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2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members

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A Special Issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 31 December 2027 | Viewed by 9261

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Prof. Dr. Gianluigi Ferrari

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Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
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Special Issue Information

Dear Colleagues,

We have launched a Special Issue titled “2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members”, which will form a collection of high-quality, open-access papers from the editorial board members of Future Internet, or those recommended and invited by board members and the Editor-in-Chief.

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

Computer networking/communications and information systems;
Internet of Things;
Big data and augmented intelligence;
Smart systems (in terms of technologies, architectures, and applications);
Network virtualization;
Edge/fog computing;
Cybersecurity.

Prof. Dr. Gianluigi Ferrari
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet 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.

Keywords

Internet of Things
artificial intelligence
network virtualization
cybersecurity

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Published Papers (5 papers)

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Research

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46 pages, 4621 KB

Open AccessArticle

DANA: A Digital Agent for Network Data Acquisition in Network Digital Twin Context

by Mario Sanz-Rodrigo, Diego Rivera, José Ignacio Moreno, Manuel Álvarez-Campana and Carmen Sánchez-Zas

Future Internet 2026, 18(10), 523; https://doi.org/10.3390/fi18100523 - 29 Sep 2026

Viewed by 151

Abstract

Network Digital Twins (NDTs) require accurate, structured, and timely data from heterogeneous communication networks. However, many existing approaches assume that this information is already available and do not address the operational path needed to convert raw observations into deployable twin artifacts. This paper presents DANA, a lifecycle-aware data acquisition and transformation system for dedicated network devices and general-purpose systems hosting virtualized or containerized functions. Its key technical contribution is a unified workflow that coordinates centralized and distributed acquisition through a common, state-aware normalization layer. Each observation is associated with source, temporal, scenario, and lifecycle metadata, enabling the system to preserve the structural baseline used to construct the twin while processing runtime monitoring information separately. The normalized representation is decoupled from deployment-specific descriptor generation, while data and control exchanges follow a publish/subscribe communication model. The system is evaluated in two complementary controlled laboratory scenarios. The centralized workflow demonstrates accurate topology reconstruction and high agreement in network reachability, whereas the distributed workflow validates the complete path from host-level acquisition and normalization to descriptor generation and twin instantiation. Complementary local microbenchmarks characterize MQTT transport, fixed-load resource use, and stop/recovery event propagation to a test consumer while preserving the structural baseline. These results support the feasibility of the proposed workflow in the studied environments; large-scale operation and full synchronization of deployed NDTs require further experimental validation. Full article

(This article belongs to the Special Issue 2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members)

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28 pages, 389 KB

Open AccessArticle

Assessing Generative Artificial Intelligence Tools Usage Guidelines at the Top 200 Universities Worldwide

by Midrar Ullah and Maged N. Kamel Boulos

Future Internet 2026, 18(9), 492; https://doi.org/10.3390/fi18090492 - 19 Sep 2026

Viewed by 997

Abstract

This study aimed to evaluate the guidelines for the use of GenAI tools available on the websites of the world’s top 200 universities. We collected all publicly available guidelines from the websites of the top 200 universities listed in the 2026 QS World University Rankings. These guidelines were evaluated using the same 24-item checklist developed for our 2024 survey of the top 50 universities worldwide. The assessment revealed that 180 institutions have publicly available guidelines. All universities permit the use of GenAI tools under specific conditions. These policies are primarily established by academic bodies that focus on teaching, learning, and innovation. Most guidelines emphasize the use of university-licensed options to enhance data security. The most cited GenAI tools included ChatGPT, Microsoft Copilot, and Google Gemini (formerly Bard). More than 80% of the guidelines covered academic integrity, the limitations of GenAI tools, data privacy, the integration of GenAI in teaching and assessment, and the need to acknowledge its use. However, the operation of AI algorithms, reporting mechanisms for non-permitted use, documentation of prompts, and adoption of outputs were addressed in fewer than 40% of the guidelines. A comparison with our 2024 data for the subset of 50 universities re-evaluated in 2026 shows progress in guidelines coverage, reflecting institutions’ rapid responses to the evolving AI landscape. Despite the QS top 200’s concentration in wealthy nations, our checklist and findings are actionable for universities across the Global South. Full article

(This article belongs to the Special Issue 2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members)

30 pages, 531 KB

Open AccessArticle

Defeat Devices in AI Systems

by Emilio Ferrara

Future Internet 2026, 18(7), 339; https://doi.org/10.3390/fi18070339 - 27 Jun 2026

Viewed by 1109

Abstract

AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchmark gaming, deceptive scheming, specification gaming, and trojans have each been documented separately, with each line of work characterizing one facet of what we argue is a single structural mechanism: we propose that this common mechanism is a defeat device, an engineering and regulatory concept long established in vehicle-emissions law and brought to broad public attention by the 2015 Volkswagen emissions case. A defeat device in an AI system has three necessary elements: a discriminator that detects evaluation context, a concealed swap that conditions behavior on detection, and a gap between eval-distribution and deployment-distribution performance on the stated evaluation criterion. We formalize this triadic test as a behavioral definition, organize documented cases along three taxonomic axes (origin, trigger, swap mechanism), propose Trigger-Axis-Aware Differential Probing (TADP) as a forensic detection protocol, and advance the claim that defeat devices can naturally emerge in current frontier AI systems without any operator engineering. We characterize naturally emerging defeat devices as potentially one of the harmful emerging phenomena that AI safety practice should monitor and test for systematically. An illustrative study applying TADP across eight open-weight models finds the discriminator to be near-universal (every model detects evaluation context well above chance), while the conditional swap is real but heterogeneous: it appears strongly as sycophantic stance-conditioning and as an evaluation-cued register shift, yet not as overt demographic discrimination, indicating that the mechanism’s discriminator generalizes even where individual swaps do not. Implications for evaluation methodology, post-training pipeline design, interpretability research priorities, and AI governance follow. Full article

(This article belongs to the Special Issue 2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members)

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30 pages, 3376 KB

Open AccessArticle

Topology-Aware Deep Reinforcement Learning for Dynamic Multicast Routing in Software-Defined Networks

by Peiying Zhang, Lijuan Chen, Jian Wang, Yujie Yuan, Chun Sing Lai and Lizhuang Tan

Future Internet 2026, 18(6), 281; https://doi.org/10.3390/fi18060281 - 25 May 2026

Viewed by 818

Abstract

Dynamic multicast routing in software-defined networks is challenging due to continuously changing network states, multicast branch coupling, and the dependency between local forwarding decisions and global multicast tree construction. Existing multicast routing approaches mainly rely on static heuristics or snapshot-based optimization, which makes them difficult to maintain routing adaptability and decision stability under dynamic network conditions. To address these limitations, this paper proposes a topology-aware deep reinforcement learning multicast routing algorithm, named Graph-structured Hierarchical Actor–Critic for Multicast Routing (GHAC-MR). Specifically, the multicast routing process is formulated as a sequential tree construction problem, where each forwarding action incrementally affects the subsequent multicast tree evolution. A graph-structured state representation mechanism is designed to encode network topology information, link resource states, and multicast branch dependencies, enabling the routing agent to capture structural correlations among multicast forwarding nodes. Furthermore, a hierarchical actor–critic learning architecture is introduced to jointly optimize multicast forwarding policies and long-term routing rewards, thereby improving routing adaptability and convergence stability in dynamic network environments. Experimental results on multiple representative network topologies demonstrate that the proposed GHAC-MR algorithm achieves superior performance in multicast acceptance ratio, resource utilization efficiency, and routing adaptability compared with representative heuristic, evolutionary, and reinforcement learning-based multicast routing schemes. Full article

(This article belongs to the Special Issue 2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members)

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35 pages, 710 KB

Open AccessReview

AI Agent Communications in the Future Internet—Paving a Path Toward the Agentic Web

by Qiang Duan and Zhihui Lu

Future Internet 2026, 18(3), 171; https://doi.org/10.3390/fi18030171 - 21 Mar 2026

Cited by 5 | Viewed by 5429

Abstract

The rapid evolution of artificial intelligence technologies toward the agentic AI paradigm enables the emergence of the Agentic Web in the future Internet. Agent communication plays a critical role in constructing the Agentic Web but faces unique challenges posed by the edge–network–cloud continuum in the future Internet. This paper provides a comprehensive overview of state-of-the-art agent communication protocols and technologies, evaluating their readiness to support the construction of the Agentic Web. We first survey representative communication protocols and analyze the key technologies they employ, assessing their effectiveness in addressing the challenges for agent communications in the future Internet. We then identify critical gaps between existing approaches and the requirements of the Agentic Web, and propose a unified architectural framework grounded in virtualization and service-oriented principles to address these gaps. Such a framework may greatly facilitate the development of a pluralistic ecosystem in which various agent communication technologies and protocols can be freely developed and fully utilized. We also discuss open topics and possible directions for future research toward a fully realized Agentic Web. Full article

(This article belongs to the Special Issue 2026 and 2027 Feature Papers from Future Internet’s Editorial Board Members)

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