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Mobile Information Systems publishes original research articles as well as review articles that report the theory and/or application of new ideas and concepts in the field of mobile information systems.
Chief Editor Dr Alessandro Bazzi is based at the University of Bologna, Italy. His current research is focused on wireless technologies applied to automated and connected vehicles.
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Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment
Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.
Exploring the Impact of Avatar Customization in Metaverse: The Role of the Class Mode on Task Engagement and Expectancy-Value Beliefs for Fashion Education
This study aims to explore the impact of avatar customization in metaverse environments, especially for fashion education. Considering the unique nature of fashion as an educational field in which theories and practices are equally significant, the impact of class modes (theoretical versus practical) on expectancy and value toward the class in the metaverse environment is empirically investigated, with task engagements as mediators. Students’ creative self-efficacy, an individual characteristic, was considered as a moderator. A total of 38 female undergraduate students participated voluntarily. In the experimental session, the participants were randomly allocated to one of the two class mode conditions, theoretical or practical. They were then asked to customize their own avatars for use in the metaverse class and write a descriptive essay about the avatar they made. The results showed that the practical class mode evoked higher engagements (i.e., dedication and absorption), which demonstrates the participants’ positive expectancy (i.e., self-efficacy for learning and performance) and value (i.e., task value belief) toward learning in the metaverse class. Interestingly, participants’ creative self-efficacy played a moderating role in the impact of dedication on expectancy and value in different directions, while the impact of absorption was positive regardless of participants’ creative self-efficacy level. Additionally, we found that expectancy and value toward learning led to the participants’ positive class engagement intention.
Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks
Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.
A Novel Model for Intelligent Pull-Ups Test Based on Key Point Estimation of Human Body and Equipment
Applying computer vision and machine learning techniques into sport tests is an effective way to realize “intelligent sports.” Facing practical application, we design a real-time and lightweight deep learning network to realize intelligent pull-ups test in this study. The main contributions are as follows: (1) a new self-produced pull-ups dataset is established under the requirement of including a human body and horizontal bar. In addition, a semiautomatic annotating software is developed to enhance annotation efficiency and increase labeling accuracy. (2) A novel lightweight deep network named PEPoseNet is designed to estimate and analyze a human pose in real time. The backbone of the network is made up of the heatmap network and key point network, which conduct human pose estimation based on the key points extracted from a human body and horizontal bar. The depth-wise separable convolution is adopted to speed up the training and convergence. (3) An evaluation criterion of intelligent pull-ups test is defined based on action quality assessment (AQA). The action quality of five states, i.e., ready or end, hang, pull, achieved, and resume in one pull-ups test cycle is automatically graded using a random forest classifier. A mobile application is developed to realize intelligent pull-ups test in real time. The performance of the proposed model and software is confirmed by verification and ablation experiments. The experimental results demonstrated that the proposed PEPoseNet has competitive performance to the state of the art. Its PCK @ 0.2 and frames per second (FPS) achieved were 83.8 and 30 fps, respectively. The mobile application has promising application prospects in pull-ups test under complex scenarios.
A Secure and Efficient Access Control Scheme for Shared IoT Devices over Blockchain
The concept of shared IoT devices has attracted much attention from the industry sector, academia, and financial institutions, providing various benefits, such as saving resources, reducing personal expenses, and providing convenience. Although shared IoT devices facilitate people’s lives and work, the information exchange is over wireless networks that may suffer from some security attacks such as unauthorized access to a shared device or some private information of legitimate users being leaked. It makes the secure access control to the shared IoT devices become an intractable issue. In order to guarantee the access right of the legitimate users, to prevent the problems of privacy leakage and unnecessary economic disputes, a secure decentralized access control scheme for shared IoT devices is proposed leveraging the technologies of blockchain and a proposed authentication protocol in this paper. The new lightweight authentication protocol is proposed to perform mutual authentication between the user and the IoT device. To protect the privacy of the user, the instruction data are encrypted by a temporary session key negotiated between the user and the IoT device with the help of blockchain which enables nontamperable transactions and prevents central corruption and single point of failure. In our scheme, blockchain is maintained by the gateway nodes an acts as a distributed database and a smart contract for shared service is deployed on it. The smart contract has three functions in our scheme: (1) achieving the prepayment of users and settlement for the service contributor, (2) participating in a verification step during the key negotiation to prevent some malicious behaviour from users or devices, (3) recording the workload of the gateway. Finally, a comprehensive analysis on the safety and reliability of the entire scheme is carried out; extensive simulation experiments are conducted to reveal the authentication protocol is efficient and the scheme is feasible.
Performance Analysis of Directional Ultra-Dense Networks with Dynamic Spectrum Partition Strategy
Intercell interference coordination (ICIC) plays a significant role in strengthening ultra-dense network (UDN) downlink coverage. From a statistical average perspective, a user is primarily interfered by its adjacent base station (BS), especially the second nearest BS. By modeling BSs equipped with directional antennas as a Poisson point process (PPP), this paper proposes a dynamic spectrum resource allocation strategy mainly about users’ service BS and its nearest interference BS, where the subchannel assigned by the typical (served) user is interlaced from the channel simultaneously occupied by users within the effective radiation range of its second nearest BS. To fully explore this scheme for directional networks, we develop analytical expressions in terms of success probability and ergodic rate for the typical user based on the techniques of stochastic geometry, taking into account the fading of directional BS radiation angle. Then, we derive the meta distribution of the signal-to-interference ratio (SIR) for capturing individual link performance changes of users. Simulations verify the correctness of numerical results, and it is revealed that this strategy is in favor of users alleviating interference from their second nearest BSs and the performance advantages of the proposed ICIC strategy are better than those of the traditional directional UDNs.