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research-article

An RFID-Enabled IoT-Based Smart Tourist Route Recommendation Algorithm

Author:

Zhao Hua Academic Editor:

Mian Ahmad JanAuthors Info & Claims

Mobile Information Systems, Volume 2022

https://doi.org/10.1155/2022/9866086

Published: 01 January 2022 Publication History

Abstract

With the rapid development and broad deployment of Internet of Things (IoT) technologies, the IoT are increasingly shifting away from “interconnection of everything” to “human-computer-thing” sensing integration. Although there are numerous sensing technologies available today, radio frequency identification (RFID) has emerged as useful medium for “passive sensing” due to its lightweight, taggable, and simple deployment properties. With the growth of social networks in recent years, it has become a significant research hotspot for the development of path suggestion systems that are tailored to the demands of individual users’ preferences. This paper considers the relevant features of interest points, integrates the user’s emotion and product similarity into the heuristic function of the ant colony algorithm, adopts the elite management ant strategy, maximizes the management ant strategy, and uses particle swarm algorithm to improve the initial pheromone distribution of the ant colony algorithm. The proposed model combines the ratings of 593 tourists and text comment information into one dataset and proposes a smart tourist route recommendation model. The improved ant colony algorithm is utilized to recommend the most popular tourist routes and recommend the tourist routes of the most popular tourist spots in the scenic area. The suggested method is more efficient in terms of accuracy and recall. The F measure value is derived from real-world dataset testing.

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Cited By

Makhdomi AGillani I(2023)Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation TechniquesACM Transactions on Intelligent Systems and Technology10.1145/362782515:1(1-57)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627825
Li CQiao L(2022)Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov ChainMobile Information Systems10.1155/2022/87262062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8726206

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Information & Contributors

Information

Published In

cover image Mobile Information Systems

Mobile Information Systems Volume 2022, Issue

2022

19033 pages

ISSN:1574-017X

EISSN:1875-905X

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Copyright © 2022 Zhao Hua.

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.

Publisher

IOS Press

Netherlands

Publication History

Published: 01 January 2022

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Cited By

Makhdomi AGillani I(2023)Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation TechniquesACM Transactions on Intelligent Systems and Technology10.1145/362782515:1(1-57)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627825
Li CQiao L(2022)Rapid Prediction Algorithm for Economic Development Trend of Tourism Using Markov ChainMobile Information Systems10.1155/2022/87262062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8726206

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