Wireless Communications and Mobile Computing

Journal Information
ISSN / EISSN : 1530-8669 / 1530-8677
Published by: Hindawi Limited (10.1155)
Total articles ≅ 4,528
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, , Olaide N. Oyelade, Mubarak S. Almutairi
Wireless Communications and Mobile Computing, Volume 2022, pp 1-22; https://doi.org/10.1155/2022/9307961

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.
Gang Liu, Zhaobin Liu, Victor S. Sheng, Liang Zhang, Yuanfeng Yang
Wireless Communications and Mobile Computing, Volume 2022, pp 1-25; https://doi.org/10.1155/2022/3450361

In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy-efficient, static scenario-oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.
Min Zhu
Wireless Communications and Mobile Computing, Volume 2022, pp 1-11; https://doi.org/10.1155/2022/1368841

This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.
Jiangdong Lu, Dongfang Li, Penglong Wang, Fen Zheng, Meng Wang
Wireless Communications and Mobile Computing, Volume 2022, pp 1-8; https://doi.org/10.1155/2022/8374473

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.
Qiao Chen, Shihong Liu
Wireless Communications and Mobile Computing, Volume 2022, pp 1-13; https://doi.org/10.1155/2022/7900467

Sports can cause the consumption of energy materials in the body. The rational use of nutritional supplements can maintain the homeostasis of the organism, which plays a very important role in improving the competitive performance of sports athletes. The purpose of this study is to explore the effect of nutritional supplements on basketball sports fatigue. The method of this study is as follows: first of all, 15 basketball players in our city were selected as the experimental objects, and they were randomly divided into the experimental group and the control group. The members of the experimental group took nutrients. After the training, 6 days a week, 3 hours in the morning and 3 hours in the afternoon, and the rest was adjusted on Sunday. Before training, four weeks and eight weeks of training, the blood routine indexes and body functions of athletes were tested. The results showed that the number of red blood cells, hemoglobin concentration, and average hemoglobin concentration of ligustilide supplement of the athletes were at the level of 0.05 after 4 weeks and 8 weeks, and the difference was significant (). The nutritional supplements were used in sprint (3.4 s less), long-distance running (12.8 s less), and weight lifting (6.2 kg more) to a certain extent. Nutritional supplements are used as an auxiliary means of diet to supplement the amino acids, trace elements, vitamins, minerals, etc. required by the human body. The conclusion is that nutrition supplement can effectively improve the indexes of athletes’ body in about four weeks, but the effect is not obvious after a long time. This study provides a certain method for the research of nutritional supplements in the field of sports.
, Shi Liu, Zhenfeng Li, , Di Wu, Yuanyuan Wu,
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/8476000

A two-stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two-channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen-2 and WorldView-2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.
Shuo Zhou
Wireless Communications and Mobile Computing, Volume 2022, pp 1-7; https://doi.org/10.1155/2022/2194373

Mobile learning has become an efficient way to meet the needs of work learning in the epidemic situation because of its convenience, flexibility, and freedom. This paper studies and discusses the impact of mobile learning on learning education and preschool education in the epidemic. A mobile learning community resource sharing algorithm is used to explore the speed of the online learning community to obtain learning resources. The advantages of online learning are analyzed by comparing the speed of learning resources obtained in ordinary groups. In this research, the random offloading algorithm (ROA) is proposed to analyze the student response. The results revealed that majority of the students believed that mobile learning helps in learning subjects to a greater extent.
Jianhua Dai, Jingxin Xu
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/4867220

Mobile Internet-based intelligent media has become a popular academic topic. This study uses the CiteSpace visualisation tool and Scientific Citation Index Expanded database to comb the existing research in the field of intelligent media from a quantitative perspective. A total of 7248 English papers were published on the topic of “intelligent media” from 2012 to 2021, and 145 highly cited papers refined were analysed. Scientific knowledge graphs were analysed from six dimensions: annual publication quantity, country of publication, institution of publication, author, keywords, and cited references. In the last 10 years, the research literature on intelligent media has been found to increase annually. Presently, the People’s Republic of China and the United States of America have a high proportion of documents in this field. Chinese universities and institutions have achieved significantly in terms of the quantity and quality of documents. From the perspective of the whole intelligent media discipline, the high-yield author group has not been formed, and there is minimal cooperation amongst authors. Popular intelligent media topics include film, social media, machine learning, swarming motility, data mining, and artificial intelligence. Subject words of the main research directions are event recognition, fake news, Cable News Network model, reconfigurable intelligent surface, comprehensive survey, microblog message, strain sensor, and traffic event. Combined with popular topics and time zone maps, the future research frontier in the field of smart media is identified.
Peng Wang, , Jianpei Zhang
Wireless Communications and Mobile Computing, Volume 2022, pp 1-13; https://doi.org/10.1155/2022/6719514

Unlike outdoor trajectory prediction that has been studied many years, predicting the movement of a large number of users in indoor space like shopping mall has just been a hot and challenging issue due to the ubiquitous emerging of mobile devices and free Wi-Fi services in shopping centers in recent years. Aimed at solving the indoor trajectory prediction problem, in this paper, a hybrid method based on Hidden Markov approach is proposed. The proposed approach clusters Wi-Fi access points according to their similarities first; then, a frequent subtrajectory based HMM which captures the moving patterns of users has been investigated. In addition, we assume that a customer’s visiting history has certain patterns; thus, we integrate trajectory prediction with shop category prediction into a unified framework which further improves the predicting ability. Comprehensive performance evaluation using a large-scale real dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large shopping center in Sydney was conducted; the experimental results show that the proposed method outperforms the traditional HMM and perform well enough to be usable in practice.
Chuanqi Ma
Wireless Communications and Mobile Computing, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/1275508

Aerobic exercise is a very popular form of exercise. It combines various forms of sports and music. Aerobic exercise improves muscle tone and relaxes the mind and body while burning calories. It is designed to individualize instruction for different audiences. It is an important factor in the applicability of the operation. The purpose of this paper is to build different human models based on sensor network numbers to quantify different movements through the Internet of Things (IoT) to design personalized curriculum design and practice to improve the popularity of creative aerobics curriculum. In this paper, we first give an overview of the algorithm and data fusion algorithm and then simulate the aerobics creative curriculum design. First, the variance is used as the error measure to establish the data fusion algorithm and aerobics new concept innovation curriculum design and practice. The established model is compared with the aerobics curriculum design under the traditional model to highlight the advantages of the curriculum design under the data fusion algorithm. A comparison is also made with examples. The experimental results show that the data of the audience’s movement changes during different creative processes solve the aerobics creative editing problem. Compared with the traditional curriculum design, the efficiency of the curriculum design and practice is improved by 20.23%.
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