LibreOffice in office suites. The pattern repeatsWhen a big problem arises and there is no solution, an open project appears to address it.
Featured open source resources for data, backend, and more
1) Apache Hudi
Hudi offers a framework for real-time incremental storage and processing on top of Hadoop and Spark. Their focus on upserts, deletes, and incrementals This makes it perfect for continuous data ingestion and interactive analytics; it fits with data lakes and lakehouses, allowing low latency queries for large volumes. It integrates with Spark, Flink, Presto, StarRocks, or Amazon Athena.
2) Apache Iceberg
Iceberg provides a transactional table format with atomic writing, snapshots, optimized reads and partitioning/sortingIt works with Spark, Hive, Presto and other engines (ClickHouse, Dremio, StarRocks), solving HDFS/Hive bottlenecks in large datasets and facilitating scheme evolution, compaction and rollbacks.
3) Apache Superset
Superset is the self-service analytics platform that many teams need to scale. Connect with SQL, data warehouses and data lakesIt offers a chart builder and an SQL IDE, and supports everything from bar and pie charts to advanced geospatial visualizations. If your project has a strong BI component, It saves you time and headaches..
4) Bun
Bun concentrates into a single runtime tool, package manager and packager for server-side JS. Its greatest strength is performance (thanks in part to Zig) and the integrated experience, making it a serious alternative to Node and Deno. If you're interested in shaving seconds off CLI and build times, Give it a spin.
5) Claude 2
This Anthropic assistant handles huge contexts (up to ~100.000 tokens) and smoothly drafts or transforms text in multiple languages. You can summarize, extract, rewrite, and respond based on content, and understands common programming languages. Trained under the HHH principle (Helpful, Honest, Harmless). is less prone to dangerous exits and it doesn't train with your data or consult the internet to answer.
6) CockroachDB
Distributed, ACID-compliant, and highly available SQL database with automatic replication and horizontal scaling of reads and writesIdeal for high-transaction or multi-region deployment applications, it reduces latency and helps with regulatory compliance. Useful for organizations like Netflix and financial institutions. They use it in large-scale production.
7) CPython (recent improvements)
Python 3.11 and 3.12 have brought a tangible leap in performance of the reference performerbenefiting most projects without code changes. On the horizon, plans to address the GIL open the door to true parallelism in multiprocessing and additional earnings.
8) DuckDB
Embeddable analytical engine with columnar execution, parallelism and low consumptionPerfect for complex queries and interactive exploration on laptops or embedded systems. Compatible with standard SQL, ACID transactions, and integration with pandas and dplyr. Reduces friction between analysis and application.
9) HTML and Hyperscript
They propose going "full throttle" with conventional HTML: HTMX replaces repetitive JavaScript with Declarative attributes for AJAX, states, and dataHyperScript simplifies asynchronicity and DOM with HyperCard-style syntax. Together they offer a sober alternative to reactive frameworks when you're looking for speed without overload.
10) Istio
The ultimate service mesh: facilitates routing and load balancing, detailed observabilityEncryption, authentication, and authorization between microservices. It integrates with Kubernetes and separates network and security concerns of the code, standardizing policies in complex deployments.
Community and good practices
If you work with AI and automation tools, it's a good idea to participate in communities that share code, tips, and best practices. There are subreddits focused on programming with ChatGPT where interactions, usage tips, and complete projects are published; don't forget to read the rules before posting to maintain a useful and respectful environment.
Resources and expansion
If you're interested in reading more about open-source tools, there are extensive listings with dozens of projects covering development, data, analytics, AI, and ML. The previous selection ranges from 1 to 10 and continues further in subsequent articles. For more in-depth information, you can download documentation and technical guides, such as university reports and white papers, which They expand on the concepts of data lakes, warehouses, and lakehouses.Here is a reference example in PDF format. available for consultationAnd by the way, Follow us on Twitter and LinkedIn If you want to stay up to date with news.
Looking at the whole thing, it appears to be a very solid technological fabric: open IoT platforms with mature components (DeviceHive, ThingSpeak, Mainflux, Thinger.io, OpenRemote), cloud options that fit well with open workflows (Google Cloud IoT), an industrial suite like eManager with key tools (Chirpstack, Node-RED, TICK, Grafana, InfluxDB, MariaDB, PostgreSQL, SQLite), and a crop of open source projects for data and backend (Hudi, Iceberg, Superset, Bun, Claude 2, CockroachDB, CPython, DuckDB, HTML/Hyperscript, Istio) that They cover everything from ingestion to viewing and governanceWith all this, you have more than enough material to design a solid, secure, scalable IoT solution that is ready to grow without losing control.
