Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning
Nalini A. Mhetre1, Arvind V. Deshpande2, Parikshit Narendra Mahalle3
1Nalini A. Mhetre*, Dept. of Computer Engg., Sinhgad College of Engg., Pune (Maharashtra), India.
2Arvind V. Deshpande, Dept. of Computer Engg., S.K.N. College of Engg., Pune (Maharashtra), India.
3Parikshit Narendra Mahalle, Dept. of Computer Engg., S.K.N. College of Engg., Pune (Maharashtra), India.
Manuscript received on May 24, 2021. | Revised Manuscript received on May 31, 2021. | Manuscript published on June 30, 2021. | PP: 135-142 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E26880610521 | DOI: 10.35940/ijeat.E2688.0610521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources.
Keywords: Context Management, Device Classification, IoT Device Management, K-Means Clustering, Ubiquitous Computing, Unsupervised Machine Learning.
Scope of the Article: Machine Learning