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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 15624

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Special Issue Editors

Dr. Sicong Liu

E-Mail Website
Guest Editor

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: edge intelligence; ubiquitous computing; mobile computing; cloud-edge-end collaborative deep computing; IoT systems; deep learning; deep model compression; optimization methods; machine learning

Prof. Dr. Lei Xie

E-Mail Website
Guest Editor

Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China
Interests: wireless sensing; multimodal sensing; wearable computing; edge computing

Special Issue Information

Dear Colleagues,

Due to improvements in their CPU/GPU capabilities, intelligent mobile devices can now undertake more computing tasks. This computational mechanism is called pervasive and mobile computing. The proliferation of pervasive and mobile computing in intelligent mobile devices is now regarded as an effective mechanism able to assist or change human life. Ubiquitous computing through the design and deployment of various pervasive and mobile computing systems is able to sufficiently perceive various states of humans and things, such as human activities, decision assistance, and health care. It utilizes the computation power of intelligent mobile devices to improve the quality of people's work and life, proving its potential for pervasive and mobile computing. Ideally, computing services will be efficient, reliable, and ubiquitous. However, in reality, the implementation of adaptive and reliable mobile computing systems is still challenging. Maintaining robustness under dynamic network conditions, adapting to dynamic and limited resource constraints, rational energy schedules, protecting data privacy, etc., are still open problems. This Special Issue provides an opportunity for researchers and system developers to address research challenges facing the design, development, deployment, use, and fundamental limits of pervasive and mobile computing systems. We submissions from various fields related to pervasive and mobile computing, wireless communication and networking, embedded systems and hardware, learning algorithms and models, distributed systems and algorithms, data management, and real-world measurement and deployment for mobile systems and applications. Papers on theoretical, practical, and methodological issues in pervasive and mobile computing are welcome.

Potential topics include, but are not limited to:

Applications of machine learning to mobile/wireless research;
Mobile health;
Edge computing and resource scheduling based on artificial intelligence algorithms;
Security and privacy of embedded intelligent systems;
Ubiquitous computing and mobile human–computer interaction;
Mobile web, video, virtual reality, and other applications;
System smart spaces (e.g., smart factories, smart workspace, smart agriculture);
Robotic and drone-based networking;
Implanted and wearable computing;
Embedded intelligent operating systems and operating environments;
Embedded intelligence based on sensors, UAVs, and robots;
Intelligent algorithms for the Internet of Things and the physical space of information;
Modeling, simulation, and evaluation tools for embedded intelligent applications;
Experiences, challenges, and comparisons of embedded intelligence platforms;
Data sharing and management based on embedded intelligent algorithms;
New communication paradigms for ubiquitous connectivity;
Low-power wireless media access control, network, and transport protocol designs;
Resource-efficient machine learning for embedded and mobile platforms;
Heterogeneous collaborative sensing, including human–robot sensor systems;
Fault tolerance, dependability, and verification;
Applications and deployment experiences;
Applications of embedded intelligence in health, transportation, smart city, intelligent manufacturing, and other fields;
Security and privacy of embedded intelligent systems;
Embedded intelligent application networks, system architecture, and protocol;
Modeling, simulation, and evaluation tools for embedded smart applications.

Dr. Sicong Liu
Prof. Dr. Lei Xie
Guest Editors

Manuscript Submission Information

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Keywords

pervasive and mobile computing
wireless communication and networking
embedded systems and hardware
learning algorithms and models
distributed systems and algorithms
data management
real-world measurement and deployment for mobile systems

Published Papers (10 papers)

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Research

20 pages, 1367 KiB

Open AccessArticle

Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing

by Ahmed S. Alfakeeh and Muhammad Awais Javed

Appl. Sci. 2023, 13(24), 13034; https://doi.org/10.3390/app132413034 - 06 Dec 2023

Viewed by 669

Abstract

Fog computing is a key component of future intelligent transportation systems (ITSs) that can support the high computation and large storage requirements needed for autonomous driving applications. A major challenge in such fog-enabled ITS networks is the design of algorithms that can reduce the computation times of different tasks by efficiently utilizing available computational resources. In this paper, we propose a data-enabled cooperative technique that offloads some parts of a task to the nearest fog roadside unit (RSU), depending on the current channel quality indicator (CQI). The rest of the task is offloaded to a nearby cooperative computing vehicle with available computing resources. We developed a cooperative computing vehicle selection technique using an artificial neural network (ANN)-based prediction model that predicts both the computing availability once the task is offloaded to the potential computing vehicle and the link connectivity when the task result is to be transmitted back to the source vehicle. Using detailed simulation results in MATLAB 2020a software, we show the accuracy of our proposed prediction model. Furthermore, we also show that the proposed technique reduces total task delay by 37% compared to other techniques reported in the literature. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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20 pages, 2485 KiB

Open AccessArticle

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

by Yang Liu, Yong Li, Wei Cheng, Weiguang Wang and Junhua Yang

Appl. Sci. 2023, 13(10), 6040; https://doi.org/10.3390/app13106040 - 14 May 2023

Cited by 2 | Viewed by 1203

Abstract

Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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23 pages, 3441 KiB

Open AccessArticle

Multilayer Measurement Methodology with Open-Source Tools for Evaluating IEEE 802.11ad Communication Quality

by Shigeru Kashihara, Morihiko Tamai, Akio Hasegawa and Hiroyuki Yokoyama

Appl. Sci. 2023, 13(9), 5378; https://doi.org/10.3390/app13095378 - 25 Apr 2023

Viewed by 1098

Abstract

IEEE 802.11ad is the practical networking technology widely used as a 60 GHz millimeter-wave communication method ahead of Beyond 5G and 6G networks. However, compared with the existing Wi-Fi systems that use 2.4/5 GHz, it is difficult to manage the communication quality due to the high carrier frequency, e.g., the signal strength suddenly and significantly degrades due to various factors such as distance and obstacles. For IEEE 802.11ad to be widely adopted, a simple and inexpensive measurement method is needed for non-specialists, including end users, to build and manage a stable IEEE 802.11ad network. In addition, easy-to-understand performance indicators are required. This paper then proposes a multilayer measurement methodology with open-source tools, i.e., iperf3, ping, and iw, for IEEE 802.11ad communication. Our methodology selects indicators necessary to evaluate IEEE 802.11ad communication performance. The tools iperf3 and ping mainly evaluate the IP layer indicators such as throughput, jitter, packet retries, packet loss, and RTT. On the other hand, the RSSI and Tx bitrates of the iw results are used to investigate the wireless link quality. Through empirical results with our measurement methodology in real environments, we show that in addition to traditional indicators such as through-put, the Tx bitrate output by iw can be a new indicator for understanding the IEEE 802.11ad communication quality. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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18 pages, 3925 KiB

Open AccessArticle

A Time-Series Model for Varying Worker Ability in Heterogeneous Distributed Computing Systems

by Daejin Kim, Suji Lee and Hohyun Jung

Appl. Sci. 2023, 13(8), 4993; https://doi.org/10.3390/app13084993 - 16 Apr 2023

Cited by 1 | Viewed by 853

Abstract

In this paper, we consider the problem of estimating the time-dependent ability of workers participating in distributed matrix-vector multiplication over heterogeneous clusters. Specifically, we model the workers’ ability as a latent variable and introduce a log-normally distributed working rate as a function of the latent variable with parameters so that the working rate increases as the latent ability of workers increases, and takes positive values only. This modeling is motivated by the need to reflect the impact of time-dependent external factors on the workers’ performance. We estimate the latent variable and parameters using the expectation-maximization (EM) algorithm combined with the particle method. The proposed estimation and inference on the working rates are used to allocate tasks to the workers to reduce expected latency. From simulations, we observe that our estimation and inference on the working rates are effective in reducing expected latency. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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17 pages, 8392 KiB

Open AccessArticle

Wi-NN: Human Gesture Recognition System Based on Weighted KNN

by Yajun Zhang, Bo Yuan, Zhixiong Yang, Zijian Li and Xu Liu

Appl. Sci. 2023, 13(6), 3743; https://doi.org/10.3390/app13063743 - 15 Mar 2023

Cited by 1 | Viewed by 947

Abstract

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional device. In this case, channel state information (CSI) extracted from Wi-Fi signal is used to capture the action information of the human body via CSI. After pre-processing to reduce the interference of environmental noise as much as possible, clear action information is extracted using the feature extraction method based on time domain to obtain the gesture action feature data. The gathered data are integrated with the weighted k-nearest neighbor (KNN) classification recognizer for classification task. The experiment outcomes revealed that the accuracy scores of the same gesture for different users and different gestures for the same user under the same environment were 93.1% and 89.6%, respectively. The experiments in different environments also achieved good recognition results, and by comparing with other experimental methods, the experiments in this paper have better recognition results. Evidently, good classification results were generated after the original data were processed and incorporated into the weighted KNN. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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17 pages, 3162 KiB

Open AccessArticle

Visual Exploration of Cycling Semantics with GPS Trajectory Data

by Xuansu Gao, Chengwu Liao, Chao Chen and Ruiyuan Li

Appl. Sci. 2023, 13(4), 2748; https://doi.org/10.3390/app13042748 - 20 Feb 2023

Cited by 1 | Viewed by 1732

Abstract

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. In this paper, based on large-scale GPS trajectories and road network data, we mainly explore cycling semantics from two perspectives. On one hand, from the perspective of the cyclists, trajectories could tell their frequently visited sequences of streets, thus potentially revealing their hidden cycling themes, i.e., cyclist behavior semantics. On the other hand, from the perspective of the road segments, trajectories could show the cyclists’ fine-grained moving features along roads, thus probably uncovering the moving semantics on roads. However, the extraction and understanding of such cycling semantics are nontrivial, since most of the trajectories are raw data and it is also difficult to aggregate the dynamic moving features from trajectories into static road segments. To this end, we establish a new visual analytic system called VizCycSemantics for pervasive computing, in which a topic model (i.e., LDA) is used to extract the topics of cyclist behavior semantics and moving semantics on roads, and a clustering method (i.e., k-means ++) is used to further capture the groups of similar cyclists and road segments within the city; finally, multiple interactive visual interfaces are implemented to facilitate the interpretation for analysts. We conduct extensive case studies in the city of Beijing to demonstrate the effectiveness and practicability of our system and also obtain various insightful findings and pieces of advice. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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19 pages, 3832 KiB

Open AccessArticle

EEDLABA: Energy-Efficient Distance- and Link-Aware Body Area Routing Protocol Based on Clustering Mechanism for Wireless Body Sensor Network

by Khalid Zaman, Zhaoyun Sun, Altaf Hussain, Tariq Hussain, Farhad Ali, Sayyed Mudassar Shah and Haseeb Ur Rahman

Appl. Sci. 2023, 13(4), 2190; https://doi.org/10.3390/app13042190 - 08 Feb 2023

Cited by 10 | Viewed by 1913

Abstract

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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20 pages, 7282 KiB

Open AccessArticle

Practical Performance Analysis of Interference in DSS System

by Mingshuo Wei, Xiao Li, Weiliang Xie and Chunlei Hu

Appl. Sci. 2023, 13(3), 1233; https://doi.org/10.3390/app13031233 - 17 Jan 2023

Cited by 2 | Viewed by 1629

Abstract

The 5G network is developing rapidly. However, due to spectrum resource limitation, it is expected to use the 5G network to ensure high resource utilization and network efficiency, while keeping part of 4G in the same band for existing 4G users. Dynamic spectrum-sharing (DSS) technology enables 4G/5G wireless networks to coexist in scarce spectrum resources and dynamically allocates spectrum resources in the same band. 4G/5G DSS has been successfully commercialized in some countries such as Germany and Brazil. However, complex 4G/5G DSS networks will introduce intra-frequency interference in the inter-system, which will affect network performance. Therefore, we innovatively proposed two interference mitigation schemes: buffer setting and rate matching. Furthermore, we have verified the practical performance of both schemes in a commercial network for the first time to determine the feasibility of the schemes. From theory, simulation, and practical analysis, both schemes can effectively mitigate the interference of the inter-system introduced by DSS: increasing the network rate by 60% in the interference environment and improving the user experience in the DSS architecture. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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23 pages, 7205 KiB

Open AccessArticle

mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

by Zhanjun Hao, Ruidong Wang, Xiaochao Dang, Hao Yan and Jianxiang Peng

Appl. Sci. 2022, 12(23), 12085; https://doi.org/10.3390/app122312085 - 25 Nov 2022

Cited by 4 | Viewed by 2149

Abstract

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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15 pages, 4511 KiB

Open AccessArticle

Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS

by Zhanjun Hao, Guowei Wang and Xiaochao Dang

Appl. Sci. 2022, 12(22), 11809; https://doi.org/10.3390/app122211809 - 21 Nov 2022

Cited by 3 | Viewed by 1685

Abstract

Casualties caused by people trapped in cars have been common in recent years. Despite a variety of solutions, complex detection devices need to be arranged, or privacy is poor. Since device-free Wi-Fi sensing has attracted much attention due to its simplicity, low cost, and no need for additional hardware, this paper proposes a contactless wireless Wi-Fi sensing-based method for detecting people left in cars: Car-Sense. The method uses ESP32 devices in the vehicle to build a wireless Wi-Fi network for low-cost, real-time, and accurate personnel awareness. By capturing and analyzing the CSI (Channel State Information) signal, extracting features, and building a machine-learning correlation model, the number and location of occupants can be estimated and further inferred in combination with sensing data such as vehicle temperature. Even better, with the computing power of the edge-side devices to process data in collaboration with the cloud, the computing process is partially localized to reduce the computing pressure and latency in the cloud. The approach has been experimentally verified to have more than 85% accuracy. Full article

(This article belongs to the Special Issue New Insights into Pervasive and Mobile Computing)

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