Mobile Computing for IoT

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 12584

Special Issue Editors


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Guest Editor
Digital Innovation Lab, Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: Internet of Things; big data; cloud computing; mobile computing; data mining
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Digital Innovation Lab, Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: data science; digital health; machine learning; cloud computing; big data analytics

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Guest Editor
School of Info Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: IoT; middleware platforms; software engineering; scalable architecture; query language
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) and its interplay with mobile computing is growing fast and will power a plethora of future applications in domains, such as manufacturing, healthcare, agriculture, and smart cities, among others. Thanks to advancements in hardware and software technologies, such as the introduction of dedicated onboard graphics processing units (e.g., Google Pixel with Qualcomm Adreno 530 GPU), advanced machine learning and deep learning algorithms on mobile devices (enabling federated learning), and access to contextualised information from IoT sensors, the computing capability of multisensory smart mobile devices with internet connectivity has dramatically improved. IoT devices (e.g., wearables, RFID tags, energy monitors, environmental sensors) located in the proximity of mobile users can improve overall user experience, perception, and knowledge and help to make better decisions. An increasing number of research works are exploring the interplay between mobile computing and IoT to enable the facilitation and development of more fine-grained, personalised, and enriched services.

While mobile computing for IoT is of enormous potential, it also faces several challenges. These include (1) a lack of optimal solutions for performing computationally expensive operations and data integration from various IoT devices and sensors on mobile phones, (2) handling issues regarding IoT device heterogeneity and real-time sensing to build fault-tolerant solutions and models, (3) addressing security and privacy concerns when integrating data from IoT mobile computing for sensitive applications, (4) handling uncertainty in data and context, and (5) a lack of scalable architectures to support distributed learning among IoT devices, mobile devices, edge, and cloud.

This Special Issue aims to bring together researchers and application developers working on the intersection of IoT and mobile computing developing next-generation applications, algorithms, frameworks, and solutions to support the growth of IoT data analytics using mobile devices. We also welcome high-quality research, work in progress, quality review articles, real-world experiments, and deployment use case papers that address the challenges and gaps in the current state of the art in the aforementioned areas.

Potential research topics of interest include (but are not necessarily limited to these):

  • Smart IoT sensors (e.g., wearables, smart devices) for IoT mobile computing;
  • Architectures, protocols, frameworks and applications of IoT mobile computing;
  • Machine learning and deep learning for IoT mobile computing;
  • Distributed learning for IoT mobile computing;
  • Methods and techniques to support real-time data analytics for IoT mobile computing;
  • IoT mobile crowd-sensing;
  • Middleware platforms for IoT mobile computing;
  • Social IoT computing
  • Security and privacy for IoT in mobile computing;

Application use-cases reporting outcomes of real-world case studies that demonstrate IoT Mobile Computing include but are not limited to:

  • Digital health;
  • Industry 4.0;
  • Digital agriculture;
  • Smart cities.

Dr. Prem Prakash Jayaraman
Dr. Abdur Rahim Mohammad Forkan
Dr. Ali Hassani
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. IoT is an international peer-reviewed open access quarterly 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 1200 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.

Published Papers (2 papers)

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Research

13 pages, 2543 KiB  
Article
TSCH Evaluation under Heterogeneous Mobile Scenarios
by Charalampos Orfanidis, Atis Elsts, Paul Pop and Xenofon Fafoutis
IoT 2021, 2(4), 656-668; https://doi.org/10.3390/iot2040033 - 22 Oct 2021
Cited by 8 | Viewed by 3365
Abstract
Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be [...] Read more.
Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be tailored to the requirements of specific applications. Several performance aspects of TSCH have been investigated so far, such as the energy consumption, reliability, scalability and many more. However, mobility in TSCH networks remains an aspect that has not been thoroughly explored. In this paper, we examine how TSCH performs under mobility situations. We define two mobile scenarios: one where autonomous agriculture vehicles move on a predefined trail, and a warehouse logistics scenario, where autonomous robots/vehicles and workers move randomly. We examine how different TSCH scheduling approaches perform on these mobility patterns and when a different number of nodes are operating. The results show that the current TSCH scheduling approaches are not able to handle mobile scenarios efficiently. Moreover, the results provide insights on how TSCH scheduling can be improved for mobile applications. Full article
(This article belongs to the Special Issue Mobile Computing for IoT)
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22 pages, 7012 KiB  
Article
An IoT-Based Mobile System for Safety Monitoring of Lone Workers
by Pietro Battistoni, Monica Sebillo and Giuliana Vitiello
IoT 2021, 2(3), 476-497; https://doi.org/10.3390/iot2030024 - 3 Aug 2021
Cited by 11 | Viewed by 6831
Abstract
The European Agency for Safety and Health at Work considers Smart Personal Protective Equipment as “Intelligent Protection For The Future”. It mainly consists of electronic components that collect data about their use, the workers who wear them, and the working environment. This paper [...] Read more.
The European Agency for Safety and Health at Work considers Smart Personal Protective Equipment as “Intelligent Protection For The Future”. It mainly consists of electronic components that collect data about their use, the workers who wear them, and the working environment. This paper proposes a distributed solution of Smart Personal Protective Equipment for the safety monitoring of Lone Workers by adopting low-cost electronic devices. In addition to the same hazards as anyone else, Lone Workers need additional and specific systems due to the higher risk they run on a work site. To this end, the Edge-Computing paradigm can be adopted to deploy an architecture embedding wearable devices, which alerts safety managers when workers do not wear the prescribed Personal Protective Equipment and supports a fast rescue when a worker seeks help or an accidental fall is automatically detected. The proposed system is a work-in-progress which provides an architecture design to accommodate different requirements, namely the deployment difficulties at temporary and large working sites, the maintenance and connectivity recurring cost issues, the respect for the workers’ privacy, and the simplicity of use for workers and their supervisors. Full article
(This article belongs to the Special Issue Mobile Computing for IoT)
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