Architecture For Location-based Services Research Articles

Cloud Edge-Client Collaborative Trajectory Privacy Protection System and Technology

Location-based service (LBS), with its personalized, real time, and mobile features, is one of the more popular mobile applications (apps) in the world today. However, under the centralized LBS architecture, the high dimensional trajectory data collected from LBS users are directly exposed to the central server, which entails serious privacy risks. Although existing privacy protection technologies can protect the private user trajectories to a certain extent, they often ignore the reasonable data requirements of LBS providers (e.g., data required to maintain or improve services). Focusing on the weaknesses of centralized LBS and the shortcomings of existing solutions, this article proposes a cloud edge-client collaborative trajectory privacy protection system. The system migrates LBS from the cloud to the network edge and balances privacy and utility-including service utility and data utility-with anonymous authentication, dummy location, and privacy risk evaluation mechanisms. The theoretical analysis and simulation results show that the system can effectively protect trajectory privacy under the premise of high availability of services and data, which is a significant improvement over the current LBS system.

Online Learning Support Service System Architecture Based on Location Service Architecture

With the development of network, location-based service architecture is gradually applied to the learning support service system. The purpose of this study is to develop an online learning support system using allocation information service architecture. In this study, the 2019 year of this study was selected as a research material through an online learning support service system within six months. The system architecture of this study is the Browser/Server architecture. Based on this, we analyze the learning result data using the weight setting of the feature attribute and the learning evaluation algorithm. The results show that the improved algorithm based on location-based service architecture can get the best algorithm accuracy when the weight of the whole algorithm is about α = 0.4. For the degree of demand for learning guidance, 16.2% thought it was absolutely necessary and 50.7% thought it was more necessary. And 70.6% of the learners thought that the learning system was not very accurate or generally accurate. It is concluded that the learning support service system of this study has a certain role. It provides a new path to improve the quality of online education learning support service and promotes the development of online education.

Location Privacy Protection via Delocalization in 5G Mobile Edge Computing Environment

In this paper, we propose LBS@E, a new architecture for location-based services (LBSs) facilitated by the mobile edge computing paradigm. LBS@E tackles the location privacy problem innovatively by delocalizing LBSs so that mobile users of LBSs implemented based on LBS@E do not have to reveal their locations. They retrieve local information from nearby edge servers around them instead of the cloud. In this way, we resolve the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile users location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. We model this problem as a constrained optimization problem and propose an optimal approach for solving it based on integer programming. Extensive experiments are conducted on a widely-used real-world dataset to demonstrate effectiveness and efficiency.