PhD Candidate
Apostolos Panagiotopoulos is a PhD candidate at the laboratory. His publications address the digital transformation of humanitarian supply chains through the AidTech platform and IoT safety applications built from commercial off-the-shelf components.
Publications
Journal Articles
Panagiotopoulos, A., Karyotis, V., Dimitrakopoulos, G. N., Choleva-Tsiringkaki, V., & Kourouthanassis, P. E. (2026). Demystifying the Digital Transformation of Humanitarian Supply Chains Through AidTech. Logistics, 10(5), 110. https://doi.org/10.3390/logistics10050110
@article{panagiotopoulos2026demystifying,
title = {Demystifying the Digital Transformation of Humanitarian Supply Chains Through AidTech},
author = {Apostolos Panagiotopoulos and Vasileios Karyotis and Georgios N. Dimitrakopoulos and Vassiliki Choleva-Tsiringkaki and Panos E. Kourouthanassis},
url = {https://doi.org/10.3390/logistics10050110},
doi = {10.3390/logistics10050110},
year = {2026},
date = {2026-01-01},
journal = {Logistics},
volume = {10},
number = {5},
pages = {110},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping the next generation of humanitarian aid management systems, focusing on algorithmic and operational aspects, matching them to a new architecture for a target-platform, called AidTech. Methods: The methodology follows a systematic literature coverage, identifying cross-disciplinary trends from operations research, computer science and sustainable logistics. We examine the convergence of optimization, artificial intelligence, and blockchain-enabled traceability toward efficient and transparent humanitarian logistics. The employed methods include adaptive scheduling, resource allocation algorithms and privacy–preserving collaboration for meeting special constraints imposed by the humanitarian scope. Results: The findings focus on the architectural perspective of humanitarian supply chains and highlight that modern humanitarian infrastructures will increasingly rely on hybrid optimization methods integrating graph theory, dynamic routing under stochastic demand, multi-criteria decision analysis and distributed ledger technologies. Conclusions: We conclude that these paradigms, when combined under a unified cyber–physical architecture, e.g., AidTech, can substantially improve responsiveness, equity and sustainability in crisis management, further shaping future humanitarian logistics.},
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pubstate = {published},
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Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping the next generation of humanitarian aid management systems, focusing on algorithmic and operational aspects, matching them to a new architecture for a target-platform, called AidTech. Methods: The methodology follows a systematic literature coverage, identifying cross-disciplinary trends from operations research, computer science and sustainable logistics. We examine the convergence of optimization, artificial intelligence, and blockchain-enabled traceability toward efficient and transparent humanitarian logistics. The employed methods include adaptive scheduling, resource allocation algorithms and privacy–preserving collaboration for meeting special constraints imposed by the humanitarian scope. Results: The findings focus on the architectural perspective of humanitarian supply chains and highlight that modern humanitarian infrastructures will increasingly rely on hybrid optimization methods integrating graph theory, dynamic routing under stochastic demand, multi-criteria decision analysis and distributed ledger technologies. Conclusions: We conclude that these paradigms, when combined under a unified cyber–physical architecture, e.g., AidTech, can substantially improve responsiveness, equity and sustainability in crisis management, further shaping future humanitarian logistics.
Panagiotopoulos, A., & Karyotis, V. (2025). Commercial Off-the-Shelf IoT-Based Infant Car Seat Application for Preventing the Forgotten Baby Syndrome. Future Internet, 17(10), 443. https://doi.org/10.3390/fi17100443
@article{panagiotopoulos2025commercial,
title = {Commercial Off-the-Shelf IoT-Based Infant Car Seat Application for Preventing the Forgotten Baby Syndrome},
author = {Apostolos Panagiotopoulos and Vasileios Karyotis},
url = {https://doi.org/10.3390/fi17100443},
doi = {10.3390/fi17100443},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {10},
pages = {443},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {The Forgotten Baby Syndrome (FBS), the accidental abandonment of infants in vehicles, continues to result in otherwise preventable tragedies worldwide. This work presents a prototype system called SafeCuddle, designed to mitigate the risks associated with FBS. The proposed solution utilizes an Arduino UNO R4 WiFi microcontroller integrated with low-cost IoT sensors for real-time data acquisition and processing. Processed signals are visualized via a Python-based desktop application. A key feature of the system is its ability to issue immediate alerts to the driver upon detecting their departure from the vehicle while an infant remains seated in the vehicle. An extensive review of the syndrome’s etiology identifies disrupted routines and the high demands of modern life as primary contributing factors. In response, the proposed system can be easily implemented with commercial off-the-shelf components and aims to support caregivers by acting as a fail-safe mechanism. The paper is structured into two primary sections: (i) an analytical overview of FBS and its contributing factors and (ii) a detailed account of the system’s design, implementation, operational workflow, and evaluation results. The unique contribution of this work lies in the integration of a low-cost, real-time alert system within a modular and easily deployable architecture that can be integrated in existing infant car seats as an aftermarket solution, if properly commercialized, specifically tailored to prevent FBS through immediate driver feedback at the critical moment of risk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Forgotten Baby Syndrome (FBS), the accidental abandonment of infants in vehicles, continues to result in otherwise preventable tragedies worldwide. This work presents a prototype system called SafeCuddle, designed to mitigate the risks associated with FBS. The proposed solution utilizes an Arduino UNO R4 WiFi microcontroller integrated with low-cost IoT sensors for real-time data acquisition and processing. Processed signals are visualized via a Python-based desktop application. A key feature of the system is its ability to issue immediate alerts to the driver upon detecting their departure from the vehicle while an infant remains seated in the vehicle. An extensive review of the syndrome’s etiology identifies disrupted routines and the high demands of modern life as primary contributing factors. In response, the proposed system can be easily implemented with commercial off-the-shelf components and aims to support caregivers by acting as a fail-safe mechanism. The paper is structured into two primary sections: (i) an analytical overview of FBS and its contributing factors and (ii) a detailed account of the system’s design, implementation, operational workflow, and evaluation results. The unique contribution of this work lies in the integration of a low-cost, real-time alert system within a modular and easily deployable architecture that can be integrated in existing infant car seats as an aftermarket solution, if properly commercialized, specifically tailored to prevent FBS through immediate driver feedback at the critical moment of risk.
Proceedings Articles
Dimitrakopoulos, G. N., Vrahatis, A. G., Panagiotopoulos, A., Korfiatis, N., Karyotis, V., & Kourouthanassis, P. E. (2025). Decision Support and Predictive Intelligence in Humanitarian Logistics: An Updated Overview. In 2025 IEEE International Conference on Big Data (BigData). Macau, China: IEEE.
@inproceedings{dimitrakopoulos2025decision,
title = {Decision Support and Predictive Intelligence in Humanitarian Logistics: An Updated Overview},
author = {Georgios N. Dimitrakopoulos and Aristidis G. Vrahatis and A. Panagiotopoulos and Nikolaos Korfiatis and Vasileios Karyotis and Panos E. Kourouthanassis},
year = {2025},
date = {2025-01-01},
booktitle = {2025 IEEE International Conference on Big Data (BigData)},
publisher = {IEEE},
address = {Macau, China},
note = {IEEE BigData 2025, December 8-11, 2025, Macau, China},
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pubstate = {published},
tppubtype = {inproceedings}
}