#### Wireless Communications and Mobile Computing

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ISSN / EISSN : 1530-8669 / 1530-8677
Total articles ≅ 6,843
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#### Latest articles in this journal

Zhoukai Wang, Huaijun Wang, Liu He, Yang Lv, Zhaoying Wei, Xuan Li
Published: 3 October 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-13; https://doi.org/10.1155/2022/3222979

Abstract:
The massive amount of sensing and communication data that needs to be processed during the production process of complex heavy equipment generates heavy storage pressure on the cloud server-side, thus limiting the convergence of sensing, communication, and computing in intelligent factories. To solve the problem, based on machine learning techniques, a storage optimization model is proposed in this paper for reducing the storage pressure on the cloud server and enhancing the coupling between communication and sensing data. At first, based on the operation rules of the distributed file system on the cloud server, the proposed model screens and organizes the system logs. With the filtered logs, the model sets feature labels, constructs feature vectors, and builds sample sets. Then, based on the ID3 decision tree, a file elimination model is trained to analyze the files stored in the cloud server and predict their reusability. In practice, the proposed model is applied in the Hadoop Distributed File System and helps the system delete underutilized and low-value files and save storage space. Experiments show that the proposed model can effectively reduce the storage load on the cloud server and improve the integration efficiency of multisource heterogeneous data during complex heavy equipment production.
, Taiyuan Zhang, Huanli Gao, Jiahao Long, Jiali Wen
Published: 3 October 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-16; https://doi.org/10.1155/2022/4708496

Abstract:
The predation of large school of fish by the cooperation of multiple predators of different species is one of the most marvelous scene in the sea. In this paper, we study how to drive the fish school to the surface of the sea by the cooperation of multiple predators. In particular, we have first proposed a modified Couzin model for the fish school, which, on top of the classic Couzin model, also takes into consideration the effect imposed passively by the predators. The basic idea for the driven algorithm is to drive behind the fish school to some delicate extent so that, on one hand, the fish school shall move towards the target region, and on the other hand, the fish school will not disperse and spread away. Moreover, if some fish strays away from the fish school, the predator will take actions to force it back which can greatly reduce the possibility for the fish school to split into multiple subgroups. Comprehensive simulation results are provided to validate the proposed algorithm.
Zhongliang Shen, Zijun Zheng, Lijun Zhu, Shaozeng Yang, Yonggen Cai, Mingge Wu
Published: 3 October 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-7; https://doi.org/10.1155/2022/9171876

Abstract:
Aiming at the problem of the study, of high error probability in the practical application of traditional hydromechanical calendering process, the hydromechanical calendering process is optimized based on wireless network communication. GPRS or LORaipot-IOT wireless communication mode is adopted to calculate the impact kinetic energy of hydromechanical calendering, and the simulation model of hydromechanical calendering is built based on AMESim. The characteristics of hydromechanical calendering are analyzed. The oil pressure is controlled by setting the pressure adjusting valve and adjusting the spring pressure to realize the optimization of the hydromechanical calendering process. On this basis, the grinding tool is designed, and the clearance between punch and die is reserved to prevent the blank from cracking. The experimental results show that the error probability of hydromechanical calendering with the optimized design process is significantly lower than that of the control group, which can solve the problem of high error probability of traditional hydromechanical calendering process in practical application and meet the application requirements. Conclusions. Hydraulic mechanical calendering may be used to calendar different plastic materials, with a blank thickness of 0.5-3mm, according to the technologys operating principle, test production, and foreign data. Thicker sheets may be calendered with adequate hydraulic pressure. For the form of parts, the optimum effect is that the projectile, cone, etc. section vary from small too big. For the section does not change or smaller parts also have an impact? Some pieces with an interior concave shape and bottom and side stiffeners are difficult to the calendar. Hydromechanical calendering technologies make it easier to create these pieces.
Fang Ren, Haiyan Xiu, Chuxin Ji, Dong Zheng, Ziyi Wu
Published: 3 October 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-9; https://doi.org/10.1155/2022/3150714

Abstract:
In this paper, we construct a new code-based linkable threshold signature scheme whose security is based on the hardness of the syndrome decoding problem and general syndrome decoding problem. We show that our scheme is secure in terms of existential unforgeability, anonymity, nonslanderability, and linkability. The complexity of the signature size proposed in this paper is $O\left(t\right)$ . Our method is particularly well suited for large free group voting systems. The greater the number of members in the ring, the more pronounced the advantage of the signature length is when compared to other schemes. That is, our scheme achieves a fixed length signature, independent of the number of ring members. In addition, our scheme has a very short public key, and the size is $O\left(N\right)$ .
Haitao Chen, , , Jiao Zhang, Yan Liu, Xiaoqian Pan, Xingguang Liu, Jibo Wei
Published: 1 October 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/9446418

Abstract:
This paper investigates a deep reinforcement learning algorithm based on dueling deep recurrent $Q$ -network (Dueling DRQN) for dynamic multichannel access in heterogeneous wireless networks. Specifically, we consider the scenario that multiple heterogeneous users with different MAC protocols share multiple independent channels. The goal of the intelligent node is to learn a channel access strategy that achieves high throughput by making full use of the underutilized channels. Two key challenges for the intelligent node are (i) there is no prior knowledge of spectrum environment or the other nodes’ behaviors; (ii) the spectrum environment is partially observable, and the spectrum states have complex temporal dynamics. In order to overcome the aforementioned challenges, we first embed the long short-term memory layer (LSTM) into the deep $Q$ -network (DQN) to aggregate historical observations and capture the underlying temporal feature in the heterogeneous networks. And second, we employ the dueling architecture to overcome the observability problem of dynamic environment in neural networks. Simulation results show that our approach can learn the optimal access policy in various heterogeneous networks and outperforms the state-of-the-art policies.
Xingguang Liu, , Xiaoying Zhang, Xiang Tan, Jibo Wei
Published: 30 September 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-11; https://doi.org/10.1155/2022/7410708

Abstract:
With the emergence of a large number of smart devices, the radio environment in which unmanned aerial vehicles (UAVs) take tasks is becoming more and more complex, which puts forward higher requirements for UAVs’ situational awareness and autonomous obstacle avoidance capabilities. To tackle this issue, we propose a three-dimension (3D) UAV path planning method under communication connectivity constraints guided by radio environment maps (REMs), which are distributed by ground edge servers in the form of compressed global REMs and detailed local REMs. An interfered fluid dynamic system (IFDS) model is deployed on UAVs to allow them to avoid obstacles and plan paths. We propose a twin-delayed deep deterministic policy gradient- (TD3-) based deep reinforcement learning (DRL) method to optimize the reaction coefficients of UAVs to avoid obstacles and improve the signal to interference plus noise ratio (SINR). The simulation results show that the proposed algorithm can effectively avoid static obstacles and dynamic interference under communication connectivity constraints, significantly improve the communication stability with a higher receive signal SINR and reduce the cost of UAV performing tasks with the shortest path.
Tianbo Sun, Tong Meng, Yutong Liu
Published: 30 September 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-9; https://doi.org/10.1155/2022/9910471

Abstract:
The pathologist’s diagnosis is crucial in identifying and categorizing pathological cancer sections, as well as in the physician’s subsequent evaluation of the patient’s condition and therapy. It is recognised as the “gold standard”; however, both objective and subjective pathological diagnoses have limits, such as tissue corruption resulting from the nonstandard collection of diseased tissue, nonstandard tissue fixation or delivery, or a lack of necessary clinical data. In addition, diagnostic pathology encompasses too much information; thus, it requires time and effort to grow a trained pathologist. Consequently, computer-assisted diagnosis has become an essential tool for replacing or assisting pathologists with computer technology and graphical development. In this regard, the CAMELYON 17 competition was designed to identify the best algorithm for detecting cancer metastases in the lymph. Each participant was given 899 whole-slide photos for the development of their algorithms. More than 300 people enrolled on the competition. CAMELYON 17 is primarily focused on the categorization of lymph node metastases. The TNM classification system is the primary classification system. Participants at CAMELYON 17 mostly use categorization and learning techniques in deep learning and machine learning. In order to get a better understanding of the top-selected algorithms, we examine the advantages and limitations of traditional machine learning and deep learning for classifying breast cancer metastases.
, Jiawei Lin, Wanlong Li, Yongpan Zou, Kaishun Wu
Published: 30 September 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/2134493

Abstract:
Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.
Xiaofeng Han
Published: 29 September 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-7; https://doi.org/10.1155/2022/9685652

Abstract:
The rapid development of artificial intelligence, “Internet +,” big data, 5G, and other technologies in the twenty-first century has not only brought great changes to the field of education but also brought unprecedented opportunities and challenges to the reform and innovation of mathematics teaching. The use of intelligent technology to carry out the reform of college mathematics teaching in depth and effectively improve the quality of teaching has become a hot topic discussed by the majority of mathematics teachers. This paper carefully analyzes and sorts out the problems existing in the current intelligent college mathematics teaching and systematically studies the related theories and design principles of the blended teaching mode. The ideas and approaches of the reform of college mathematics blended teaching supported by intelligent technology are deeply discussed from the aspects of improving the level of teachers, setting up a blended teaching team supported by intelligent technology, optimizing the informatization construction of teaching environment and establishing rich teaching resources, building an online and offline classroom teaching system, personalized learning under the background of microclass and cloud class, cross-university blended learning in the network environment, and mathematics precision teaching evaluation and optimization using big data.
Jianming Dong
Published: 29 September 2022
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/9938813

Abstract: