标签：#protocol

There are more IoT protocols out there than most teams will ever need. That sounds overwhelming until you realize most connected products only use two or three; one for local communication, one for cloud connectivity, and sometimes one for device management. The problem is not the number of options. The problem is that the protocol you pick at the design stage gets baked into your firmware, your cloud pipeline, and your data model. Change it later and you are rewriting half of your stack. Here is a practical breakdown of the protocols that matter for most developers building connected products today. The Three Layers You Are Choosing Across IoT protocols sit in three distinct layers, and you typically pick one from each: Application Layer → MQTT, CoAP, HTTP, AMQP (how data reaches your cloud) Network Layer → LoRaWAN, NB-IoT, LTE-M, Wi-Fi, BLE (how data travels physically) Industrial Layer → Modbus, OPC UA, Profinet (machine-to-machine on the factory floor) A soil moisture sensor on a farm might use LoRaWAN at the network layer to push data 10 kilometers to a gateway and MQTT at the application layer to deliver that data to a cloud dashboard. Two protocols, two layers, one product. The Big Three for Cloud Connectivity MQTT - The Default for a Reason Publish-subscribe model. Lightweight. Three QoS levels for delivery guarantees. Run over TCP with TLS encryption. Roughly 70% of cloud-connected IoT deployments use MQTT today. A basic publish looks like this: import paho.mqtt.client as mqtt client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2) client.tls_set() client.connect("broker.example.com", 8883) client.publish("sensors/temperature", '{"value": 23.5, "unit": "C"}') Use when: You need real-time telemetry, bidirectional device control, or guaranteed message delivery across unreliable networks. HTTP - Not for Telemetry, But Still Essential Too heavy for continuous sensor data. But it is the standard for OTA firmware updates, cloud API integrations, and management das

Building a Benchmarking Agent with A2A and MCP This tutorial aims to build and test benchmarking Agents using the A2A protocol across several mainstream Cloud providers. A Master Orchestrator Agent is exposed via MCP to allow Antigravity CLI to be used as a MCP client to co-ordinate the benchmarks. Deja Vu — What is Old is New! This paper is a re-visiting of the original benchmark series with Gemini CLI over Node, GO, and Python: Cross Language A2A Agent Benchmarking with Gemini 3 and Gemini CLI In this updated version, the Antigravity CLI is used to push Rust Agents cross-cloud and co-ordinate Mersenne Prime Calculations. Why would I need Multi-Cloud Support? And Rust? Can’t I just use Python? Most mature Agent development tools and libraries are Python based. Python allows for rapid prototyping and evaluation of approaches. Python is also an interpreted language- which has trade-offs in memory safety, and performance. Other languages like GO and Rust offer high performance and memory safe operations. With a language neutral communication protocol — the actual Agent implementation of each Agent can be coded in the most appropriate language. What is this Approach actually Benchmarking? The high level goal was to measure the actual time spent running an algorithm in the native language code inside the A2A agent. Each language had a slightly different implementation due to the language syntax. After running the algorithm- each Agent was instructed to calculate and return the elapsed time for cross cloud comparison. What is the A2A protocol? The Agent2Agent (A2A) protocol, an open communication standard for AI agents, was initially introduced by Google in April 2025. It is specifically engineered to facilitate seamless interoperability within multi-agent systems, enabling AI agents developed by diverse providers or built upon disparate AI agent frameworks to communicate and collaborate effectively. A good overview of the A2A protocol can be found here: A2A Protocol Lan

Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio