Sokratis Kartakis completed his PhD at Imperial College London, at the Adaptive Embedded Systems Engineering (AESE) Laboratory under Dr. Julie A. McCann supervision in 2017. His research is based on Wireless Sensor Networks (WSN), Internet of Things (IoT) Cyber-Physical Systems (CPS) and he is involved in the “Big data technologies for smart water networks” sponsored by NEC Japan.
He received his BSc with distinction in 2006 and he started his graduate studies in the same year. He earned a MSc in October 2008, in the scientific fields of Ambient Intelligence (AmI) and Human Computer Interaction (HCI) and his Master Thesis, titled “Tool Support for Designing and Generating Accessible User Interfaces in Ambient Intelligence Environments”, published in “Computers in Industry” journal (2010). Alongside his studies he had undergraduate and graduate fellowship with HCI Laboratory of ICS-FORTH, Crete, Greece (2003 – 2008).
The next two years (2009-2011), he worked as Lecturer and Lab Coordinator at Science Department of Technological Institute of Crete, Greece, and as Senior Software Engineer and Research Staff, in the project of Ambient Intelligent and Smart Home, at HCI Laboratory of ICS-FORTH.
In 2011, he moved at Yale University, and ENALAB Laboratory of Electrical Engineering Department, as Visiting Assistant in Research. He continued his research in the field of Smart Environment from the perspective of Power Consumption and CO2 reduction, and Empowerment of Wellness of Elders.
After that, he cooperated with Seldera LLC of Ameresco in projects about the representation and analysis of commercial buildings power data, and algorithms development in order to provide weather normalized data and building power data comparison.
- Kartakis, S., Yang, S., and McCann, J. A. (2017). Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water. ACM Transactions on Sensor Networks (TOSN). (Accepted)
- Kartakis, S., Fu, A., Espinosa, M. M., and McCann, J. A. (2016). Evaluation of Decentralized Event-Triggered Control Strategies for Cyber-Physical Systems. arXiv preprint arXiv:1611.04366
- Kartakis, S., Choudhary, B. D., Gluhak, A. D., Lambrinos, L., & McCann, J. A. (2016). Demystifying Low-Power Wide-Area Communications for City IoT Applications. In Proceeding of the 10th ACM International Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization, MobiCom 2016. ACM
- Kartakis, S., Jevric, M. M., Tzagkarakis, G., & McCann, J. A. (2016). Energy-based Adaptive Compression in Water Network Control Systems. In Proceedings of the 2st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks, CPSWeek 2016. ACM
- Kartakis, S., Yu, W., Akhavan, R., & McCann, J. A. (2016). Adaptive Edge Analytics for Distributed Networked Control of Water Systems. In Proceedings of the 1st IEEE International Conference on Internet-of-Things Design and Implementation. 4-8 April 2016, Berlin, German.
- Kartakis, S., Tzagkarakis, G., & McCann, J. A. (2015). Adaptive Compressive Sensing in Smart Water Networks. In 2nd International Electronic Conference on Sensors and Applications. Multidisciplinary Digital Publishing Institute.
- Kartakis, S., Abraham, E., & McCann, J. A. (2015). WaterBox: A Testbed for Monitoring and Controlling Smart Water Networks. In Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks (p. 8), CPSWeek 2015. ACM
- Kartakis, S., McCann, J. (2014). Real-time Edge Analytics for Cyber Physical Systems using Compression Rates. 11th International Conference on Autonomic Computing (ICAC) 2014. 17-20 June 2014, Philadelphia, PA, USA.
BentoBox: Hybrid Communication Sensor Node (LoRA, Xbee868, NWave, WiFi, Bluetooth)
To conduct the field experiments we developed BentoBox; a custom hardware platform based on the Intel Edison board. BentoBox incorporates multiple Low Power Wide Area (LPWA) communication modules, software to facilitate easy programming of the modules for different experiments, and experimental data collection mechanisms
WaterBox: Smart Water Network Emulator
WaterBox simulates smart water networks and enables the evaluation of in-node decision making, energy
optimization, automatic control, and event-driven communication algorithms.