Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts
Abstract Street vendors in developing regions often lack access to portable and affordable cold storage, leading to accelerated food spoilage, financial losses, and health risks. Traditional refrigeration solutions are bulky and costly, while manual freshness assessment is error-prone. This study pr...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01063-3 |
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| author | Thompson Stephan Padma Priya Dharishini Paramana Chia-Chen Lin Saurabh Agarwal Rajan Verma |
| author_facet | Thompson Stephan Padma Priya Dharishini Paramana Chia-Chen Lin Saurabh Agarwal Rajan Verma |
| author_sort | Thompson Stephan |
| collection | DOAJ |
| description | Abstract Street vendors in developing regions often lack access to portable and affordable cold storage, leading to accelerated food spoilage, financial losses, and health risks. Traditional refrigeration solutions are bulky and costly, while manual freshness assessment is error-prone. This study proposes a smart vending cart integrating IoT sensors and federated learning (FL) to address these challenges, offering real-time environmental monitoring, freshness classification, and privacy-preserving data handling. The smart vending cart incorporates IoT sensors to monitor temperature, humidity, and gas emissions. A Peltier cooling module and a humidifier maintain optimal conditions. Machine learning models classify food freshness, while federated learning ensures vendor privacy by training models locally on each cart. The study explores nine federated learning approaches to train machine learning models across multiple carts without sharing raw data, thus preserving vendor privacy. The Stacking Ensemble approach outperformed all other methods, achieving the highest accuracy, F1-Score, and Cohen’s Kappa (0.99964), with the lowest log loss (0.0022). MetaLearning and Weighted Aggregation also demonstrated high performance but with marginally higher log loss values. Personalized models performed well in heterogeneous data environments but were less effective than ensemble methods. The developed smart vending cart system effectively reduces food spoilage and enhances vendor profitability through automated freshness classification and real-time environmental control. The integration of federated learning ensures privacy, while ensemble techniques improve robustness in resource-constrained settings, offering a scalable solution for street vendors. |
| format | Article |
| id | doaj-art-7eec918ae6e643d4a641852df33a7a1c |
| institution | DOAJ |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-7eec918ae6e643d4a641852df33a7a1c2025-08-20T02:56:11ZengSpringerOpenJournal of Big Data2196-11152025-02-0112113410.1186/s40537-025-01063-3Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending cartsThompson Stephan0Padma Priya Dharishini Paramana1Chia-Chen Lin2Saurabh Agarwal3Rajan Verma4Thumbay College of Management and AI in Healthcare, Gulf Medical UniversityDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied SciencesDepartment of Computer Science and Information Engineering, National Chin-Yi University of TechnologyDepartment of Information and Communication Engineering, Yeungnam UniversityUniversity Centre for Research and Development, Chandigarh UniversityAbstract Street vendors in developing regions often lack access to portable and affordable cold storage, leading to accelerated food spoilage, financial losses, and health risks. Traditional refrigeration solutions are bulky and costly, while manual freshness assessment is error-prone. This study proposes a smart vending cart integrating IoT sensors and federated learning (FL) to address these challenges, offering real-time environmental monitoring, freshness classification, and privacy-preserving data handling. The smart vending cart incorporates IoT sensors to monitor temperature, humidity, and gas emissions. A Peltier cooling module and a humidifier maintain optimal conditions. Machine learning models classify food freshness, while federated learning ensures vendor privacy by training models locally on each cart. The study explores nine federated learning approaches to train machine learning models across multiple carts without sharing raw data, thus preserving vendor privacy. The Stacking Ensemble approach outperformed all other methods, achieving the highest accuracy, F1-Score, and Cohen’s Kappa (0.99964), with the lowest log loss (0.0022). MetaLearning and Weighted Aggregation also demonstrated high performance but with marginally higher log loss values. Personalized models performed well in heterogeneous data environments but were less effective than ensemble methods. The developed smart vending cart system effectively reduces food spoilage and enhances vendor profitability through automated freshness classification and real-time environmental control. The integration of federated learning ensures privacy, while ensemble techniques improve robustness in resource-constrained settings, offering a scalable solution for street vendors.https://doi.org/10.1186/s40537-025-01063-3Smart vending cartFederated learningIoT-based freshness detectionData privacyMachine learning classificationCold storage solutions |
| spellingShingle | Thompson Stephan Padma Priya Dharishini Paramana Chia-Chen Lin Saurabh Agarwal Rajan Verma Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts Journal of Big Data Smart vending cart Federated learning IoT-based freshness detection Data privacy Machine learning classification Cold storage solutions |
| title | Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts |
| title_full | Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts |
| title_fullStr | Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts |
| title_full_unstemmed | Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts |
| title_short | Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts |
| title_sort | federated learning driven iot system for automated freshness monitoring in resource constrained vending carts |
| topic | Smart vending cart Federated learning IoT-based freshness detection Data privacy Machine learning classification Cold storage solutions |
| url | https://doi.org/10.1186/s40537-025-01063-3 |
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