Handling heterogeneity in context aware services

Eijk, Ronald van; Salden, Alfons H.; Heer, Johan de; Peddemors, Arjan J. H.; Määttä, Petri; Haataja, Ville

doi:10.1108/17427370580000110

Cited by 1 publication

(1 citation statement)

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“…Context-aware services respond and adapt to changes in their computing environment by designing policy rules [10]. As ubicomp comprises heterogeneous devices, context related to these devices [11] and their capabilities is important for context-aware applications . This context may affect the behavior of these applications as they not only use user interactions and their internal state information but also context information sensed during execution.…”

Section: Motivationmentioning

confidence: 99%

Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning

Mhetre¹,

Deshpande²,

Mahalle³

2021

IJEAT

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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.

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Section: Motivationmentioning

confidence: 99%