WaggleNet: A LoRa and MQTT-Based Monitoring System for Internal and External Beehive Conditions

TL;DR

WaggleNet is a cost-effective, dual-scope monitoring system using LoRa and MQTT that captures internal hive and external environmental data, enabling better beekeeping decisions and health assessments.

Contribution

It introduces a novel low-cost, scalable monitoring system that integrates internal and external hive data using LoRa-MQTT architecture, enhancing contextual analysis for beekeeping.

Findings

Reliable data transmission over 95 meters in obstructed environments

Stable operation with end-to-end latency under 5 seconds

Continuous two-month deployment across diverse conditions

Abstract

Bee populations are declining globally due to habitat loss, pesticide exposure, and climate change, threatening agricultural productivity and food security. While existing smart beehive systems monitor internal conditions, they typically overlook external environmental factors that significantly influence colony health, and are constrained by high cost, limited scalability, and inadequate contextual analysis. We present WaggleNet, a novel dual-scope monitoring system that simultaneously captures both internal hive conditions and external environmental parameters using a cost-effective LoRa-MQTT architecture. Our system deploys modular worker nodes ($\sim$\$15 each) equipped with temperature, humidity, light, and GPS sensors both inside and around beehives. A master node functions as a LoRa-MQTT gateway, forwarding data to a cloud server with a mobile application interface. Field experiments confirmed reliable operation with 100\% packet delivery over 110 meters in line-of-sight conditions and 95 meters in obstructed environments, including successful deployment inside wooden hive structures. Our system demonstrated stable end-to-end latency under 5 seconds and continuous operation over a two-month period across diverse environmental conditions. By bridging the gap between internal and external monitoring, WaggleNet enables contextual anomaly detection and supports data-driven precision beekeeping in resource-constrained settings.