When deploying a LoRaWAN network, one of the most common questions is whether to choose an indoor gateway or an outdoor gateway. Although both types use similar communication technology, they differ significantly in protection level, installation environment, coverage characteristics, and deployment cost. This article explains the key differences between indoor and outdoor LoRaWAN gateways based […]

Retrofitting existing water meters into a LoRaWAN network is one of the most common requirements in smart water utility and irrigation modernization projects. However, many deployments fail due to high wiring complexity, unstable power supply, lack of remote configuration, and poor scalability. This article introduces a proven Smart Water Meter LoRaWAN Retrofit Solution based on

In industrial IoT retrofitting projects, connecting traditional wired sensors such as RS-485 and Modbus devices to LoRaWAN networks is a common requirement. However, existing protocol conversion approaches often compromise between network capacity, power efficiency, scalability, and development cost. This article analyzes the limitations of three conventional approaches and explains how EdgeBus (EB) redefines LoRaWAN terminal

In industrial IoT deployments, integrating Modbus RTU sensors into LoRaWAN networks traditionally requires heavy firmware development, register debugging, byte order handling, and complex reporting logic. This article provides a complete technical walkthrough on how to convert Modbus devices to LoRaWAN using EBHelper — a configuration-driven plugin in the EB compiler ecosystem — eliminating the need

Step 1: Obtain Official Decoder Script Official GitHub decoder: https://github.com/Milesight-IoT/SensorDecoders/blob/main/em-series/em300-sld/em300-sld-decoder.js ThinkLink supports ChirpStack format without structural modification. Step 2: Create a New Data Model In ThinkLink: Model Management → Create New Model Step 3: Configure Data Fields Important: Field identifiers must match decoder return keys exactly. Recommended fields: Save configuration. Bind Model to Device Device

Alarm management is a critical component of large-scale IoT systems, especially in LoRaWAN deployments where devices are widely distributed and operate unattended. ThinkLink provides a flexible alarm notification mechanism built on its native RPC framework and trigger linkage models. By implementing alarm logic entirely at the platform level, users can configure, adjust, and scale alarm

As legacy device retrofitting becomes a dominant IoT demand, integrating wired sensors into LoRaWAN networks remains a major challenge. ThinkLink, combined with EdgeBus (EB), enables flexible protocol abstraction and remote programmability at the edge, allowing RS-485, M-Bus, and analog sensors to be efficiently transformed into LoRaWAN nodes and delivered through a unified platform. Understanding the

In today’s rapidly evolving IoT landscape, LoRaWAN stands out with low power consumption, long-range, and wide coverage, making it the preferred communication technology for smart cities, industrial monitoring, smart agriculture, and energy management. However, building a stable, reliable, and maintainable LoRaWAN device goes beyond simple data transmission. Based on practical engineering experience, we summarize 12

1. Overview of ThinkLink Docker Edition The thinklink-docker repository provides Docker deployment configurations for the ThinkLink platform and currently supports x64 architecture only. Docker Compose allows all ThinkLink services to be started together, making it suitable for private deployment, project delivery, and testing environments. 2. Repository and Deployment Steps ThinkLink Docker repository:https://gitee.com/manthink_bj/thinklink-docker Deployment steps: Clone

1. Making Edge Logic Truly Programmable: The TypeScript EB Compiler EdgeBus (EB) was introduced by ManThink in 2018 as a lightweight execution framework designed specifically for retrofitting existing field devices. Initially developed for large-scale heating retrofit projects, EB addressed challenges such as fragmented protocols, constrained hardware resources, and unstable field conditions. Over years of deployment,