Mobile Edge Computing in Smart City

Dear Colleagues,

This Special Issue aims to bring together high-quality papers on the assimilation of mobile devices, edge computing, Internet of Things, cloud computing, Big Data, and deep learning into efficient smart city systems. More precisely, it will address models, methods, protocols, and standards of mobile edge computing as a global virual computer that integrates distributed physical objects with sensing, decision making, and actuating. Additionally, cooperation with cloud computing and load balancing beetween mobile devices and cloud hosts are crucial challenges regarding new communication technology G5.

The Internet of Things (IoT) connects a wide variety of devices, such as vehicles with built-in sensors (Internet of Vehicles), heart monitoring implants, biochip transponders, or field operation devices. In the context of smart cities, home sensors such as smart thermostats enable remote monitoring, and radio-frequency identification supports various objects and citizens in daily life. In addition, these sensors usually generate big data streams with high-value information that can aid decision-making. In consequence, artificial intelligence algorithms support smart city to prevent crisis situations, e.g., COVID-19. Furthermore, an efficient annual expenditure budget can be prepared to satisfy recognized community expectations regarding the current global crisis.

We welcome the submission of original research and review articles on models, algorithms, computer languages, databases, artificial intelligence, and software engineering in smart cities. We encourage academic researchers, computer scientists, security and privacy specialists, as well as industry experts to submit their research papers to this Special Issue. The submitted manuscripts should represent significant and novel research contributions.

Potential topics for submissions include but are not limited to:

• Edge cloud computing techniques—algorithms, software, and applications;
• Wireless sensor networks—architecture, protocols, and algorithms;
• Big Data—processing, communication, and data gathering;
• Internet of Things—architecture, algorithms, and development;
• Internet of Vehicles—models, protocols, applications;
• Computing clouds, grids, and supercomputers—design, software, platforms, algorithms;
• Artificial intelliegence in smart city systems;
• Machine learning, especially deep learning for mobile devices.

Prof. Dr. Jerzy Balicki
Dr. Vasos Vassiliou
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. Computers is an international peer-reviewed open access monthly 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 1800 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.

• Edge computing
• Smart city
• Computing clouds
• Artificial intelligence
• Deep learning
• Big Data
• Internet of Things