Toward Resilient Rural Resource Governance: A Cost-Effective, Data-Driven IoT System for Sustainable Beehive Monitoring and Management

Beekeeping occupies a challenging intersection: it is economically vital across vast, under-served rural areas, yet the tools available to manage it rarely match that scale. This paper presents a low-cost, solar-powered IoT system designed to address this gap. Rather than treating beehive management as a narrow agricultural concern, the system frames it as a case study in distributed, data-driven resource governance. Each hive is fitted with an ESP32-based sensor node that tracks temperature, humidity, and equivalent CO2 (eCO2), while solar-powered base stations provide GPS positioning and intrusion detection. All components are connected through a tiered wireless architecture: a 100 m 2.4 GHz RF sensor link connects individual hive nodes to solar-powered base stations, which forward aggregated telemetry to the cloud over a 120 m Wi-Fi or cellular uplink. The system was evaluated in an active apiary across five hives and two solar-powered base stations, which operated continuously for two weeks without failure, including during periods of darkness. The sensors are supported by a four-layer backend architecture that converts raw readings into real-time alerts and monitoring dashboards, providing a decision-support layer designed to assist beekeeping associations and agricultural authorities. The modular backend enables these organizations to coordinate operations, such as pesticide-spraying schedules and hive density across branches, while establishing a practical software foundation for future integration of AI-based analytics and additional sensing modalities. Taken together, the results present a practical, low-cost model for extending resilient, sustainable resource monitoring into rural and agricultural settings, with direct relevance to Saudi Arabia’s beekeeping sector and potential applications in similar deployments elsewhere.

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Publication Details

Journal
Sustainability
Published
2026-10-13
DOI
https://doi.org/10.3390/su182010404
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Toward Resilient Rural Resource Governance: A Cost-Effective, Data-Driven IoT System for Sustainable Beehive Monitoring and Management

Kais Belwafi, Achraf Gazdar, Ameur A. Touir, Sofien Gannouni et al.
Sustainability
Smart Agriculture and AI
article

Toward Resilient Rural Resource Governance: A Cost-Effective, Data-Driven IoT System for Sustainable Beehive Monitoring and Management

Kais Belwafi, Achraf Gazdar, Ameur A. Touir, Sofien Gannouni, Ahmad Alghamdi
article en

Abstract

Beekeeping occupies a challenging intersection: it is economically vital across vast, under-served rural areas, yet the tools available to manage it rarely match that scale. This paper presents a low-cost, solar-powered IoT system designed to address this gap. Rather than treating beehive management as a narrow agricultural concern, the system frames it as a case study in distributed, data-driven resource governance. Each hive is fitted with an ESP32-based sensor node that tracks temperature, humidity, and equivalent CO2 (eCO2), while solar-powered base stations provide GPS positioning and intrusion detection. All components are connected through a tiered wireless architecture: a 100 m 2.4 GHz RF sensor link connects individual hive nodes to solar-powered base stations, which forward aggregated telemetry to the cloud over a 120 m Wi-Fi or cellular uplink. The system was evaluated in an active apiary across five hives and two solar-powered base stations, which operated continuously for two weeks without failure, including during periods of darkness. The sensors are supported by a four-layer backend architecture that converts raw readings into real-time alerts and monitoring dashboards, providing a decision-support layer designed to assist beekeeping associations and agricultural authorities. The modular backend enables these organizations to coordinate operations, such as pesticide-spraying schedules and hive density across branches, while establishing a practical software foundation for future integration of AI-based analytics and additional sensing modalities. Taken together, the results present a practical, low-cost model for extending resilient, sustainable resource monitoring into rural and agricultural settings, with direct relevance to Saudi Arabia’s beekeeping sector and potential applications in similar deployments elsewhere.

SustainabilityVol. 18(20)
King Saud University (SA), University of Sharjah (AE)
King Saud University
Openalex Percentile: Top 15%
Smart Agriculture and AI
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