Emerging Ubiquitous Networking and Computing: Techniques, Standards, and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 15848

Special Issue Editors

College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
Interests: flying ad hoc networks; mobile social networks; vehicle ad hoc networks photo
School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
Interests: social computing; big data mining; pervasive computing
Special Issues, Collections and Topics in MDPI journals
Department of Information and Computer Science, Faculty of Science and Technology (Yagami-Campus), Keio University, Yokohama, Kanagawa 223-8522, Japan
Interests: social computing; big data analysis and processing; pervasive computing
Department of Electrical, Electronic and Information Engineering, Università di Bologna, 40136 Bologna, Italy
Interests: wireless communications and networking; satellite communications; mobile edge computing; fog computing; optimization techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

For the future 6G, ubiquitous networking and computing (UNC) technologies are the foundation to allow internet connectivity and real-time computing service everywhere. All types of devices can be involved in the scenario of emerging ubiquitous networking and computing, from satellites in space, through UAVs and airships in the sky, to vehicles on the road and sensors embedded in farming crops.

With such technologies, people and devices can be connected to the internet anywhere.   Meanwhile, with such wired or wireless connection, data can be easily transmitted between device and computing center in order to instantly enable computing and storage abilities.

This Special Issue is devoted to collecting novel and original articles related to techniques, standards, and applications in emerging ubiquitous networking and computing, aiming to realize the future 6G scenarios.

Prof. Dr. Liang Zhao
Dr. Junling Shi
Dr. Fei Hao
Dr. Yuan He
Dr. Daniele Tarchi
Guest Editors

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Keywords

  • mobile edge computing
  • fog computing
  • cloud computing
  • wireless sensor networks
  • data science for UNC
  • UAV-related UNC technologies and applications
  • space-related UNC technologies and applications

Published Papers (8 papers)

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Research

15 pages, 4256 KiB  
Article
DV-Hop Location Algorithm Based on RSSI Correction
by Wanli Zhang and Xiaoying Yang
Electronics 2023, 12(5), 1141; https://doi.org/10.3390/electronics12051141 - 26 Feb 2023
Cited by 2 | Viewed by 862
Abstract
To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and [...] Read more.
To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance. Full article
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11 pages, 3867 KiB  
Article
Modelling and Analysis of Adaptive Cruise Control System Based on Synchronization Theory of Petri Nets
by Qi Guo, Wangyang Yu, Fei Hao, Yuke Zhou and Yuan Liu
Electronics 2022, 11(21), 3632; https://doi.org/10.3390/electronics11213632 - 07 Nov 2022
Cited by 2 | Viewed by 1466
Abstract
The ACC (adaptive cruise control) system has developed rapidly in recent years, and its reliability and safety have also attracted a lot of attention. The ACC system can realize automatic driving following the vehicle in the longitudinal range, and its reliability is closely [...] Read more.
The ACC (adaptive cruise control) system has developed rapidly in recent years, and its reliability and safety have also attracted a lot of attention. The ACC system can realize automatic driving following the vehicle in the longitudinal range, and its reliability is closely related to the synchronization between two vehicles. Combined with formal modelling methods, this paper analyzes and detects the logical flaw that is poor synchronization in the following process of the ACC system from the perspective of synchronization. Aiming at avoiding this kind of logical flaw, this paper presents a novel optimized modelling solution based on the synchronization theory of Petri nets and further improves the calculation method of the synchronic distance. The simulation results reduce the token accumulation by an average of 91.357%, which demonstrates that the improved model can effectively improve reliability and reduce the risk of rear-end collision. Full article
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16 pages, 402 KiB  
Article
Computational Resources Allocation and Vehicular Application Offloading in VEC Networks
by Fan Gu, Xiaoying Yang, Xianwei Li and Haiquan Deng
Electronics 2022, 11(14), 2130; https://doi.org/10.3390/electronics11142130 - 07 Jul 2022
Cited by 4 | Viewed by 1413
Abstract
With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and [...] Read more.
With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and power-constrained vehicles may not meet the requirements of processing these vehicular applications. By offloading these vehicular applications to the edge cloud, vehicular edge computing (VEC) is deemed a novel paradigm for improving vehicular performance. However, how to optimize the allocation of computation resources of both vehicles and VEC servers to reduce the energy and delay is a challenging issue when deploying the VEC systems. In this article, we try to address this issue and propose a vehicular application offloading and computational resources allocation strategy. We formulate an optimization problem and present an efficient offloading scheme for vehicular applications. Extensive simulation results are offered to analyze the performances of the proposed scheme. In comparison with the benchmark schemes, the proposed scheme can outperform them in terms of computation cost. Full article
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18 pages, 1102 KiB  
Article
Aggregated Boolean Query Processing for Document Retrieval in Edge Computing
by Tao Qiu, Peiliang Xie, Xiufeng Xia, Chuanyu Zong and Xiaoxu Song
Electronics 2022, 11(12), 1908; https://doi.org/10.3390/electronics11121908 - 19 Jun 2022
Cited by 1 | Viewed by 1328
Abstract
Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms [...] Read more.
Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms may not be applicable since they mostly depend on a centralized retrieval method. To remedy this issue, this paper proposes a processing technique for aggregated Boolean queries in the context of edge computing, where each sub-region of the network corresponds to an edge network regulated by an edge server, and the Boolean queries are evaluated in a distributed fashion on the edge servers. This decentralized query processing technique has demonstrated its efficiency and applicability for the document retrieval problem. Experimental results on two real-world datasets show that this technique achieves high query performance and outperforms the traditional centralized methods by 2–3 times. Full article
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21 pages, 6689 KiB  
Article
Weather-Conscious Adaptive Modulation and Coding Scheme for Satellite-Related Ubiquitous Networking and Computing
by Shiqi Zhang, Guoxin Yu, Shanping Yu, Yanjun Zhang and Yan Zhang
Electronics 2022, 11(9), 1297; https://doi.org/10.3390/electronics11091297 - 19 Apr 2022
Viewed by 1784
Abstract
As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust [...] Read more.
As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust the modulation and coding types according to the varying channel condition. However, satellite-to-ground communication channels have the characterizations such as fast dynamic change, fast switching, and significant fading. These characterizations make it challenging to predict the channel state information accurately and, thus, to perform accurate AMC. For example, rain loss is one of the crucial factors in satellite-to-ground channel fading. In general, it is difficult to build an integrated global model for rain loss because it varies in different regions around the world. Moreover, for the emerging applications of multiple antennas on satellites, the conventional look-up table method cannot cope with the high-dimensional inputs of the multiple antennas. To tackle the above challenges, we propose an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground networks. The proposed method directly processes real-time global weather and location information in the environment and intelligently selects encoding schemes to maximize system throughput. Simulation results show that the proposed method can increase the total throughput. The total number of correctly transmitted bits per unit time is improved, and the efficiency of the satellite-to-ground communication is enhanced. Full article
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14 pages, 1090 KiB  
Article
Information Separation Network for Domain Adaptation Learning
by Zeqing Zhang, Zuodong Gao, Xiaofan Li, Cuihua Lee and Weiwei Lin
Electronics 2022, 11(8), 1254; https://doi.org/10.3390/electronics11081254 - 15 Apr 2022
Cited by 1 | Viewed by 1295
Abstract
The Bai People have left behind a wealth of ancient texts that record their splendid civilization, unfortunately fewer and fewer people can read these texts in the present time. Therefore, it is of great practical value to design a model that can automatically [...] Read more.
The Bai People have left behind a wealth of ancient texts that record their splendid civilization, unfortunately fewer and fewer people can read these texts in the present time. Therefore, it is of great practical value to design a model that can automatically recognize the Bai ancient (offset) texts. However, due to the expert knowledge involved in the annotation of ancient (offset) texts, and its limited scale, we consider that using handwritten Bai texts to help identify ancient (offset) Bai texts for handwritten Bai texts can be easily obtained and annotated. Essentially, this is a problem of domain adaptation, and some of the domain adaptation methods were transplanted to handle ancient (offset) Bai text recognition. Unfortunately, none of them succeeded in obtaining a high performance due to the fact that they do not solve the problem of how to separate the style and content information of an image. To address this, an information separation network (ISN) that can effectively separate content and style information and eventually classify with content features only, is proposed. Specifically, our network first divides the visual features into a style feature and a content feature by a separator, and ensures that the style feature contains only style and the content feature contains only content by cross-domain cross-reconstruction; thus, achieving the separation of style and content, and finally using only the content feature for classification. This greatly reduces the impact brought by cross-domain. The proposed method achieves leading results on five public datasets and a private one. Full article
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15 pages, 537 KiB  
Article
Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things
by Kang Tian, Haojun Chai, Yameng Liu and Boyang Liu
Electronics 2022, 11(6), 879; https://doi.org/10.3390/electronics11060879 - 10 Mar 2022
Cited by 11 | Viewed by 2105
Abstract
Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. [...] Read more.
Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm. Full article
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17 pages, 620 KiB  
Article
Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things
by Yong Wei, Huifeng Yang, Junqing Wang, Xi Chen, Jianqi Li, Sunxuan Zhang and Biyao Huang
Electronics 2022, 11(6), 839; https://doi.org/10.3390/electronics11060839 - 08 Mar 2022
Cited by 5 | Viewed by 1492
Abstract
With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of [...] Read more.
With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16. Full article
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