Wireless Communications and Mobile Computing
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Acceptance rate33%
Submission to final decision81 days
Acceptance to publication37 days
CiteScore4.300
Journal Citation Indicator0.390
Impact Factor2.336

Innovation Efficiency Evaluation of China’s High-Tech Industry considering Subindustry with a Parallel Slack-Based Measure Approach

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Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.

 Editor spotlight

Chief Editor Dr Cai is an Associate Professor in the Department of Computer Science at Georgia State University, USA and an Associate Director at INSPIRE Center.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Underwater Localization Using Differential Doppler Scale and TDOA Measurements with Clock Imperfection

Underwater acoustic localization is an important, yet challenging problem: (1) node mobility issue, (2) Doppler effect, and (3) clock imperfection. To be specific, underwater nodes are not stationary in real-life due to unpredictable currents. Relative motion between a transmitter and a receiver causes the time scaling problem on the received signals, where the time scaling factor is termed as Doppler scale. Then, due to the slow acoustic signal propagation speed, the underwater Doppler scale becomes more severe compared with the one in terrestrial environments. Thus, the differential Doppler scale (DDS) measurements should also be collected, other than the time measurements like time-difference-of-arrival (TDOA), for enhancing the underwater localization. Since DDS/TDOA measurements and clock skew are tightly coupled, clock synchronization is essential for accurate localization. However, due to the stringent cost and power constrains of underwater nodes, low-cost clocks with relative low precision are normally employed, which makes it even more difficult to guarantee a perfect clock synchronization between transmitter/receiver pairs. In order to cope with those issues, we propose an algebraic underwater localization method using the hybrid DDS/TDOA measurements, which is particularly robust against the node clock imperfection. A new DDS/TDOA measurement model with clock imperfection is first presented by analyzing the received signals over underwater acoustic channels. Then, we devise a two-step weighted least square-based estimator, and the analytical study shows that our estimator can achieve the Cramer-Rao lower bound (CRLB) accuracy under small noise. Simulations corroborate the theoretical results and the good performance of the proposed method.

Research Article

3D Localization for Mobile Node in Wireless Sensor Network

Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.

Review Article

Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets

Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time. Spreading this kind of data undermines societal cohesiveness and well by cultivating political division and doubt in government. Since of the sheer volume of news being disseminated through social media, human confirmation has ended up incomprehensible, driving to the improvement and arrangement of robotized strategies for the recognizable proof of wrong news. Fake news publishers use a variety of stylistic techniques to boost the popularity of their works, one of which is to arouse the readers’ emotions. Due to this, text analytics’ sentiment analysis, which determines the polarity and intensity of feelings conveyed in a text, is now being utilized in false news detection methods, as either the system’s foundation or as a supplementary component. This assessment analyzes the full explanation of false news identification. The study also emphasizes characteristics, features, taxonomy, different sorts of data in the news, categories of false news, and detection approaches for spotting fake news. This research recognized fake news using the probabilistic latent semantic analysis approach. In particular, the research describes the fundamental theory of the related work to provide a deep comparative analysis of various literature works that has contributed to this topic. Besides this, a comparison of different machine learning and deep learning techniques is done to assess the performance for fake news detection. For this purpose, three datasets have been used.

Research Article

An Old Photo Image Restoration Processing Based on Deep Neural Network Structure

Old photos retain precious historical image information, but today’s existing old photos often have varying degrees of damage. Although these old photos can be digitally processed and then restored, the restoration of old photos involves multiple areas of image restoration and has various types of degradation. Currently, there is no unified model for repairing multiple types of degradation of old photos. Photo restoration technology still has a lot of developments. Traditional image restoration technology mainly repairs the missing areas of the image based on mathematical formulas or thermal diffusion. This technology can only repair images with simple structures and small damaged areas and is difficult to apply in people’s daily lives. The emergence of deep learning technology has accelerated the pace of research on image restoration. This article will discuss the methods of repairing old photos based on deep neural networks. It is aimed at proposing an image restoration method based on deep neural network to enhance the effect of image restoration of old photos and provide more possibilities for restoration of old photos. This article discusses the background significance of image restoration methods, designs an image restoration model based on deep neural networks, and introduces the structure, principle, and loss function of the model. Finally, this article did a comparative experiment to compare the model in this article with other models and draw the conclusion: in the blur repair experiment, the algorithm in this paper is better than other algorithms for the peak signal-to-noise ratio and structure similarity of the repaired image; in the damage repair experiment, the value of the algorithm’ s peak signal-to-noise ratio is 32.34, and the structure similarity under different damage average levels is 0.767, which is also higher than other algorithms. Therefore, the model in this paper has the best effect on image restoration.

Research Article

UCB-Based Route and Power Selection Optimization for SDN-Enabled Industrial IoT in Smart Grid

As an essential building block for smart grid, the industrial internet of things (IIoT) plays a significant role in providing powerful sensing capability and ubiquitous connectivity for differentiated power services. The rapid development of smart grid imposes higher data monitoring and transmission requirements in terms of delay and energy efficiency. However, due to the severe electromagnetic interference (EMI) caused by massive electrical equipment, the transmission performance of IIoT becomes inferior. The traditional single-hop transmission mode evolves towards a multihop cooperation mode to satisfy differentiated quality of service (QoS) requirements. In this paper, we propose an upper confidence bound- (UCB-) based joint route and power selection optimization algorithm to support multihop cooperation mode evolution, which adopts a software-defined networking- (SDN-) enabled IIoT network framework to simplify network configuration and management. Compared with existing local-side-information-based route selection (LSI-RS) and random route selection (RRS) algorithms, simulation results demonstrate that the proposed algorithm has superior performances in total delay, energy efficiency, and utility.

Research Article

Digital Transformation of Enterprise Finance under Big Data and Cloud Computing

In today’s rapid development of technology, with the popularity and use of information technology networking, big data and cloud computing technology are gradually integrated into all walks of life. In this context, the financial management and control of enterprises have put forward higher requirements and brought greater challenges for enterprise finance. In the actual work, how to analyze the problems of enterprise financial management according to the actual situation of the enterprise and effectively apply big data and cloud computing technology to the work practice of enterprise financial control is a problem worth exploring.

Wireless Communications and Mobile Computing
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate33%
Submission to final decision81 days
Acceptance to publication37 days
CiteScore4.300
Journal Citation Indicator0.390
Impact Factor2.336
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.