INFORMATION TECHNOLOGY OF IOT DEVICE INTEGRATION INTO WAREHOUSE MANAGEMENT SYSTEMS

Abstract

The paper substantiates a theoretical
model of an information technology for logistics process automation based on the integration of the Internet
of Things (IoT) and autonomous mobile robots (AMR). The author proposes a transition from conventional
centralized Warehouse Management Systems (WMS) to a multi-level architecture utilizing edge computing
principles. This approach mitigates data latency, resolves the "phantom inventory" issue, and ensures stable
processing of high-frequency telemetry in environments with high robotic unit density. The technological
stack is based on the Node.js event-driven environment, SQLite local storage, and the Server-Sent Events
(SSE) protocol for real-time streaming telemetry. At the hardware level, the system incorporates RFID,
BLE, and UWB technologies for high-precision real-time locating (RTLS), alongside the MQTT protocol
for reliable machine-to-machine (M2M) interaction. The scientific contribution of the study lies in the
development of the "Heat Sort" hybrid algorithm, which combines dynamic stock placement with multiagent reinforcement learning (MARL). This enables predictive collision avoidance and warehouse space
optimization through real-time "heat map" analysis of corridor congestion. The implementation of the
proposed technology establishes the framework for creating a warehouse digital twin, automating inventory
management, and deploying predictive maintenance systems.
Keywords: IoT, WMS, warehouse logistics, real-time monitoring, edge computing, Node.js, ServerSent Events, autonomous mobile robots, Heat Sort, multi-agent reinforcement learning

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