BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Cota, Duarte; Martins, José; Mamede, Henrique São; Branco, Frederico

doi:10.1016/j.joitmc.2023.100110

Cited by 6 publications

(4 citation statements)

References 77 publications

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“…IOHIVE, an IoT-based platform that assists beekeepers in monitoring the temperature, humidity, and weight of the hive, is presented by Chamaidi et al in [18]. A beehive monitoring system based on IoT and microservices, BHiveSense was most recently introduced by Cota et al [19]. It consists of a number of components, including a web application, a mobile application, a REST back-end API, and a low-cost hive-sensing prototype.…”

Section: Related Workmentioning

confidence: 99%

Enabling Robust Sensor Network Design with Data Processing and Optimization Making use of Local Beehive Image and Video Files

Namugenyi,

Tugume,

Kigwana

et al. 2024

Artificial Intelligence and Big Data

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There is an immediate need for creative ways to improve resource efficiency given the dynamic nature of robust sensor networks and their increasing reliance on data-driven approaches. One key challenge faced is efficiently managing large data files collected from sensor networks for example optimal beehive image and video data files. We offer a revolutionary paradigm that uses cutting-edge edge computing techniques to optimize data transmission and storage in order to meet this problem. Our approach encompasses data compression for images and videos, coupled with a data aggregation technique for numerical data. Specifically, we propose a novel compression algorithm that performs better than the traditional Bzip2, in terms of data compression ratio and throughput. We also designed as an addition a data aggregation algorithm that basically performs very well by reducing on the time to process the overhead of individual data packets there by reducing on the network traffic. A key aspect of our approach is its ability to operate in resource-constrained environments, such as that typically found in a local beehive farm application from where we obtained various datasets. To achieve this, we carefully explore key parameters such as throughput, delay tolerance, compression rate, and data retransmission. This ensures that our approach can meet the unique requirements of robust network management while minimizing the impact on resources. Overall, our study presents and majorly focuses on a holistic solution for optimizing data transmission and processing across robust sensor networks for specifically local beehive image and video data files. Our approach has the potential to significantly improve the efficiency and effectiveness of robust sensor network management, thereby supporting sustainable practices in various IoT applications such as in Bee Hive Data Management.

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Section: Related Workmentioning

confidence: 99%

Enabling Robust Sensor Network Design with Data Processing and Optimization Making use of Local Beehive Image and Video Files

Namugenyi,

Tugume,

Kigwana

et al. 2024

Artificial Intelligence and Big Data

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“…Cota et al proposed a system called BHiveSense, which is an information system for remote monitoring and management of apsiaries based on microservices and IoT [20]. The goal is to "monitor honeybee colonies to promote more sustainable resource usage and maximize productivity.".…”

Section: B Microservices Architecturementioning

confidence: 99%

“…The great system behind the IPS is crucial for realtime data retrieval. There are few architectures to build systems, such as monolith [16] and microservices [17], [18], [19], [20]. Monolith's modules are dependent on each other, while modules in microservices are independent.…”

Section: Introductionmentioning

confidence: 99%

Microservices for Asset Tracking Based on Indoor Positioning System

Sasmita,

Putra Kusuma

2024

IJCDS

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Indoor positioning systems (IPS) are widely used for different use cases, but most of them are for asset tracking and indoor navigation. Asset tracking, for instance, might help industries be more efficient, such as warehouses, stock recording, guest trackers, and many more. Implementation of asset tracking needs to have the IPS, such as trilateration and fingerprinting. To have an accurate location, it is not just precise; the data that will be consumed also needs robust services to process all that data in almost real time. Bluetooth low energy (BLE) is used to send the received signal strength indicator (RSSI) to the microservices-based server. To support this, microservices architecture (MSA) is designed with a Service-Oriented Modeling and Architecture (SOMA) framework to translate business goals into necessary services. We were implementing and comparing both MSA implementation strategies, which are orchestration and choreography strategies, on cloud computing with the Kubernetes platform. These strategies were compared to find the most resource-efficient with the biggest number of served requests. The bigger the served request number, the more assets will be tracked in real time. Less resource usage could also mean the computational cost is inexpensive. The study finds that the choreography strategy in MSA is better for IPS since the number of served requests is five times higher with similar resource usage.

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“…The use of advanced sensors and Machine Learning algorithms [ 41 , 87 , 88 ] provides a wealth of accurate information regarding life inside the beehive [ 102 ]. Sensors’ data fusion enables timely hive intervention, reducing response time to issues like stress, diseases, and pests and enhancing beekeeping efficiency [ 18 , 103 ]. For example, this means preventing, with a combination of hive temperature or sound data, a possible swarming or being alerted in time, using collected scale data, that swarming has occurred [ 35 , 41 , 51 , 104 , 105 ].…”

Section: Swot Analysis On the Application Of Pb Systemsmentioning

confidence: 99%

Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector

Danieli,

Addeo,

Lazzari

et al. 2023

Animals

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The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware–software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical–chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees’ colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies’ health) that the beekeeper will appraise as a consequence of their use.

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BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Cited by 6 publications

References 77 publications

Enabling Robust Sensor Network Design with Data Processing and Optimization Making use of Local Beehive Image and Video Files

Enabling Robust Sensor Network Design with Data Processing and Optimization Making use of Local Beehive Image and Video Files

Microservices for Asset Tracking Based on Indoor Positioning System

Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector

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