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Applied Sciences
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Mobile Computing and Intelligent Sensing, 2nd Edition
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Mobile Computing and Intelligent Sensing, 2nd Edition

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1386

Special Issue Editors

Dr. Han Ding

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: AIOT; smart sensing; human-centric applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Xi

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: Internet of things; artificial intelligence; network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in mobile computing and intelligent sensing technologies present opportunities to extend a wide variety of applications, for example, location estimation, context sensing, virtual or augmented reality, healthcare, human–mobile interactions, vehicular and mobile robotic systems, and so on. This Special Issue aims to collect high-quality and innovative research on all aspects of mobile computing, applications, and services.

Topics of interest include, but are not limited to, the following:

  • Mobile/pervasive/ubiquitous/wearable computing;
  • New platforms and communication paradigms for networked sensor systems;
  • Communication media (6G, millimeter wave, UWB, ultrasound, RFID, NFC);
  • IoT systems and applications in Smart Cities;
  • Systems for location estimation and context sensing;
  • Learning algorithms and models for perception, understanding, and adaptation;
  • Novel mobile applications using machine learning;
  • Security and privacy in mobile applications and systems.

Dr. Han Ding
Prof. Dr. Wei Xi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pervasive computing
  • signal processing for mobile systems
  • communication paradigm and hardware design
  • intelligence systems with machine/deep learning
  • security and privacy in mobile systems
  • IoT systems and applications

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Related Special Issue

Published Papers (2 papers)

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Research

68 pages, 8733 KB  
Article
Towards Privacy-Preserving Deep Learning for Intelligent IoT Botnet Detection
by Ariwan M. Rasool, Nader Sohrabi Safa and Consolee Mbarushimana
Appl. Sci. 2026, 16(3), 1665; https://doi.org/10.3390/app16031665 - 6 Feb 2026
Viewed by 527
Abstract
Internet of Things (IoT) botnets are networks of infected smart devices controlled by attackers and posing a serious cybersecurity challenge. Developing detection approaches that maintain high accuracy while protecting privacy presents considerable challenges, particularly in large and heterogeneous IoT networks. This paper empirically [...] Read more.
Internet of Things (IoT) botnets are networks of infected smart devices controlled by attackers and posing a serious cybersecurity challenge. Developing detection approaches that maintain high accuracy while protecting privacy presents considerable challenges, particularly in large and heterogeneous IoT networks. This paper empirically compares three modelling approaches on Bot-IoT and N-BaIoT in binary and multiclass settings: handcrafted machine learning with random forest (RF), centralised deep learning (CDL) with DNN/LSTM/BiLSTM, and federated deep learning (FDL) with the same architectures. Model hyperparameters are selected via randomised search on stratified subsets and then fixed for final training. Results show near-perfect performance for all approaches in binary detection: on Bot-IoT, CDL-DNN attains perfect accuracy, and RF is virtually perfect (only four benign-to-attack false positives), while FDL models are similarly strong with only small false-positive and false-negative counts. On N-BaIoT, RF and CDL (especially LSTM) are near-perfect, and FDL is very close to CDL. For multiclass detection, CDL-DNN leads on Bot-IoT, RF remains near perfect with minimal cross-class confusion, and FDL trails slightly; on N-BaIoT, FDL-BiLSTM and RF are essentially perfect, with CDL-LSTM close behind. Overall, the findings validate RF as a competitive classical approach, show where centralised representation learning adds value, and demonstrate that federated training preserves most of the centralised accuracy while avoiding raw data centralization (data locality) for scalable deployment. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing, 2nd Edition)
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26 pages, 1921 KB  
Article
Research on Dependency-Aware Service Migration Strategy in the Internet of Vehicles Integrating a Graph Attention Network and Deep Reinforcement Learning
by Ying Liu, Zhaofu Liu and Yu Yao
Appl. Sci. 2026, 16(2), 943; https://doi.org/10.3390/app16020943 - 16 Jan 2026
Viewed by 326
Abstract
The integration of Mobile Edge Computing and container virtualization technologies provides crucial support for low-latency and highly resilient service deployment in Internet of Vehicles (IoV) applications. However, the high mobility of vehicles poses challenges to service continuity, necessitating dynamic adjustment of service deployment [...] Read more.
The integration of Mobile Edge Computing and container virtualization technologies provides crucial support for low-latency and highly resilient service deployment in Internet of Vehicles (IoV) applications. However, the high mobility of vehicles poses challenges to service continuity, necessitating dynamic adjustment of service deployment locations through container migration. Existing research predominantly focuses on independent service migration while overlooking the complex interdependencies among multiple subtasks in practical applications. In this paper, we investigate the container migration problem for dependency-aware services in IoV environments. We first formulate the problem as a dual-objective optimization problem centered on minimizing both the average service delay and system load imbalance. To address the complex dependencies among containers and the highly dynamic nature of IoV environments, we propose an intelligent migration algorithm named GADM that integrates Graph Attention Networks with Deep Reinforcement Learning. The GADM algorithm leverages Graph Attention Networks to capture critical paths in task dependencies, and combines this with an actor–critic-based Deep Reinforcement Learning framework to achieve adaptive decision-making in dynamic environments. Validation using real-world vehicle trajectory datasets and Alibaba cluster trace datasets demonstrates the effectiveness of the proposed algorithm. Experimental results indicate that compared to other methods, GADM significantly improves system load balancing while reducing average service latency. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing, 2nd Edition)
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