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Future Internet
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State-of-the-Art Future Internet Technology in Japan 2022-2023
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State-of-the-Art Future Internet Technology in Japan 2022-2023

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6814

Special Issue Editors

Dr. Kien Nguyen

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Guest Editor
Institute for Advanced Academic Research, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Interests: Internet of Things; network protocols; network softwarization; distributed ledger technology; wireless communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mikio Hasegawa

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Guest Editor
Department of Electrical Engineering, Tokyo University of Science, Tokyo 125-8585, Japan
Interests: chaos; neural networks; optimization; wireless communication systems; mobile networks; cognitive radio
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hiroo Sekiya

E-Mail Website
Guest Editor
Graduate School of Engineering, Chiba University, 1-33,Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Interests: power amplifiers; power electronics; wireless communications; nonlinear circuit theory; digital signal processing
Dr. Kentaro Ishizu

E-Mail Website
Guest Editor
Beyond 5G Design Initiative, National Institute of Information and Communications Technology, 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Interests: mobile communications systems; spectrum sharing systems

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 Japan. 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 communications network infrastructures;
  • Internet of Things technologies and applications;
  • 5G and beyond;
  • Network softwarization, software-defined networking, network virtualization, P4;
  • AI/Machine learning for Future Internet;
  • Blockchain/distributed ledger technologies for Future Internet
  • Centralized and distributed data centers;
  • Quantum computing, quantum communication, quantum Internet;
  • Industrial Internet;
  • Cloudlet and fog computing;
  • Cyber-physical systems;
  • Smart energy systems;
  • Smart healthcare systems;
  • Smart manufacturing lines;
  • Smart cities;
  • Smart learning systems;
  • Artificial and augmented intelligence;
  • Cyber-security compliance;

Dr. Kien Nguyen
Prof. Dr. Mikio Hasegawa
Prof. Dr. Hiroo Sekiya
Dr. Kentaro Ishizu
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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

15 pages, 510 KiB  
Article
Online Optimization of Pickup and Delivery Problem Considering Feasibility
by Ryo Matsuoka, Koichi Kobayashi and Yuh Yamashita
Future Internet 2024, 16(2), 64; https://doi.org/10.3390/fi16020064 - 17 Feb 2024
Viewed by 1702
Abstract
A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases [...] Read more.
A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents’ paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Japan 2022-2023)
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19 pages, 5776 KiB  
Article
Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System
by Siyuan Yang, Mondher Bouazizi, Tomoaki Ohtsuki, Yohei Shibata, Wataru Takabatake, Kenji Hoshino and Atsushi Nagate
Future Internet 2023, 15(1), 34; https://doi.org/10.3390/fi15010034 - 12 Jan 2023
Cited by 3 | Viewed by 2010
Abstract
In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the [...] Read more.
In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Japan 2022-2023)
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20 pages, 1370 KiB  
Article
A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
by Yue Su, Kien Nguyen and Hiroo Sekiya
Future Internet 2022, 14(11), 330; https://doi.org/10.3390/fi14110330 - 14 Nov 2022
Cited by 5 | Viewed by 2040
Abstract
Many IoT-blockchain systems in which blockchain connections run on an infrastructure-based network, such as Wi-Fi or LTE, face a severe problem: the single point of failure (SPoF) (i.e., depending on the availability, an access point of an LTE base station). Using infrastructure-less networks [...] Read more.
Many IoT-blockchain systems in which blockchain connections run on an infrastructure-based network, such as Wi-Fi or LTE, face a severe problem: the single point of failure (SPoF) (i.e., depending on the availability, an access point of an LTE base station). Using infrastructure-less networks (i.e., ad hoc networks) is an efficient approach to prevent such highly disruptive events. An ad hoc network can automatically restore blockchain communication using an ad hoc routing protocol, even if a node fails. Moreover, an ad hoc routing protocol is more efficient when considering the IoT nodes’ mobility. In this paper, we first construct IoT-blockchain systems on emulated and real ad hoc networks with Ethereum and three ad hoc routing protocols (i.e., OLSR, BATMAN, and BABEL). We then evaluate the blockchain recovery time in static and mobile scenarios. The results show that BATMAN achieves the best blockchain recovery performance in all investigated scenarios because BATMAN only determines whether to switch a route by comparing the number of OGM packets received from a different next-hop. More specifically, in the small-scale real IoT-blockchain, BATMAN recovers at least 73.9% and 59.8% better than OLSR and BABEL, respectively. In the medium-scale emulated IoT-blockchain, the recovery time of BATMAN is at least 69% and 60% shorter than OLSR or BABEL, respectively. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Japan 2022-2023)
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