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Google Earth engine based approach for finding fire locations and burned areas in Muğla, Turkey
(Science Publishing Group, 2021-10-05) Çavdaroğlu, Gülsüm Çiğdem
Forests are considered as one of the main sources of biodiversity. Forest fires caused by various reasons pose a high risk in terms of biodiversity. Therefore, mapping of fire zones is of great importance in determining the damage caused by the fire, managing the fire process, and planning the interventions in the fire zone. Although remote sensing is a fast and cost-effective methodology for mapping fire zones, the implementation of the remote sensing methodologies is problematic in some respects. The web-based Google Earth Engine makes possible to access the satellite imagery and process the imagery easily. The research area of this study is Muğla, Turkey in where many forest fires broke out in 2021 summer. This study provides an implementation of normalized burn ratio which is widely used to highlight burned areas on Google Earth Engine platform. Both vector data and satellite images were used in the study. The vector data is in the shape file format and was uploaded to the Google Earth Engine platform as a table. The Sentinel-2 imagery was used to calculate normalized burn ratio. The satellite imagery was clipped using the table data. The difference pre-fire and post-fire images was calculated, and the classes were assigned to the pixels according to the normalized burn ratio ranges. The study indicates that finding the burned areas and constructing the burn severity levels can be realized in 1.32 minutes on Google Earth Engine platform.
A character segmentation method to increase character recognition accuracy for Turkish license plates
(Science Publishing Group, 2021-12-31) Çavdaroğlu, Gülsüm Çiğdem; Gökmen, Mehmet
Automatic License Plate Recognition is a computer vision technology that provides a way to recognize the vehicle's license plates without direct human intervention. Developing Automatic License Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Automatic License Plate Recognition systems include image acquisition and character segmentation phases. Although there are many studies, the research in character segmentation and improving recognition accuracy remains limited. The lack of an international standard for license plates and the misinterpretation of ambiguous characters are challenging problems for Automatic License Plate Recognition systems. Several academic works have shown that the ambiguous character problem can be overcome by using a second model that contains only these characters. In this study, we propose a new methodology to reduce the character recognition errors of Automatic License Plate Recognition systems. One of the reasons for the low accuracy rates is the problem of ambiguous characters. In most studies using OCR, it was observed that a single model was used for alphanumeric characters during the recognition phase. Instead of using a single model, using separate models for letters and digits will improve the recognition process and increase accuracy. Therefore, we determined whether the characters are letters or numbers, and we expressed the license plates in the form of letters - digits. The method suggested for segmenting blobs worked with an accuracy of 96.12% on the test dataset. The method recommended for generating letter-digit expressions for the license plates worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish license plates. In future studies, we will expand our method by using the license plate dataset of a different country.
Correlation analysis between the community mobility and nighttime lights in the city of Istanbul, Turkey
(2022) Çavdaroğlu, Gülsüm Çiğdem
The COVID-19, which emerged in Wuhan, China, in 2019, has significantly affected people’s Daily lives, business environment, surrounding environment, and countries' economic and social conditions. This study aims to measure the correlation between community mobility changes in six different areas and nighttime lights in the city of Istanbul, Turkey. Nighttime light data used in the study was obtained from VIIRS Nighttime Day/Night Band Composites Version 1 using remote sensing methods via Google Earth Engine platform. Then the correlation between Nighttime light values and community mobility values was investigated. It has been observed that the correlation values have changed dramatically over the years. The most significant correlation values were observed for the year 2020. This is because 2020 is the year when the pandemic is most effective, and restrictions are at the highest level in Turkey. The increase in freedom in the following years caused a decrease in the correlation. When the correlation results covering the period of February 2020 - to January 2022 were examined, it was observed that there was no significant relationship between nighttime light values and Google Community Mobility Reports’ variables. Considering the correlation results for 2020, it was observed that there was a high negative correlation between nighttime light data and mobility trends for grocery and pharmacy, and mobility trends for places of work. In addition, there was a moderate negative correlation between nighttime light data and mobility trends for retail and recreation, and a moderate positive correlation between nighttime light and mobility trends for places of residence. When the correlation values of 2021 and the correlation values of the period 2021-2022 were examined, no significant relationship was observed.
Istanbul’s community mobility changes during the COVID-19 pandemic: a spatial analysis
(Istanbul University Press, 2023-08-15) Arık, Ahmet Okan; Çavdaroğlu, Gülsüm Çiğdem
COVID-19 was the most recent pandemic to strike humanity. Moreover, this pandemic occurred during the most active period of global interaction and mobility, unlike pandemics like cholera, plague, and flu in earlier centuries. Many countries restricted domestic mobility after suspending international mobility to prevent the pandemic from spreading. Although these policies differ from nation to nation, they have affected the mobility of communities. This study examined spatial and non-spatial independent variables that affected how the community’s mobility patterns changed in various locations, including parks, transit stations, workplaces, grocery and pharmacies, and residential areas in Istanbul, Türkiye. The impact of the independent spatial variables on the mobility changes was examined after identifying the non-spatial independent variables influencing the mobility changes in 6 different areas. It was determined that the altitude variable, expected to impact how mobility changed, had no overall impact on the dependent variable. On the other hand, the dependent variables representing the mobility changes were affected by the independent variables representing the county center’s latitude and longitude values and whether the county is located near the sea. Regression analysis across Türkiye will be performed in upcoming studies using an updated version of the methodology used in this study.
An ontology for apiculture practices (Onto4API): towards semantic interoperability and knowledge sharing in the apiculture community
(Ege Tarımsal Araştırma Enstitüsü Müdürlüğü, 2025-12-31) Aydın, Şahin; Okuyan, Samet; Solmaz, Serhat
This study presents the development of Onto4API, a domain ontology designed to support semantic interoperability and structured knowledge sharing in the field of apiculture. The ontology addresses the lack of standardized, machine-interpretable vocabularies that hinder knowledge integration and decision support in traditional beekeeping practices. Developed under the guidance of subject-matter experts from the Türkiye Apiculture Research Institute, Onto4API formalizes key concepts, relationships, and production practices in modern beekeeping. The ontology was built using OWL 2 and RDF/XML syntax, and includes 67 classes, six object properties, and 10 data properties. Following the METHONTOLOGY framework, our approach ensures methodological rigor from specification to implementation and evaluation, combining expert validation, reasoning-based consistency checks, and SPARQL-based functional testing. To demonstrate its practical utility, a web-based educational tool was implemented using ASP.NET MVC and dotNetRDF. This prototype enables users to explore apiculture knowledge through SPARQL-based queries in a guided question-and-answer format. By providing a reusable and extensible semantic framework, Onto4API lays the groundwork for future ontology-driven agricultural systems, including intelligent decision support, educational tools, and interoperable data services in apiculture and beyond.
An analysis on environmental justice and air quality using machine learning techniques
(Murat Gök, 2025-12-24) Demircan, Görkem; Çavdaroğlu Akkoç, Gülsüm Çiğdem
This study examines air quality dynamics across countries using machine learning with a focus on environmental justice. Random Forest, Decision Tree, XGBoost, and Adaboost algorithms were applied for a 10-year air pollution forecast. XGBoost showed the best performance. Increases in pollutant levels are expected in Bhutan and North Korea, while improvements may occur in India, Pakistan, and Nepal. Significant air quality changes are projected in Laos, Indonesia, and North Korea. The study highlights inequalities in pollution exposure and emphasizes the need for targeted interventions.
Designing a scalable agricultural information system for pest detection and decision support in hazelnut cultivation
(World Scientific Publishing Company, 2025-11-12) Aydın, Şahin
This study presents a microservices-based, multi-tiered information system to detect, monitör and manage pest species that cause yield losses in hazelnut production. The system integrates a deep learning model for classifying pest images submitted by field users, the generation of pest density maps and location-based early warning mechanisms for growers. Delivered through mobile, web and desktop platforms, the system enables data sharing among farmers, researchers and decision-makers, supporting agricultural decisions. Experimental findings show that the DNN+ResNet50 architecture achieved the highest accuracy (91.88%) among all tested CNN models. Performance evaluations indicated that the Authentication and Heatmap services sustained high stability under loads of up to 1000 requests, while the Bug Classification Service was reliable up to 750 requests before reaching a critical resource threshold. The usability test resulted in an overall score of 38 out of 50, with sub-scores of Appropriateness Recognizability (0.73, Acceptable), Learnability (0.71, Acceptable), Operability (0.65, Questionable), User Error Protection (0.86, Good), User Interface Aesthetics (0.83, Good) and Accessibility (0.74, Acceptable). With its robust technical architecture and practical implementation, the proposed system can generate economic, social and commercial outcomes. This study provides a software engineering-oriented approach to the digitalization of agricultural production and the sustainable management of pests.
Future circular collider feasibility study report: volume 3 civil engineering, implementation and sustainability
(Springer Science and Business Media Deutschland GmbH, 2025-10-13) Benedikt, Michael; Zimmermann, Frank; Auchmann, Bernhard; Bayındır, Cihan; Özaydın, Fatih
Volume 3 of the FCC Feasibility Report presents studies related to civil engineering, the development of a project implementation scenario, and environmental and sustainability aspects. The report details the iterative improvements made to the civil engineering concepts since 2018, taking into account subsurface conditions, accelerator and experiment requirements, and territorial considerations. It outlines a technically feasible and economically viable civil engineering configuration that serves as the baseline for detailed subsurface investigations, construction design, cost estimation, and project implementation planning. Additionally, the report highlights ongoing subsurface investigations in key areas to support the development of an improved 3D subsurface model of the region. The report describes the development of the project scenario based on the ‘avoid-reduce-compensate’ iterative optimisation approach. The reference scenario balances optimal physics performance with territorial compatibility, implementation risks, and costs. Environmental field investigations covering almost 600 hectares of terrain—including numerous urban, economic, social, and technical aspects—confirmed the project’s technical feasibility and contributed to the preparation of essential input documents for the formal project authorisation phase. The summary also highlights the initiation of public dialogue as part of the authorisation process. The results of a comprehensive socio-economic impact assessment, which included significant environmental effects, are presented. Even under the most conservative and stringent conditions, a positive benefit-cost ratio for the FCC-ee is obtained. Finally, the report provides a summary of the studies conducted to document the current state of the environment.
Quantum Zeno repeaters
(Cornell Univ, 2022-06-17) Bayrakçı, Veysel; Özaydın, Fatih
Quantum repeaters pave the way for long-distance quantum communications and quantum Internet, and the idea of quantum repeaters is based on entanglement swapping which requires the implementation of controlled quantum gates. Frequently measuring a quantum system affects its dynamics which is known as the quantum Zeno effect (QZE). Beyond slowing down its evolution, QZE can be used to control the dynamics of a quantum system by introducing a carefully designed set of operations between measurements. Here, we propose an entanglement swapping protocol based on QZE, which achieves almost unit fidelity. Implementation of our protocol requires only simple frequent threshold measurements and single particle rotations. We extend the proposed entanglement swapping protocol to a series of repeater stations for constructing quantum Zeno repeaters which also achieve almost unit fidelity regardless of the number of repeaters. Requiring no controlled gates, our proposal reduces the quantum circuit complexity of quantum repeaters. Our work has potential to contribute to long distance quantum communications and quantum computing via quantum Zeno effect.
Critical digital data enabling traceability for smart honey value chains
(Taylor and Francis Ltd., 2025-02) Ziemba, Ewa Wanda; Maruszewska, Ewa Wanda; Karmanska, Anna; Aydın, Mehmet Nafiz; Aydın, Şahin
Data analysis and sharing are becoming increasingly important in creating value within food supply chains, including honey value chains. While some data is readily shared between supply chain actors, unlocking further benefits requires additional investments in digital data capturing, particularly for value-based claims such as sustainability, equity, and traceability from hives to customers. This study aims to identify critical digital data necessary for smart honey value chains to ensure traceability and transparency while fostering trust among beekeepers, intermediaries, and consumers. Semi-structured interviews with 30 beekeeping experts were conducted to explore their perspectives. The analysis identified four critical categories of data—beekeeper data, apiary data, honey data, and apiary practices data—encompassing 21 specific data points essential for ensuring transparency, traceability, and trust. These findings provide novel insights into the digital data requirements necessary to support the honey industry’s evolving needs for transparent and traceable value chains.
An intrusion detection approach based on the combination of oversampling and undersampling algorithms
(Istanbul University Press, 2023-06-14) Arık, Ahmet Okan; Çavdaroğlu, Gülsüm Çiğdem
The threat of network intrusion has become much more severe due to the increasing network flow. Therefore, network intrusion detection is one of the most concerned areas of network security. As demand for cybersecurity assurance increases, the requirement for intrusion detection systems to meet current threats is also growing. However, network-based intrusion detection systems have several shortcomings due to the structure of the systems, the nature of the network data, and uncertainty related to future data. The imbalanced class problem is also crucial since it significantly negatively affects classification performance. Although high performance has been achieved in deep learning-based methodologies in recent years, machine learning techniques may also provide high performance in network intrusion detection. This study suggests a new intrusion detection system called ROGONG-IDS (Robust Gradient Boosting – Intrusion Detection System) which has a unique two-stage resampling model to solve the imbalanced class problem that produces high accuracy on the UNSW-NB15 dataset using machine learning techniques. ROGONGIDS is based on gradient boosting. The system uses Synthetic Minority Over-Sampling Technique (SMOTE) and NearMiss-1 methods to handle the imbalanced class problem. The proposed model's performance on multi-class classification was tested with the UNSW-NB15, and then its robust structure was validated with the NSL-KDD dataset. ROGONG-IDS reached the highest attack detection rate and F1 score in the literature, with a 97.30% detection rate and 97.65% F1 score using the UNSW-NB15 dataset. ROGONG-IDS provides a robust, efficient intrusion detection system for the UNSW-NB15 dataset, which suffered from imbalanced class distribution. The proposed methodology outperforms state-of-the-art and intrusion detection methods.
Finans sektöründe dijital dönüşüm uygulamaları ve dolandırıcılık tespiti
(Işık Üniversitesi Yayınları, 2024-10-31) Aktay, Habibe; Soykut Sarıca, Yeşim Pınar
Teknolojik inovasyonlarda yaşanan gelişmeler ışığında rekabet şartları küresel düzeyde değişmiştir. Bu değişim ve dönüşümlerden finans sektörü de nasibini almıştır. Artan rekabet ile şirketlerin sürdürülebilir rekabet avantajı elde etmeleri, müşteri memnuniyeti sağlamaları ve pazar paylarını arttırmalarının yolu tüm iş süreçlerini dijitalleştirmelerinden geçmektedir. Dolayısıyla örgütlerin manuel olarak yürüttükleri tüm iş sistemlerini bilişim sistem ve teknolojilerine entegre etmeleri gerekmektedir. Bu sayede örgütsel hedeflere ulaşma noktasında daha etkili ve verimli bir yol tercih edilmiş olacaktır. Tüm bunların sistem güvenliğinin sağlanarak yapılması gerekmektedir. Bu çalışmanın amacı mevcut literatür incelenerek finans sektöründe dijitalleşme olgusuna değinme ve dolandırıcılık tespitinde kullanılacak strateji ve algoritmalar hakkında uygulayıcılara ve teorisyenlere faydalı olacak kurumsal alt yapı ve içgörü sağlamaktır. Araştırma kapsamında finansal hizmetlerde dijital dönüşümü sağlayan kavramların temel unsurları ve bileşenleri verilmiş olup, dolandırıcılık tespitinde kullanılan algoritmalar incelenmiştir. Bu çalışma sonucunda dijitalleşmenin finansal ve örgütsel süreçleri büyük oranda iyileştirdiği fakat dijital sistemlerin siber saldırıları da olanaklı kıldığı sonucuna ulaşılmıştır. Dolandırıcılık tespitinde veri madenciliği ve makine öğrenmesi algoritmalarının faydalı olduğu sonucuna ulaşılmıştır. Makine öğrenmesi algoritmaları arasında Sinir Ağları(Neural Networks), Rastgele Orman(Random Forest), Destek Vektör Makineleri(Support Vector Machines (SVM)) ve Gradyan Güçlendirme Ağaçları (Gradient Boosting Trees) algoritmalarının her biri doğruluk ve performans kriterleri açısından değerlendirildiklerinde yüksek performans gösterdikleri sonucuna ulaşılmıştır. Dolandırıcılık tespitinde algoritmalar ve farkındalık stratejilerinin uygulanmasının kurumlar lehine olduğu sonucu çıkarılmıştır.
Microservices-based databank for Turkish hazelnut cultivars using IoT and semantic web technologies
(John Wiley and Sons Ltd, 2024-03-30) Aydın, Şahin; Aldara, Dieaa
Information and communication technologies (ICTs) can play a crucial role in facilitating access to comprehensive information on the quality standards of Turkish hazelnut cultivars. In this regard, this study introduces a Hazelnut Databank System (HDS) that utilizes the microservices architecture, an integrated software system supported by the Internet of Things (IoT) and semantic web, to categorize Turkish hazelnut cultivars. The study focuses on developing microservices using various programming languages and frameworks. Specifically, C# on the.NET Core Framework was used for both microservices and the web-based application implemented through the ASP.NET Core MVC Framework. Mobile-based software applications were created using Xamarin. Forms, and the IoT application was developed using the Python programming language. The data storage is facilitated through the MS SQL Server database. Additionally, the study incorporates the implementation of a hazelnut species classification system using the DNN + ResNet50 machine learning model, achieving an impressive accuracy rate of 95.77%. The overall usability of the system was evaluated, resulting in a score of 42 out of 50. By providing detailed information on Turkish hazelnut cultivars, the HDS has the potential to greatly improve hazelnut production quality in Turkey and increase awareness of hazelnut agriculture among relevant stakeholders.
Powering quantum Otto engines only with q -deformation of the working substance
(American Physical Society, 2023-11) Özaydın, Fatih; Müstecaplıoğlu, Özgür E.; Hakioğlu, Tuğrul
We consider a quantum Otto cycle with a q-deformed quantum oscillator working substance and classical thermal baths. We investigate the influence of the quantum statistical deformation parameter q on the work and efficiency of the cycle. In usual quantum Otto cycle, a Hamiltonian parameter is varied during the quantum adiabatic stages while the quantum statistical character of the working substance remains fixed. We point out that even if the Hamiltonian parameters are not changing, work can be harvested by quantum statistical changes of the working substance. Work extraction from thermal resources using quantum statistical mutations of the working substance makes a quantum Otto cycle without any classical analog.
Spatial-Temporary analysis of Istanbul air pollution during the pandemic using Google Earth Engine and Google community mobility reports
(Gök, Murat, 2023-06-30) Çavdaroğlu, Gülsüm Çiğdem; Arık, Ahmet Okan
The Covid-19 pandemic has brought drastic changes to people's daily life and environmental characteristics. To control the pandemic, all governments have implemented particular policies for their countries and imposed restrictions that affect people's daily life. The traffic index has decreased in many countries and cities depending on the restrictions. Therefore, restrictions in many countries and cities have positively impacted air quality. However, the opposite has also been observed in metropolitan cities. In this study, the change in the air quality of Istanbul, which is accepted as Turkey's largest metropolitan city, has been examined. First, the spatio-temporal distribution of air pollutants (NO2, CO, and SO2) has been analyzed using Sentinel-5P NRTI satellite images. Then six independent variable groups (traffic index of Istanbul, daily deaths in Istanbul, Google community mobility reports of Istanbul, fuel prices, stringency index of Turkey, two logical attributes regarding the Covid-19 restrictions and in-class education) were collected and combined to analyze the correlations between these variable groups and air pollutant concentrations. According to the spatial distribution graphs, there is a tendency to decrease NO2, CO, and SO2 pollutant concentrations in Istanbul when the restrictions are applied in Turkey. There was no significant relationship between the decrease in community mobility in Istanbul and pollutant concentrations, although an increase in air quality has been observed in many cities due to the restrictions of the Covid-19 pandemic.
Quantum Zeno repeaters
(Nature Research, 2022-09-12) Bayrakçı, Veysel; Özaydın, Fatih
Quantum repeaters pave the way for long-distance quantum communications and quantum Internet, and the idea of quantum repeaters is based on entanglement swapping which requires the implementation of controlled quantum gates. Frequently measuring a quantum system affects its dynamics which is known as the quantum Zeno effect (QZE). Beyond slowing down its evolution, QZE can be used to control the dynamics of a quantum system by introducing a carefully designed set of operations between measurements. Here, we propose an entanglement swapping protocol based on QZE, which achieves almost unit fidelity. Implementation of our protocol requires only simple frequent threshold measurements and single particle rotations. We extend the proposed entanglement swapping protocol to a series of repeater stations for constructing quantum Zeno repeaters which also achieve almost unit fidelity regardless of the number of repeaters. Requiring no controlled gates, our proposal reduces the quantum circuit complexity of quantum repeaters. Our work has potential to contribute to long distance quantum communications and quantum computing via quantum Zeno effect.
Edge detection of aerial images using artificial bee colony algorithm
(Kırgızistan Türkiye Manas Üniversitesi, 2022-06-30) Yelmenoğlu, Elif Deniz; Akhan Baykan, Nurdan
Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.
Yakın alan iletişimi teknolojisi
(Türkiye Bilişim Vakfı, 2016-06-24) Özdenizci, Büşra; Ok, Kerem; Aydın, Mehmet Nafiz; Coşkun, Vedat
Yakın Alan İletişimi iki elektronik cihazın kısa mesafede, yüksek frekansta ve düşük bant genişliğinde haberleşmesini sağlayan Radyo Frekansı ile Tanımlama tabanlı bir teknolojidir. NFC uygulamaları temel olarak üç ayrı çalışma kipi kullanırlar ve her bir kipin kullanımının getirmiş olduğu ayrı özellikler ve avantajlar mevcuttur. Her çalışma kipi farklı iş fırsatları ve katma değer olanakları sunabilmektedir. Bu çalışmada, öncelikle dünyada şu an itibari ile mevcut tüm NFC uygulamaları ve prototipleri, kullanmış oldukları çalışma kiplerine göre incelenmiştir. Daha sonra bu çalışmaların her biri ayrıntılı biçimde analiz edilmiş ve oluşturdukları katma değer ortaya konulmuştur. Çalışma, bu alandaki hedef beklentilerimizin konulması ile neticelendirilmiştir.
Saliency detection based on hybrid artificial bee colony and firefly optimization
(Springer Science and Business Media Deutschland GmbH, 2022-11) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, Tuğrul
Saliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods.
Design and implementation of a smart beehive and its monitoring system using microservices in the context of IoT and open data
(Elsevier B.V., 2022-05) Aydın, Şahin; Aydın, Mehmet Nafiz
It is essential to keep honey bees healthy for providing a sustainable ecological balance. One way of keeping honey bees healthy is to be able to monitor and control the general conditions in a beehive and also outside of a beehive. Monitoring systems offer an effective way of accessing, visualizing, sharing, and managing data that is gathered from performed agricultural and livestock activities for domain stakeholders. Such systems have recently been implemented based on wireless sensor networks (WSN) and IoT to monitor the activities of honey bees in beehives as well. Scholars have shown considerable interests in proposing IoT- and WSN-based beehive monitoring systems, but much of the research up to now lacks in proposing appropriate architecture for open data driven beehive monitoring systems. Developing a robust monitoring system based on a contemporary software architecture such as microservices can be of great help to be able to control the activities of honey bees and more importantly to be able to keep them healthy in beehives. This research sets out to design and implementation of a sustainable WSN-based beehive monitoring platform using a microservice architecture. We pointed out that by adopting microservices one can deal with long-standing problems with heterogeneity, interoperability, scalability, agility, reliability, maintainability issues, and in turn achieve sustainable WSN-based beehive monitoring systems.

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