2019
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Palopoli, F.; Spezzano, G.; Vinci, A.
Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning Proceedings Article
In: 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, 2019, ISBN: 9781728129235.
Abstract | Links | BibTeX | Tag: Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings
@inproceedings{Cicirelli2019,
title = {Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and F. Palopoli and G. Spezzano and A. Vinci},
doi = {10.1109/DS-RT47707.2019.8958661},
isbn = {9781728129235},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
journal = {Proceedings - 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019},
publisher = {IEEE},
abstract = {This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.},
keywords = {Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.
Cicirelli, F.; Guerrieri, A.; Mercuri, A.; Spezzano, G.; Vinci, A.
ITEMa: A methodological approach for cognitive edge computing IoT ecosystems Journal Article
In: Future Generation Computer Systems, vol. 92, 2019, ISSN: 0167739X.
Abstract | Links | BibTeX | Tag: activity recognition, Cognitive Systems, domus, Edge and cloud computing, IoT-based ecosystems, Smart Office
@article{Cicirelli2019c,
title = {ITEMa: A methodological approach for cognitive edge computing IoT ecosystems},
author = {F. Cicirelli and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},
doi = {10.1016/j.future.2018.10.003},
issn = {0167739X},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Future Generation Computer Systems},
volume = {92},
abstract = {The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.},
keywords = {activity recognition, Cognitive Systems, domus, Edge and cloud computing, IoT-based ecosystems, Smart Office},
pubstate = {published},
tppubtype = {article}
}
The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.