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

TL;DR

WaggleNet introduces a low-cost, LoRa-MQTT-based system that simultaneously monitors internal hive conditions and external environmental factors with GPS-enabled, edge-cloud architecture. The three-tier design uses ESP32-based worker nodes (~$25–$30) and a gateway to deliver real-time data to a cloud database and mobile app, enabling contextual anomaly detection. Field tests show reliable operation over 110 m line-of-sight and successful indoor hive deployment, with end-to-end latency under 5 s and over two months of operation. The approach offers substantial cost savings and scalability for distributed apiaries, bridging the gap between internal hive monitoring and external environmental context for precision beekeeping.

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.

Figures (7)

• Figure 1: Three-tier system architecture: Worker nodes (LoRa) $\rightarrow$ Master node (LoRa-MQTT gateway) $\rightarrow$ Cloud services $\rightarrow$ Mobile application.
• Figure 2: Physical implementation of worker node with integrated sensors and LoRa antenna.
• Figure 3: Worker node block diagram showing sensor interfaces and data flow.
• Figure 4: Worker node operation flow with sleep mode for energy efficiency.
• Figure 5: Master node operation flow: LoRa reception $\rightarrow$ NTP timestamping $\rightarrow$ MQTT publish.