DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices
In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a dec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720962999 |
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| _version_ | 1832556707496067072 |
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| author | Márcio Alencar Raimundo Barreto Horácio Fernandes Eduardo Souto Richard Pazzi |
| author_facet | Márcio Alencar Raimundo Barreto Horácio Fernandes Eduardo Souto Richard Pazzi |
| author_sort | Márcio Alencar |
| collection | DOAJ |
| description | In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem. |
| format | Article |
| id | doaj-art-cd6628c364414a7f92bf540d52b28900 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-cd6628c364414a7f92bf540d52b289002025-02-03T05:44:34ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-10-011610.1177/1550147720962999DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devicesMárcio Alencar0Raimundo Barreto1Horácio Fernandes2Eduardo Souto3Richard Pazzi4Federal University of Amazonas (UFAM), Manaus, BrazilFederal University of Amazonas (UFAM), Manaus, BrazilFederal University of Amazonas (UFAM), Manaus, BrazilFederal University of Amazonas (UFAM), Manaus, BrazilOntario Tech University (UOIT), Oshawa, ON, CanadaIn the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.https://doi.org/10.1177/1550147720962999 |
| spellingShingle | Márcio Alencar Raimundo Barreto Horácio Fernandes Eduardo Souto Richard Pazzi DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices International Journal of Distributed Sensor Networks |
| title | DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices |
| title_full | DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices |
| title_fullStr | DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices |
| title_full_unstemmed | DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices |
| title_short | DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices |
| title_sort | dare a decentralized association rules extraction scheme for embedded data sets in distributed iot devices |
| url | https://doi.org/10.1177/1550147720962999 |
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