TY - GEN
T1 - Internet of Personalized and Autonomous Things (IoPAT)
AU - Elmalaki, Salma
AU - Shoukry, Yasser
AU - Srivastava, Mani
N1 - KAUST Repository Item: Exported on 2021-04-01
Acknowledged KAUST grant number(s): Sensor Innovation research program
Acknowledgements: This research was supported in part by the U.S. Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196, by the National Science Foundation under award # OAC-1640813 and IIS-1636916, and the King Abdullah University of Science and Technology (KAUST) through its Sensor Innovation research program. The Microsoft Research PhD Fellowship has supported Salma Elmalaki. Any findings in this material are those of the author(s) and do not reflect the views of any of the above funding agencies. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2018/11/4
Y1 - 2018/11/4
N2 - IoT devices are permeating every corner of our lives today paving the road for more substantial smart systems. Despite their ability to collect and analyze a significant amount of sensory data, traditional IoT typically depends on fixed policies and schedules to enhance user experience. However, fixed policies that do not account for variations in human mood, reactions, and expectations, fail to achieve the promised user experience. In this paper, we propose an architecture for personalized and autonomous IoT systems that weaves personalization and context-awareness into the very fabric of smart systems. By building upon ideas from reinforcement learning, we show—using an example of smart and personalized home services—how the proposed architecture can adapt to human behaviors that are varying between individuals and vary, for the same individual, across time while addressing some of the security and privacy challenges.
AB - IoT devices are permeating every corner of our lives today paving the road for more substantial smart systems. Despite their ability to collect and analyze a significant amount of sensory data, traditional IoT typically depends on fixed policies and schedules to enhance user experience. However, fixed policies that do not account for variations in human mood, reactions, and expectations, fail to achieve the promised user experience. In this paper, we propose an architecture for personalized and autonomous IoT systems that weaves personalization and context-awareness into the very fabric of smart systems. By building upon ideas from reinforcement learning, we show—using an example of smart and personalized home services—how the proposed architecture can adapt to human behaviors that are varying between individuals and vary, for the same individual, across time while addressing some of the security and privacy challenges.
UR - http://hdl.handle.net/10754/668424
UR - https://dl.acm.org/doi/10.1145/3277893.3277901
UR - http://www.scopus.com/inward/record.url?scp=85058214546&partnerID=8YFLogxK
U2 - 10.1145/3277893.3277901
DO - 10.1145/3277893.3277901
M3 - Conference contribution
SN - 9781450360517
SP - 35
EP - 40
BT - Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing
PB - ACM
ER -