Abstract
In recent years, the Internet of Things has developed rapidly in people’s lives. This brand‐new technology is flooding people’s lives and widely used in many fields, such as medical field, science and technology field, and industry and agriculture field. As a modern technology, the Internet of Things has many characteristics of low power consumption and multifunction, and it also has the characteristics of data‐aware computing. This is the characteristic of this new product. In people’s daily life, the Internet of Things is also closely related to people’s daily life. In the tourism industry, the Internet of Things can make the best use of everything and give full play to its various advantages as much as possible. The Internet of Things can perceive cross‐modal tourism routes. So here, this paper summarizes various algorithms recommended by the Internet of Things for this tourist route and works out the experimental data methods of these algorithms for cross‐modal tourism route recommendation. The proposed algorithm is verified by data simulation, compared with related algorithms. We analyze and summarize the simulation results. At present, there is no comparative analysis of the performance of ant colony algorithm, genetic algorithm, and its optimization algorithm in tourism route recommendation. On the basis of crawling the tourism data in the Internet, this paper applies ant colony algorithm, genetic algorithm, max–min optimization ant colony algorithm, and hybrid ant colony algorithm based on greedy solution to tourism route recommendation and evaluates and compares the algorithms from three aspects: average evaluation score, optimal evaluation score, and algorithm time. Experimental results show that the max–min optimization ant colony algorithm and the hybrid ant colony algorithm based on greedy solution can be effectively applied to automated tourist route recommendation.
Highlights
Internet of Things is interwoven by different modern technologies, including wireless communication technology and live data analysis technology, machine language learning technology, sensor technology, and built-in embedded system [1]
The results show that the optimal solution obtained by the genetic algorithm is within a certain error range with the known optimal solution; it can find the path sequence and path value which are very close to the known optimal solution provided by the database
From the data obtained from the solution of the greedy algorithm to the optimal travel path, it can be seen that the influence of the fluctuation of discrete values on the greedy algorithm lies in the fact that the lower the discrete values, the greater the fluctuation
Summary
Internet of Things is interwoven by different modern technologies, including wireless communication technology and live data analysis technology, machine language learning technology, sensor technology, and built-in embedded system [1]. As a key technology in the sensing field of Internet of Things, a wireless sensor network is a special ad hoc network [4], which plays a very important role in the perception of Internet of Things It does not need fixed network support and has the characteristics of fast development and strong survivability [5]. In this paper, the ant colony algorithm is used to study the data of cross-modal travel route algorithm under the Internet of Things, and its research direction is to track a single target under the perception of the Internet of Things [11]. The third part explains the collection of wireless sensor network routing protocols and related algorithms applied to travel route planning model; the fourth part is the experimental comparison of algorithms combined with various mathematical models
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