
Charalampos Marantos received the Diploma from the Department of Electrical and Computer Engineering, National Technical University of Athens, Greece, in 2016, where he is currently working toward the Ph.D. degree. His research interests include embedded systems programming, decision-making mechanisms targeting IoT and Cyber-Physical systems applications, machine learning, energy/performance and maintainability optimization for embedded systems applications. He is currently working on EU research projects and more precisely in SDK4ED, which is about designing a Software Development Toolkit for Energy Optimization and Technical Debt Elimination, targeting Embedded Systems applications, as well as in FABSPACE 2.0, which creates an open-innovation network for geodata-driven information.
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Towards Plug&Play Smart Thermostats for Building’s Heating/Cooling Control
In: Siozios K., Anagnostos D., Soudris D., Kosmatopoulos E. (eds) IoT for Smart Grids. Power Systems. Springer, Cham  BibTeX@Inbook{Marantos2019, author="Marantos, Charalampos and Lamprakos, Christos and Siozios, Kostas and Soudris, Dimitrios", editor="Siozios, Kostas and Anagnostos, Dimitrios and Soudris, Dimitrios and Kosmatopoulos, Elias", title="Towards Plug{\&}Play Smart Thermostats for Building's Heating/Cooling Control", bookTitle="IoT for Smart Grids: Design Challenges and Paradigms", year="2019", publisher="Springer International Publishing", address="Cham", pages="183--207", abstract="Buildings are immensely energy-demanding and this fact is enhanced by the expectation of even more increment of energy consumption in the near future, while the building's cooling and heating has a significant impact on the overall energy consumption (around 40{\%}). Therefore it is necessary to find proper ways for mitigating the increasing energy cost of HVAC systems (Heating Ventilation and Air Conditioning). The problem of increased energy requirements becomes far more crucial by taking into consideration the sub-optimal operation of HVAC systems by the occupants. In order to alleviate these drawbacks, throughout this chapter we introduce a decision-making mechanism in order to support the temperature control within buildings. For this purpose, a smart thermostat concept is applied, where emphasis is given to lowering the cost and deployment flexibility, in order to be widely adopted in different buildings and regions. The proposed mechanism incorporates supervised learning and reinforcement learning techniques in order to solve a multi-objective problem that comprises both satisfying occupant's thermal comfort and minimize energy consumption.", isbn="978-3-030-03640-9", doi="10.1007/978-3-030-03640-9_10", url="https://doi.org/10.1007/978-3-030-03640-9_10" }
Book Chapters
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Rapid Prototyping of Low-Complexity Orchestrator Targeting CyberPhysical Systems: The Smart-Thermostat Usecase
IEEE Transactions on Control Systems Technology  BibTeX@article{marantos2019rapid, title={Rapid Prototyping of Low-Complexity Orchestrator Targeting CyberPhysical Systems: The Smart-Thermostat Usecase}, author={Marantos, Charalampos and Siozios, Kostas and Soudris, Dimitrios}, journal={IEEE Transactions on Control Systems Technology}, year={2019}, publisher={IEEE} } -
A Flexible Decision-Making Mechanism Targeting Smart Thermostats
IEEE Embedded Systems Letters, 9 (4), 2017
Journals
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- Lazaros Papadopoulos ,
- A. Ampatzoglou ,
- Charalampos Marantos ,
- A. Chatzigeorgiou ,
- G. Digkas and
- Dimitrios Soudris
Interrelations between software quality metrics, performance and energy consumption in embedded applications
Proceedings of the 21st International Workshop on Software and Compilers for Embedded Systems, SCOPES 2018 -
Efficient support vector machines implementation on Intel/Movidius Myriad 2
2018 7th International Conference on Modern Circuits and Systems Technologies, MOCAST 2018 -
- M. Siavvas ,
- D. Tsoukalas ,
- Charalampos Marantos ,
- A. Tsintzira ,
- M. Jankovic ,
- Dimitrios Soudris ,
- A. Chatzigeorgiou and
- D. Kehagias
The SDK4ED Platform for Embedded Software Quality Improvement – Preliminary Overview
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020 -
- Charalampos Marantos ,
- A. Tsintzira ,
- Lazaros Papadopoulos ,
- A. Ampatzoglou ,
- A. Chatzigeorgiou and
- Dimitrios Soudris
Technical Debt Management and Energy Consumption Evaluation in Implantable Medical Devices: The SDK4ED Approach
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020 -
- C. Karakasis ,
- K. Machairas ,
- Charalampos Marantos ,
- Iosif Paraskevas ,
- E. Papadopoulos and
- Dimitrios Soudris
Exploiting the SoC FPGA capabilities in the control architecture of a quadruped robot
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2020 -
- Charalampos Marantos ,
- Iosif Paraskevas ,
- Kostas Siozios ,
- Josiane Mothe ,
- Colette Menou and
- Dimitrios Soudris
FabSpace 2.0: A Platform for Application and Service Development based on Earth Observation Data
in The International Conference on Modern Circuits and Systems Technologies (MOCAST), 2017
Conferences
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Towards plug&play smart thermostats inspired by reinforcement learning
Workshop on INTelligent Embedded Systems Architectures and Applications  BibTeX@inproceedings{Marantos:2018:TPS:3285017.3285024, author = {Marantos, Charalampos and Lamprakos, Christos P. and Tsoutsouras, Vasileios and Siozios, Kostas and Soudris, Dimitrios}, title = {Towards Plug\&\#38;Play Smart Thermostats Inspired by Reinforcement Learning}, booktitle = {Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications}, series = {INTESA '18}, year = {2018}, isbn = {978-1-4503-6598-7}, location = {Turin, Italy}, pages = {39--44}, numpages = {6}, url = {http://doi.acm.org/10.1145/3285017.3285024}, doi = {10.1145/3285017.3285024}, acmid = {3285024}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {HVAC control, decision making, embedded software, energy efficiency, intelligent agents, learning systems}, }