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A Cognitive-IoE Approach to Ambient-intelligent Smart Home

Topics: Context-awareness and Location-awareness ; Intelligent Systems for IoT and Services Computing ; Internet of Things; IoT Services and Applications; Network Design and Architecture ; Sensor, Wireless Technologies, APIs; Software Architecture and Middleware ; Technological focus for Smart Environments

Authors: Gopal Jamnal and Xiaodong Liu

Affiliation: Edinburgh Napier University, United Kingdom

ISBN: 978-989-758-245-5

Keyword(s): Intelligent Inhabited Environment, Ambient Intelligent Smart Home, Activity Pattern Recognition, Cognitive IoTs, and Cyber Physical System.

Related Ontology Subjects/Areas/Topics: Data Communication Networking ; Enterprise Information Systems ; Internet of Things ; Sensor Networks ; Software Agents and Internet Computing ; Software and Architectures ; Telecommunications

Abstract: In today’s world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. Cognitive IoE is a new state-of-art computing paradigm for interconnecting and controlling network objects in context-aware perception-action cycle for our cognitive needs. The interconnected objects (sensors, RFID, network objects etc.) behave as agents to learn, think and adapt situations according to dynamic contextual environment with no or minimum human intervention. One most important recent research problem is “how to recognize inhabitant activity patterns from the observed sensors data”. In this paper, we proposed a two level classification model named as ACM (Ambient Cognition Model) for inhabitant’s activities pattern recognition, using Hidden Markov Model based probabilistic model and subt ractive clustering classification method. While subtractive clustering separates similar activity states from non-similar activity state, a HMM works as the top layer to train systems for temporal-sequential activities to learn and predict inhabitant activity pattern proactively. The proposed ACM framework play, a significant role to identify user activity intention in more proactive manner such as routine, location, social activity intentions in smart home scenario. The experimental results have been performed on Matlab simulation to evaluate the efficiency and accuracy of proposed ACM model. (More)

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Paper citation in several formats:
Jamnal, G. and Liu, X. (2017). A Cognitive-IoE Approach to Ambient-intelligent Smart Home.In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 302-308. DOI: 10.5220/0006304103020308

@conference{iotbds17,
author={Gopal Jamnal. and Xiaodong Liu.},
title={A Cognitive-IoE Approach to Ambient-intelligent Smart Home},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2017},
pages={302-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006304103020308},
isbn={978-989-758-245-5},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Cognitive-IoE Approach to Ambient-intelligent Smart Home
SN - 978-989-758-245-5
AU - Jamnal, G.
AU - Liu, X.
PY - 2017
SP - 302
EP - 308
DO - 10.5220/0006304103020308

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