Announcements

Following from our two previous successful editions, we invite you to submit your abstracts and participate in the 3rd International Conference on AI Sensors and Transducers, taking place from 2 to 7 August 2026 in Jeju, South Korea.

Organized by MDPI and the open access journals Sensors, Micromachines, AI Sensors, Micro and Remote Sensing, this in-person conference will once again bring together experts and participating researchers who will share insights and innovations in sensors, sensing technology, transducers and artificial intelligence.

Start preparing your abstracts:
Don’t miss this opportunity to showcase your work to peers and leading experts in AI-enhanced sensing systems and transducers. We will be announcing the session topics at AIS 2026 soon.

Find out more about the instructions for authors: https://sciforum.net/event/AIS2026?section=#instructions.

Find out more about the publication opportunities available for authors: https://sciforum.net/event/AIS2026?section=#Publicationopportunities.
Please feel free to share the information about this conference to your colleagues and students.

We look forward to welcoming you in Jeju!

The organizing committee of the 3rd International Conference on AI Sensors and Transducers (AIS 2026).

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) is the flagship European machine learning and data mining conference, attracting a worldwide audience. The conference program includes presentations of peer-reviewed research, invited talks by leaders in the field, a wide program of workshops and tutorials, poster sessions, a discovery challenge, a demo track, an applied data science track, an industry track and a PhD forum.

Topics include the following:

• Advances in machine learning theory and algorithms, including deep learning, probabilistic modeling, and reinforcement learning;
• Knowledge discovery and data mining methodologies, covering pattern mining, graph analytics, and large-scale data processing;
• Trustworthy and responsible AI, with emphasis on fairness, interpretability, privacy, and robustness;
• Applied data science and domain-driven discovery, featuring real-world case studies in industry, health, sustainability, and the physical sciences;
• Emerging frontiers, such as foundation models, generative AI, multi-modal learning, and automated machine learning (AutoML).

MDPI will be attending the conference as an exhibitor; we welcome researchers from different backgrounds to visit and share their latest ideas with us.

The following MDPI journals will be represented:

• Data;
• MAKE;
• AI;
• Analytics;
• Remote Sensing;
• BDCC;
• Information;
• Computers;
• Knowledge;
• Future Internet;
• Technologies;
• JCP;
• Algorithms;
• Digital.

If you are planning to attend the above conference, we encourage you to visit our booth and speak to our representatives. We are eager to meet you in person and assist you with any queries that you may have. For more information about the conference, please visit the official website at https://ecmlpkdd.org/2026/.

We are delighted to announce and welcome you to the 1st International Online Conference on Algorithms and Computers (IOCAC 2027) which will take place from 28 to 30 June 2027, with Prof. Dr. Paolo Bellavista from the University of Bologna, Italy, and Prof. Dr. Frank Werner from Otto-von-Guericke-University Magdeburg, Germany, serving as chairs.

Technology stands at a crucial crossroads: advanced algorithms are meeting distributed data, and emerging intelligence is converging with complex systems, driving forward the digital transformation of society. Against this backdrop, IOCAC2027 aims to bring together cutting-edge perspectives, focusing on how fundamental computational theories, secure infrastructures, and data-driven insights can deliver meaningful technological and scientific advances. We sincerely invite computer scientists, data analysts, engineers, and scholars to join us and share their latest insights and research at this inaugural event.

IOCAC2027 will feature a wide range of sessions covering critical topics in computer science and information systems, including, but not limited to, the following:
S1: IoT, IIoT and ICT Infrastructure Cybersecurity;
S2: AI-Driven Innovations;
S3: Optimization Algorithms: Theory and Applications;
S4: Evolutionary Algorithms and Machine Learning;
S5: Data-Driven Intelligence and Analytics for Complex Systems;
S6: Networking and Communication;
S7: Information Security and Privacy.

Important deadlines:
Abstract submission deadline: 26 February 2027;
Abstract acceptance notification: 23 April 2027;
Registration deadline: 22 June 2027.

Guide for authors:
To submit your abstract, click here: https://sciforum.net/submissions/IOCAC2027/1.

To register for the event for free, click here: https://sciforum.net/event/IOCAC2027/registration.

For more information, please refer to the following link: https://sciforum.net/event/IOCAC2027.

For any enquiries regarding the event, please contact us at iocac@mdpi.com.

Our portfolio of journals available for publishing up-to-date research in immediate open access format has been further expanded. In June 2026, eleven new journals released their inaugural issues and two transferred journals released their first issue as part of MDPI, covering the subjects of environmental & earth sciences, chemistry & materials science, public health & healthcare, engineering, medicine & pharmacology, and biology & life sciences. 

We extend our gratitude to the Editors-in-Chief, Associate Editors, and Editorial Board Members who will shape the future course of these new journals. Each journal is dedicated to upholding strong editorial standards through a thorough peer review process, enabling impactful open access scholarship. 

Please feel free to browse and discover more about the new journals below. 

We would like to thank everyone who has supported the development of open access publishing. If you would like to create a new journal, you are welcome to send an application here or contact the New Journal Committee (newjournal-committee@mdpi.com).

Peer review is the invisible foundation of academic progress. The 2025 MDPI Top Reviewers are some of the key experts who helped build it.

We are honored to recognize 1000 individuals from our global community of 210,000 reviewers across 67 countries and territories—chosen for their commitment to rigorous, constructive peer review and the consistency of their contributions throughout 2025.

The Top Reviewers provided constructive and impartial feedback that makes research stronger and more impactful, and we are grateful for it.

The names of these reviewers are listed below in alphabetical order by first name:

(Note: we respected all privacy preferences, with part of nominees opting for limited attribution.)

We are honoured to announce that Mr. Paul Scalise has been selected as the winner of the Future Internet Best Paper Award 2024.

“A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas”
by Paul Scalise, Matthew Boeding, Michael Hempel, Hamid Sharif, Joseph Delloiacovo and John Reed
Future Internet 2024, 16(3), 67; https://doi.org/10.3390/fi16030067

The following is an interview with Mr. Paul Scalise:

1. Congratulations on winning the Future Internet 2024 Best Paper Award! Could you please briefly introduce yourself and what led you to write this review?

My name is Paul Scalise and I am honored to receive this award from MDPI Future Internet for the best paper of 2024. I am currently a PhD student at the University of Nebraska–Lincoln studying Computer Engineering in the Advanced Telecommunications Engineering Lab (TEL), headed by my advisor Dr. Hamid Sharif. My research interests have revolved around the tight relationship between hardware and software along with privacy and security of computing systems. With 5G becoming ever more important across the world, this review was a great place to share perspective of the current state of the art and future considerations for these complex architectures.

2. With so many studies published in this area, how did you decide which works to include and how to structure the narrative?

I thought that bringing a systematic viewpoint for this review was super important for readers to get a full picture of all the different aspects of security in 5G and future 6G systems. In doing so, I believe it allows for better understanding and enables researchers to tackle open research questions.

3. What emerging trend or underexplored direction did you uncover while surveying the literature that surprised you most?

In this study we emphasized the applications and considerations of zero-trust architectures in future cellular networks. This topic had been gaining ground in other research areas, and I thought that this was important to incorporate as a main portion of this study and how ZT principals will be increasingly valuable in the near future.

4. How do you hope this review will guide researchers or practitioners who are new to this field?

I hope our study allows newer researchers in our field, and people from adjacent fields, to understand the impacts of how security within complex cellular systems are vital to end user protection. Security of any system must be a forefront thought while designing and conceptualizing future networks to allow for a prosperous ecosystem that respects the users, maintainers, operators, and anyone in-between.

5. Based on your experience preparing and submitting this review to Future Internet, what suggestions do you have for us to improve the review article submission and peer review process?

Overall, the process went smoothly. I appreciate that there is some leniency in the time for responding to reviewers when some revisions take longer than expected.

6. What does receiving this award mean to you, and is there anyone you would like to acknowledge?

This award is greatly meaningful in that it reflects the hard work of our team at UNL. Writing this review would have been impossible for me without the diligent efforts of the leaders within my lab: Dr. Hamid Sharif, Dr. Michael Hempel, and Dr. Matthew Boeding. I also thank our partner authors from Belcan for their input from their industry knowledge. A huge thank you as well goes to my family and friends for their continued support and belief in my abilities.

We are honored to announce that Dr. Aristeidis Karras has been selected as the winner of the Future Internet Best Paper Award 2024.

The following is an interview with Dr. Aristeidis Karras:

1. Congratulations on winning the Future Internet 2024 Best Paper Award! Could you please briefly introduce yourself and your research background?
Thank you very much. It is a great honor for me and for all the authors to receive this award.
My research focuses on efficient Big Data management in large-scale Internet of Things systems. More specifically, I work in areas including distributed and edge computing, TinyML, machine learning, data engineering, and intelligent processing on resource-constrained devices.
A central question in my research is how we can process data closer to where it is generated. In a large IoT environment, transmitting every raw measurement to a central server is often inefficient. It increases communication overhead, latency, and storage requirements. Edge computing and TinyML provide opportunities to clean, analyze, compress, organize, and prioritize data locally before it is transferred to a central infrastructure.
I would also like to emphasize that this award reflects the collective work of the entire research team. All co-authors contributed to the study's conception, experimental work, result analysis, and manuscript preparation.

2. Could you give a brief overview of the key findings of this award-winning paper?
The main purpose of the paper was to examine whether TinyML could support not only machine learning inference but also broader data-management operations in large-scale IoT systems.
We proposed five algorithms, each addressing a different part of the IoT data-management process. TinyCleanEDF uses federated learning for distributed data cleaning and anomaly detection and employs autoencoders for feature extraction. EdgeClusterML combines reinforcement learning with self-organizing maps for adaptive clustering at the edge. CompressEdgeML performs neural-network-based adaptive data compression. CacheEdgeML uses predictive analytics and tiered caching to improve access to frequently requested data. Finally, TinyHybridSenseQ assesses data quality and decides whether data should be stored locally, in the cloud, or in a central database according to their quality and priority.
The algorithms were evaluated using different configurations ranging from one to ten Raspberry Pi devices and a heterogeneous collection of sensor data. The dataset included environmental, motion, orientation, light, distance, and soil-moisture measurements and contained more than one terabyte of raw data.
The results showed that TinyML can support several important data-management functions directly at the edge. EdgeClusterML achieved approximately 90% clustering accuracy. CompressEdgeML maintained data integrity above 90% while reducing compression time from approximately 1200 milliseconds with one device to around 300 milliseconds with ten devices. CacheEdgeML increased its cache hit rate from 85% to 92%. TinyHybridSenseQ maintained a data-quality score between 90% and 95%, increased storage efficiency from 85% to 91%, and reduced data-transfer latency from 250 milliseconds to 160 milliseconds.
An important observation is that adding more devices did not improve every metric equally. Some measurements, such as compression efficiency and data-quality score, decreased slightly as the system scaled, although they remained at high levels. This illustrates the practical trade-offs involved in distributed edge processing.
Overall, the study demonstrated that TinyML can become an active part of the data-management architecture. It can help decide which data should be cleaned, compressed, cached, prioritized, stored, or transmitted before that data reaches the central Big Data infrastructure.

3. What were the biggest challenges you faced during this research, and how did you overcome them?
One of the main challenges was the diversity and volume of the data. Our experimental environment included several types of sensors producing data with different characteristics and collection frequencies. Managing more than one terabyte of raw sensor data required careful preprocessing, including data cleaning and normalization, before the algorithms could be evaluated consistently.
A second challenge was the distributed experimental environment. We needed to evaluate the algorithms on resource-constrained edge devices while also considering communication with a central computing infrastructure. The system included Raspberry Pi devices, an HPC cluster, local model training, model aggregation, and communication under both Ethernet and Wi-Fi conditions.
Scalability was another important challenge. Demonstrating that an algorithm works on one device is not sufficient for a study concerning large-scale IoT systems. We therefore evaluated the algorithms using one, two, five, and ten devices and examined how accuracy, processing time, communication efficiency, resource utilization, caching, storage, and latency changed as the system expanded.
We addressed these challenges by designing the system in a modular way. Each algorithm was responsible for a clearly defined data-management function. We also used a common architecture connecting the edge devices to the central Big Data system and applied consistent evaluation metrics across the different configurations.
Perhaps the most important lesson was that scalability always involves trade-offs. Some metrics improved significantly as the workload was distributed, while others remained stable or declined slightly. Reporting these trade-offs honestly was essential for presenting a realistic evaluation of the proposed system.

4. What advice would you give to young researchers who aspire to produce high-impact research results?
My first piece would be to begin with a real and clearly defined problem rather than with a fashionable technology. A technology is useful only when it addresses a genuine scientific or practical need.
Researchers should also try to implement and test their ideas whenever possible. A theoretical model may appear effective, but practical experimentation often reveals issues related to hardware limitations, communication overhead, data quality, scalability, and reproducibility. Even a modest prototype can provide valuable scientific evidence.
It is equally important to report limitations and negative results. A strong research paper does not need to claim that every method performs perfectly. In many cases, the most useful result is an honest explanation of the conditions under which a method works well and the situations in which it requires further development.
Young researchers should also be patient. Research involves failed experiments, repeated implementation, manuscript revisions, and critical feedback. These are not exceptions to the research process; they are part of it.
Finally, collaboration is essential. This study combined knowledge from IoT systems, Big Data management, machine learning, edge computing, and embedded platforms. Such work becomes stronger when researchers with different areas of expertise contribute to the same problem.

5. How was your experience with the editorial and peer-review process for Future Internet?
My overall experience with Future Internet was positive. The editorial communication was clear, and the manuscript progressed through the submission, review, revision, and publication stages in an organized manner.
The peer-review process gave us the opportunity to revisit the manuscript carefully and improve the clarity of the methodology, the description of the proposed algorithms, and the presentation of the experimental results. We treated the revision process as an opportunity to strengthen the paper rather than simply as a procedural requirement.
I also appreciated the journal’s willingness to consider interdisciplinary research. The paper brings together IoT, Big Data management, TinyML, machine learning, embedded computing, and cloud-edge coordination. Research of this kind needs reviewers and editors who can appreciate contributions that extend across several technical areas.
Receiving the Best Paper Award has made the publication experience particularly meaningful for the entire author team.

6. Is there anything else you would like to share—perhaps thoughts on how journals like Future Internet can better support researchers, or reflections that have not come up yet?
I believe that journals can further support researchers by encouraging reproducibility and practical validation. In fields such as IoT, TinyML, and Edge AI, it is important to report not only algorithmic results but also hardware configurations, communication conditions, data characteristics, resource limitations, and implementation details.
Journals can also encourage authors to discuss trade-offs more openly. Results that show a limitation or a decline in one metric can still be scientifically valuable when they help researchers understand the behavior of a system under realistic conditions.
Where possible, sharing code, configurations, datasets, and experimental protocols can also help other researchers reproduce and extend published work. Standardized benchmarks for TinyML and distributed edge systems would be particularly valuable because comparisons between studies are often difficult when different hardware platforms, datasets, and metrics are used.
From a research perspective, several important challenges remain. These include more accurate anomaly detection, lower energy consumption, improved cloud-edge integration, secure and privacy-preserving processing, faster analysis of streaming data, greater adaptability to different IoT applications, and improved interoperability between heterogeneous devices and platforms.
Finally, I would like to thank the Future Internet Editorial Office, the Best Paper Awards Committee, the reviewers, and all my co-authors. This award is a significant encouragement for us to continue investigating practical and efficient approaches to intelligent data management in future IoT systems.

1. “An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets”
by Ahmad Salehiyan, Pardis Sadatian Moghaddam and Masoud Kaveh
Future Internet 2025, 17(7), 279; https://doi.org/10.3390/fi17070279
Available online: https://www.mdpi.com/1999-5903/17/7/279

2. “JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)”
by Jorge Cisneros-González, Natalia Gordo-Herrera, Iván Barcia-Santos and Javier Sánchez-Soriano
Future Internet 2025, 17(6), 265; https://doi.org/10.3390/fi17060265
Available online: https://www.mdpi.com/1999-5903/17/6/265

3. “Generative Adversarial and Transformer Network Synergy for Robust Intrusion Detection in IoT Environments”
by Pardis Sadatian Moghaddam, Ali Vaziri, Sarvenaz Sadat Khatami, Francisco Hernando-Gallego and Diego Martín
Future Internet 2025, 17(6), 258; https://doi.org/10.3390/fi17060258
Available online: https://www.mdpi.com/1999-5903/17/6/258

4. “Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI”
by Leandro Antonio Pazmiño Ortiz, Ivonne Fernanda Maldonado Soliz and Vanessa Katherine Guevara Balarezo
Future Internet 2025, 17(6), 257; https://doi.org/10.3390/fi17060257
Available online: https://www.mdpi.com/1999-5903/17/6/257

5. “AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media”
by Zeinab Shahbazi, Rezvan Jalali and Zahra Shahbazi
Future Internet 2025, 17(6), 231; https://doi.org/10.3390/fi17060231
Available online: https://www.mdpi.com/1999-5903/17/6/231

6. “DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization”
by Hardi Sabah Talabani, Zrar Khalid Abdul and Hardi Mohammed Mohammed Saleh
Future Internet 2025, 17(5), 211; https://doi.org/10.3390/fi17050211
Available online: https://www.mdpi.com/1999-5903/17/5/211

7. “Integrating AIoT Technologies in Aquaculture: A Systematic Review”
by Fahmida Wazed Tina, Nasrin Afsarimanesh, Anindya Nag and Md Eshrat E. Alahi
Future Internet 2025, 17(5), 199; https://doi.org/10.3390/fi17050199
Available online: https://www.mdpi.com/1999-5903/17/5/199

8. “Edge AI for Real-Time Anomaly Detection in Smart Homes”
by Manuel J. C. S. Reis and Carlos Serôdio
Future Internet 2025, 17(4), 179; https://doi.org/10.3390/fi17040179
Available online: https://www.mdpi.com/1999-5903/17/4/179

9. “A Survey on Edge Computing (EC) Security Challenges: Classification, Threats, and Mitigation Strategies”
by Abdul Manan Sheikh, Md. Rafiqul Islam, Mohamed Hadi Habaebi, Suriza Ahmad Zabidi, Athaur Rahman Bin Najeeb and Adnan Kabbani
Future Internet 2025, 17(4), 175; https://doi.org/10.3390/fi17040175
Available online: https://www.mdpi.com/1999-5903/17/4/175

We are pleased to announce that Prof. Dr. Qiang Qu has been appointed Editor-in-Chief of the Section “Big Data and Augmented Intelligence”. We look forward to his contributions to Future Internet (ISSN 1999-5903).

Prof. Dr. Qiang Qu is a full professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He is currently the director of Guangdong Provincial Blockchain and Distributed IOT Security Engineering Research Center, and the deputy director of Shenzhen Key Laboratory on High-performance Data Mining. He is a senior member of the China Computer Federation. Qiang received his PhD from Aarhus University, supervised by Obel Professor Christian S. Jensen in 2014. He has work experience from Innopolis University, Carnegie Mellon University, ETH Zurich and Singapore Management University. His research endeavors focus on blockchain/Web3 technologies, databases, data mining, and advanced data intelligence systems. He joined the Chinese Academy of Sciences at the end of 2016, and he was promoted to a full professor in 2020. He has been a principle investigator (PI) for a number of projects, and he is now the chief scientist for a project supported by the National key research and development Program of China.

The following is a short Q&A with Prof. Qiang Qu, who shared his vision for the journal with us, as well as his views of the research area and open access publishing:

1. Could you briefly introduce your main research areas and achievements?
My research focuses on trustworthy data intelligence, integrating blockchain and AI. I was among the first to systematically propose the Blockchain+AI paradigm and also proposed the concept of spatial–temporal blockchain to anchor data in real-world contexts. I published a pioneering book on this paradigm in 2019, and have since led national key R&D projects, authored 180+ papers, and deployed solutions at Huawei Cloud. I believe this paradigm is the core technology for the future intelligent society and the emerging agent economy.

2. What appealed to you about the journal that made you want to take the role as its Section Editor-in-Chief?
Future Internet’s Section “Big Data and Augmented Intelligence” sits at the perfect intersection of my work. The journal’s forward-looking, open access spirit aligns with my belief that data is the foundation and moat of AI. I see this role as a platform to shape rigorous, impactful discourse on how decentralized intelligence and big data co-evolve.

3. What are your expectations and suggestions for the future development of our Section and the journal?
I expect the section to become a hub for cross-disciplinary research on trustworthy, decentralized data systems. My suggestion is to actively encourage submissions on blockchain–AI integration, multi-agent systems, and data sovereignty, areas that are often siloed. We should also foster industry–academic dialogues to ground research in real-world agent economy needs.

4. What is your vision for the journal?
My vision is to position Future Internet as the leading venue for actionable intelligence on future data ecosystems, where big data meets augmented intelligence via verifiable, decentralized infrastructure. I want the journal to bridge theoretical advances (e.g., Web4, agent economies) with practical engineering, emphasizing data as both the foundation and the competitive moat for AI systems.

5. What does the future of this field of research look like?
The future lies in agent economies powered by trustworthy data intelligence. We will see massive multi-agent collaboration underpinned by blockchain for trust and AI for decision-making. Data will not only fuel AI but also serve as a strategic asset, the moat. Research will shift from isolated models to verifiable, interoperable, and human-centric intelligent systems.

6. What do you think of the development of open access in the publishing field?
Open access is essential for democratizing knowledge, especially in fast-moving fields like blockchain and AI. It accelerates the translation of research into real-world impact, enabling startups, policymakers, and global researchers to access cutting-edge insights. However, we must ensure quality and sustainability with rigorous peer review are the way forward.

We wish Prof. Dr. Qiang Qu every success in his new position, and we look forward to his contributions to the journal.