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Internet of Things, Big Data and Smart Systems II
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Internet of Things, Big Data and Smart Systems II

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 7393

Special Issue Editors

Prof. Dr. Weiming Shen

E-Mail Website
Guest Editor
1. National Research Council of Canada, Ottawa, ON, Canada
2. Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada
Interests: computer-supported collaborative work; modeling and implementation of decision support systems; agent-based systems; disaster management; distributed computing; wireless sensor networks; Internet of Things; smart cities; supply chain; manufacturing systems; process optimization and scheduling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giancarlo Fortino

grade E-Mail Website
Guest Editor
Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Interests: body area networks; Internet of Things; agent-based computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Liotta

E-Mail Website
Guest Editor
Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Interests: data analsis; machine learning; smart systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yanjun Shi

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian, China
Interests: connected and automated vehicles; V2X; industrial IoT; digital twins; big data; intelligent machines; cooperative connected technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Jonice Oliveira

E-Mail Website
Guest Editor
Department of Computing Science, Federal University of Rio de Janeiro, Rio de Janeiro 21941-916, RJ, Brazil
Interests: knowledge management; social network analysis; social computing; big data
Dr. Sergio F. Ochoa

E-Mail Website
Guest Editor
Computer Science Department, University of Chile, Santiago, Chile
Interests: computer-supported collaborative work; ad hoc communication and coordination; loosely-coupled collaborative work; emergency management; decision making; collaborative systems; software modeling tools and methodologies; software architecture; mobile and ubiquitous computing; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has been widely accepted as a novel paradigm that can radically transform industry and society. It can achieve the seamless integration of various devices equipped with sensing, identification, processing, communication, actuation, and networking capabilities. Along these lines, big data are considered a form of technology and have become a very active research area, primarily involving topics related to machine learning, databases, and distributed computing. The fast development of IoT and big data technologies, together with 5G communication, edge/cloud computing, and artificial intelligence, provides great opportunities for novel smart systems and applications, including smart cities, smart manufacturing, smart transportation and logistics, smart building, smart homes, and smart healthcare. The topics of interest include but are not limited to:

  • Collaborative wireless sensor networks;
  • IoT architectures, protocols, and algorithms;
  • Positioning and localization in the IoT;
  • Data and information management in IoT-based smart systems, including distributed storage, collaborative processing, query, manipulation, data cleaning, data fusion, and data mining;
  • Big Data analytics (including machine learning and deep learning) in IoT-based smart systems;
  • Edge/fog/cloud collaboration in IoT-based smart systems;
  • Intelligent decision making and control in IoT-based smart systems;
  • Reliability, security, and privacy in IoT-based smart systems;
  • Smart systems and applications (smart cities, smart manufacturing, smart transportation and logistics, smart building, smart homes, and smart healthcare).

Prof. Dr. Weiming Shen
Prof. Dr. Giancarlo Fortino
Prof. Dr. Antonio Liotta
Dr. Yanjun Shi
Dr. Jonice Oliveira
Dr. Sergio F. Ochoa
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. Sensors 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 2600 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.

Related Special Issues

Published Papers (3 papers)

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Research

14 pages, 18567 KiB  
Article
Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning
by Xianfeng Ye, Zhiyun Deng, Yanjun Shi and Weiming Shen
Sensors 2023, 23(12), 5615; https://doi.org/10.3390/s23125615 - 15 Jun 2023
Cited by 4 | Viewed by 1658
Abstract
This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient [...] Read more.
This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, with modifications made to the action and state space to fit the setting of AGV activities. While previous studies overlooked the energy efficiency of AGVs, this paper develops a well-designed reward function that helps to optimize the overall energy consumption required to fulfill all tasks. Moreover, we incorporate the e-greedy exploration strategy into the proposed algorithm to balance exploration and exploitation during training, which helps it converge faster and achieve better performance. The proposed MARL algorithm is equipped with carefully selected parameters that aid in avoiding obstacles, speeding up path planning, and achieving minimal energy consumption. To demonstrate the effectiveness of the proposed algorithm, three types of numerical experiments including the ϵ-greedy MADDPG, MADDPG, and Q-Learning methods were conducted. The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency. Full article
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems II)
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32 pages, 11794 KiB  
Article
Evaluation of Federated Learning in Phishing Email Detection
by Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba, Yansong Gao, Seyit Camtepe, Surya Nepal, Mahathir Almashor and Yifeng Zheng
Sensors 2023, 23(9), 4346; https://doi.org/10.3390/s23094346 - 27 Apr 2023
Cited by 3 | Viewed by 1969
Abstract
The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of [...] Read more.
The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric. Full article
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems II)
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25 pages, 2161 KiB  
Article
Swarm Intelligence in Internet of Medical Things: A Review
by Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi and Giancarlo Fortino
Sensors 2023, 23(3), 1466; https://doi.org/10.3390/s23031466 - 28 Jan 2023
Cited by 12 | Viewed by 3137
Abstract
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction [...] Read more.
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined. Full article
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems II)
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