QAIR: Quality Assessment Scheme for Information Retrieval in IoT Infrastructures

Conference paper


Makkar, A., Kumar, N., Obaidat, M.S. and Hsiao, K.-F. 2018. QAIR: Quality Assessment Scheme for Information Retrieval in IoT Infrastructures. 2018 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/glocom.2018.8647180
AuthorsMakkar, A., Kumar, N., Obaidat, M.S. and Hsiao, K.-F.
TypeConference paper
Abstract

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

KeywordsClassification (of information); Data Analytics; Data mining; Decision trees; Information retrieval; Websites; Internet of things
Year2018
Conference2018 IEEE Global Communications Conference
Journal2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN2576-6813
Digital Object Identifier (DOI)https://doi.org/10.1109/glocom.2018.8647180
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85063515878&partnerID=MN8TOARS
ISBN978-153864727-1
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8634808/proceeding
Output statusPublished
Publication dates
Online21 Feb 2019
Publication process dates
Deposited22 May 2023
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