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New Horizons and Applications for Big Data Analysis and Smart Computing

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 August 2025 | Viewed by 865

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, Tianjin University, Tianjin, China
Interests: cloud/edge computing; mobile computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud and edge computing, along with mobile computing, form the backbone of the Internet of Things (IoT), enabling the seamless connectivity and intelligence of smart devices. Cloud computing provides robust, scalable infrastructure that supports the massive data generated by IoT devices, offering centralized processing power and storage. Mobile computing complements this by offering portability and direct interaction with users through smartphones and tablets, allowing for real-time data access and control.

Edge computing pushes processing capabilities closer to the data source, reducing latency and bandwidth use by processing data locally. This is crucial for IoT applications that require immediate responses, such as autonomous vehicles and industrial automation. The synergy between cloud and edge computing allows for efficient data management, with edge computing handling real-time analytics and cloud computing providing long-term storage and complex processing.

The IoT thrives on this foundation, connecting billions of devices across various industries, from healthcare and agriculture to smart cities and home automation. These interconnected devices collect and share data, enabling advanced analytics, predictive maintenance, and energy management. The challenge lies in ensuring security, privacy, and interoperability across diverse devices and platforms.

In summary, the integration of cloud/edge computing with mobile computing and IoT technologies is driving innovation, enhancing efficiency, and transforming industries by harnessing the power of connected devices and data-driven insights.

Dr. Xiaobo Zhou
Guest Editor

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. 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

  • cloud/edge computing
  • mobile computing
  • Internet of Things

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Published Papers (1 paper)

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Research

22 pages, 5739 KiB  
Article
Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
by Thi-Thanh-An Nguyen, Yu-Heng Hsieh, Ching-Hsi Tseng, Yu-Chen Lin and Shyan-Ming Yuan
Appl. Sci. 2025, 15(6), 3193; https://doi.org/10.3390/app15063193 - 14 Mar 2025
Viewed by 515
Abstract
The sharing of DNA sequence data is essential for advancing medical technology and fostering innovation in healthcare. However, DNA sequences encode sensitive information, such as gender, physical attributes, and genetic predispositions, necessitating stringent privacy safeguards. Existing data-sharing frameworks often fail to adequately address [...] Read more.
The sharing of DNA sequence data is essential for advancing medical technology and fostering innovation in healthcare. However, DNA sequences encode sensitive information, such as gender, physical attributes, and genetic predispositions, necessitating stringent privacy safeguards. Existing data-sharing frameworks often fail to adequately address these privacy concerns. To overcome these challenges, this study proposes a blockchain-based, privacy-preserving ecosystem for DNA sequence sharing. The system employs a decentralized architecture to manage digital identities and access permissions, ensuring robust privacy and data security. Smart contract functionalities allow users to assign granular access controls to specific DNA sequence segments, enabling selective sharing with trusted recipients. Furthermore, research institutions are required to obtain certification and classification from governmental authorities, enhancing trust and system reliability. The user-centric design prioritizes privacy, security, and autonomy, simplifying operational processes and fostering user trust. By incentivizing DNA data sharing, the proposed model aims to accelerate medical advancements while maintaining stringent privacy protections, establishing a secure and scalable ecosystem for DNA sequence sharing. Experimental results from a prototype implementation indicate that the system achieves a throughput of up to 10–20 transactions per second for identity and access operations while incurring acceptable on-chain costs (≈1.3 million gas to deploy contracts and 400–800 k gas per user registration). These performance metrics underscore the feasibility and efficiency of the proposed approach. Full article
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