Mhetre, N. A.; Deshpande, A. V.; Mahalle, P. N. Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning. Regular issue, 2021, 10, 135–142. https://doi.org/10.35940/ijeat.e2688.0610521.
Mhetre, N. A.; Deshpande, A. V.; Mahalle, P. N. Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning. Regular issue, 2021, 10, 135–142. https://doi.org/10.35940/ijeat.e2688.0610521.
Mhetre, N. A.; Deshpande, A. V.; Mahalle, P. N. Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning. Regular issue, 2021, 10, 135–142. https://doi.org/10.35940/ijeat.e2688.0610521.
Mhetre, N. A.; Deshpande, A. V.; Mahalle, P. N. Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning. Regular issue, 2021, 10, 135–142. https://doi.org/10.35940/ijeat.e2688.0610521.
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 automatic 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. This paper fulfills an identified need of proactive device classification for device management.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
