Personalization of Multi-day Round Trip Itineraries According to Travelers’ Preferences

Travel planning is a long and tedious process for tourists since it requires processing a vast amount of information. Recommender systems can be used to facilitate the process of scoring points-of-interests (POIs) according to the travelers’ interests and creating feasible itineraries. However, itinerary planning is personal and each itinerary created must reflect the interest of the traveler as well as his/her travel style. In this paper, we extend the creation of multi-day round trip itineraries by adding different personalization options such as the pace of the traveler and diversity level of the route. The information about the travel style of the user is used to personalize the visiting duration of each POI and to create routes for each day that follow the constraints defined by users. We conducted a user study through a mobile application and the results show that the added personalization options improved the recommended multi-day round trip walking tours from a user’s perspective.

Dynamic Modeling and Analysis of Multidimensional Hybrid Recommendation Algorithm in Tourism Itinerary Planning under the Background of Big Data

Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists’ tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction.

Data Aggregation Scheme Using Multiple Mobile Agents in Wireless Sensor Network

Wireless sensor networks (WSNs) consist of large number of sensor nodes densely deployed in monitoring area with sensing, wireless communications and computing capabilities. In recent times, wireless sensor networks have used the concept of mobile agent for reducing energy consumption and for effective data collection. The fundamental functionality of WSN is to collect and return data from the sensor nodes. Data aggregation’s main goal is to gather and aggregate data in an efficient manner. In data gathering, finding the optimal itinerary planning for the mobile agent is an important step. However, a single mobile agent itinerary planning approach suffers from two drawbacks, task delay and large size of the mobile agent as the scale of the network is expanded. To overcome these drawbacks, this research work proposes: (i) an efficient data aggregation scheme in wireless sensor network that uses multiple mobile agents for aggregating data and transferring it to the sink based on itinerary planning and (ii) an attack detection using TS fuzzy model on multi-mobile agent-based data aggregation scheme is shortly named as MDTSF model.

Directional Itinerary Planning for Multiple Mobile Agents in Wireless Sensor Networks

Currently, the majority of research in the area of wireless sensor networks (WSNs) is directed towards optimizing energy use during itinerary planning by mobile agents (MAs). The route taken by the MA when migrating can get a significant effect on energy consumption and the lifespan of the network. Conversely, finding an ideal arrangement of Source Nodes (SNs) for mobile agents to visit could be a problematic issue. It is within this framework that this work focused on solving certain problems related to itinerary planning based on a multimobile agent (MMA) strategy in networks. The objective of our research was to increase the lifespan of sensor networks and to diminish the length of the data collection task. In order to achieve our objective, we proposed a new approach in WSNs, which took into consideration the criterion of an appropriate number of MAs, the criterion of the appropriate grouping of SNs, and finally the criterion of the optimal itinerary followed by each MA to visit all its SNs. Thus, we suggested an approach that may be classified as a centralized planning model where the itinerary schedule is entirely shaped by the base station (sink) which, unlike other approaches, is no longer constrained by energy consumption. A series of simulations to measure the performance of the new planning process was also carried out.