State-of-the-Art Future Internet Technology in USA 2024–2025

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2198

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Interests: network modeling and optimization; IoT; cyber–physical systems; smart grid systems; network economics; wireless networks; social networks; cybersecurity; resource management; reinforcement learning; human behavior modeling; concentrated solar power systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece
Interests: complex networks; wireless systems; ad hoc and sensor networks; software-defined radios and software-defined networks; online social networks; network modeling and optimization; network economics; cyber–physical systems; internet of things; future internet research experimentation; resource orchestration; 5G/6G system design; system sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the current state of the art in Future Internet technology in the USA. We invite research articles that will consolidate our understanding in this area.

The Special Issue will publish full research papers and reviews. Potential topics include, but are not limited to, the following research areas:

  • Advanced communication network infrastructures;
  • Internet of Things;
  • Centralized and distributed data centers;
  • Industrial internet;
  • Embedded computing;
  • 5G/6G networking;
  • IoT platforms, integration, and services;
  • Software-defined network functions and network virtualization;
  • Quality of service in wireless and mobile networks;
  • Vehicular cloud networks;
  • Cloud-let and fog computing;
  • Cyber–physical systems;
  • Smart energy systems;
  • Smart healthcare systems;
  • Smart manufacturing lines;
  • Smart cities;
  • Human–computer interaction and usability;
  • Smart learning systems;
  • Artificial and augmented intelligence;
  • Cyber security compliance;
  • Public safety;
  • Human behavior modeling;
  • Hardware security;
  • Multi-access edge computing;
  • Digital twins for future internet;
  • Zero-touch management for IoT;
  • Metaverse and future networks.

Dr. Eirini Eleni Tsiropoulou
Prof. Dr. Symeon Papavassiliou
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. 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 1600 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

  • wireless networks
  • smart grid systems
  • cybersecurity
  • public safety
  • cyber–physical systems
  • edge computing
  • smart cities
  • Internet of Things

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Published Papers (2 papers)

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13 pages, 486 KiB  
Article
Novel Approach towards a Fully Deep Learning-Based IoT Receiver Architecture: From Estimation to Decoding
by Matthew Boeding, Michael Hempel and Hamid Sharif
Future Internet 2024, 16(5), 155; https://doi.org/10.3390/fi16050155 - 30 Apr 2024
Viewed by 906
Abstract
As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially [...] Read more.
As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially with the advances of 5G/6G technology. Nevertheless, the transmission of wireless communications demands substantial power and computational resources, presenting a significant challenge to these devices and their operations. In this work, we propose the use of deep learning to improve the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) wireless receivers. By improving the BER performance of these receivers, devices can transmit with less power, thereby improving IoT devices’ battery life. The architecture presented in this paper utilizes a depthwise Convolutional Neural Network (CNN) for channel estimation and demodulation, whereas a Graph Neural Network (GNN) is utilized for Low-Density Parity Check (LDPC) decoding, tested against a proposed (1998, 1512) LDPC code. Our results show higher performance than traditional receivers in both isolated tests for the CNN and GNN, and a combined end-to-end test with lower computational complexity than other proposed deep learning models. For BER improvement, our proposed approach showed a 1 dB improvement for eliminating BER in QPSK models. Additionally, it improved 16-QAM Rician BER by five decades, 16-QAM LOS model BER by four decades, 64-QAM Rician BER by 2.5 decades, and 64-QAM LOS model BER by three decades. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in USA 2024–2025)
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23 pages, 7707 KiB  
Article
NeXtFusion: Attention-Based Camera-Radar Fusion Network for Improved Three-Dimensional Object Detection and Tracking
by Priyank Kalgaonkar and Mohamed El-Sharkawy
Future Internet 2024, 16(4), 114; https://doi.org/10.3390/fi16040114 - 28 Mar 2024
Viewed by 978
Abstract
Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep Camera-Radar fusion neural networks offer a promising solution for reliable [...] Read more.
Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep Camera-Radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient Camera-Radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in USA 2024–2025)
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