Open Access

ARTICLE

Trust and QoS-Driven Query Service Provisioning Using Optimization

by K. Narmatha^1,*, K. Karthikeyan²

1 Department of Computer Science and Engineering, SRM Institute of Engineering and Technology, Ramapuram, Chennai, India
2 Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, India

* Corresponding Author: K. Narmatha. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1827-1844. https://doi.org/10.32604/iasc.2023.028473

Received 11 February 2022; Accepted 24 June 2022; Issue published 05 January 2023

Abstract

The growing advancements with the Internet of Things (IoT) devices handle an enormous amount of data collected from various applications like healthcare, vehicle-based communication, and smart city. This research analyses cloud-based privacy preservation over the smart city based on query computation. However, there is a lack of resources to handle the incoming data and maintain them with higher privacy and security. Therefore, a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment. A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards vulnerability. If handling privacy, other inter-connected metrics like efficiency are also essential, which must be considered to fulfil the privacy requirements. Therefore, this work provides a query-based service model and clusters the query to measure the relevance of frequently generated queries. Here, a Bag of Query (BoQ) model is designed to collect the query from various sources. Validation is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get the final results. The processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Optimizer (GWO). It is used to attain global and local solutions regarding privacy preservation. The iterative data is evaluated without any over-fitting issues and computational complexity due to the tremendous data handling process. Based on this analysis, metrics like privacy, efficiency, computational complexity, the error rate is analyzed. The simulation is done with a MATLAB 2020a environment. The proposed model gives a better trade-off in contrast to existing approaches.

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Keywords

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