An open-source industrial IoT platform for device connectivity, telemetry collection, rule-chain processing, and multi-tenant management.
IoTSharp is an open-source industrial IoT platform that handles device access, data collection, processing, visualization, and management. It solves the problem of building and operating scalable IoT systems by providing integrated tools for connectivity, rule-based automation, and multi-tenant operations.
Developers and organizations building industrial IoT solutions, including those managing fleets of devices, needing real-time telemetry processing, or requiring multi-tenant SaaS capabilities.
Developers choose IoTSharp for its all-in-one, production-ready architecture, extensible protocol support, and flexible deployment options, allowing them to avoid vendor lock-in and customize the platform to specific industrial needs.
IoTSharp is an open-source IoT platform for data collection, processing, visualization, and device management.
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Supports HTTP, MQTT, CoAP, and extensible protocols for device connectivity, as highlighted in the README's 'Overview' and 'Connectivity' sections, enabling flexible integration with industrial hardware.
Offers rule-chain driven processing for transformation, notification, and automation, providing a no-code/low-code approach to business logic, detailed in the README's feature list and overview.
Can be deployed via Docker, Windows/Linux services, installers, and a Docker Desktop extension, as documented in the 'Deployment Options' section, ensuring adaptability to various production environments.
Built-in tenant-aware models for managing telemetry, assets, and users, facilitating SaaS-style IoT platform development, as described in the README under 'Platform domain' and key features.
The platform is built on .NET 10 with a Vue 3 frontend, which may not align with teams preferring other stacks like Node.js or React, limiting technology flexibility and increasing migration costs.
Requires careful setup for relational and time-series storage, message middleware, and appsettings, as noted in the docs links; this can be daunting for rapid prototyping or small teams without dedicated DevOps.
Focuses on rule-based processing but lacks native machine learning or advanced AI capabilities mentioned in the README, potentially requiring additional integrations for predictive maintenance or complex data insights.