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New Approach for Optimizing the Usage of Situation Recognition Algorithms Within IoT Domains

Mammadova, Chinara and Ben Hmida, Helmi and Braun, Andreas and Kuijper, Arjan (2017):
New Approach for Optimizing the Usage of Situation Recognition Algorithms Within IoT Domains.
pp. 183-196, AmI 2017 - 13th European Conference on Ambient Intelligence, Malaga, Spain, April 26.–28., 2017, ISBN 978-3-319-56996-3,
DOI: 10.1007/978-3-319-56997-0_15,
[Conference or Workshop Item]

Abstract

The growth of the Internet of Things (IoT) over the past few years enabled a lot of application domains. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the continuously running situation recognition algorithms. To overcome these problems, this paper proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received, thus allowing the reduction of the situation recognition algorithms execution frequency and saving computational resources.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Mammadova, Chinara and Ben Hmida, Helmi and Braun, Andreas and Kuijper, Arjan
Title: New Approach for Optimizing the Usage of Situation Recognition Algorithms Within IoT Domains
Language: English
Abstract:

The growth of the Internet of Things (IoT) over the past few years enabled a lot of application domains. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the continuously running situation recognition algorithms. To overcome these problems, this paper proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received, thus allowing the reduction of the situation recognition algorithms execution frequency and saving computational resources.

ISBN: 978-3-319-56996-3
Uncontrolled Keywords: Internet of things (IoT), Ambient assisted living (AAL), Smart home, Situation analysis, Situation aware assistance, Smart living
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: AmI 2017 - 13th European Conference on Ambient Intelligence
Event Location: Malaga, Spain
Event Dates: April 26.–28., 2017
Date Deposited: 04 May 2020 12:46
DOI: 10.1007/978-3-319-56997-0_15
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-319-56997-...
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