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Internet of Things, Sensing and Cloud Computing—2nd Edition
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Internet of Things, Sensing and Cloud Computing—2nd Edition

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 2186

Special Issue Editors

Prof. Dr. Rongxing Lu

grade E-Mail Website
Guest Editor
Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3A 0A1, Canada
Interests: blockchain; distributed concensus; distributed ledger; smart contract; security; privacy, trust, applications
Special Issues, Collections and Topics in MDPI journals
Dr. Yunguo Guan

E-Mail Website
Guest Editor
School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, MI 48197, USA
Interests: applied cryptography; privacy-preservation; blockchain
Dr. Xichen Zhang

E-Mail Website
Guest Editor
Department of Finance, Information Systems & Management Science at the Sobey School of Business, Saint Mary's University, Halifax, NS B3H 3C3, Canada
Interests: data mining in cybersecurity; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At the beginning of the last decade, the Internet of Things and the Cloud Computer worlds were both emerging. The first dealt with tiny low-power devices, while the latter focused on elasticity, virtualization, and the pay-per-use model, promising the democratic distribution of computer power and storage. In recent years, the two technologies have become intimately related to each other, converging on a new paradigm that could be defined as the high-performance Internet of Things. This evolutionary thread has advanced sensor technologies and their related applications greatly, as evidenced by the massive rise of distributed low-cost sensor networks and the data crowdsourcing model. At the same time, the computation for the edge and fog computing paradigms has enabled the design and development of a new class of cloud-native applications in which the IoT acts as a bridge between sensing technologies and cloud computing-hosted applications.

This Special Issue looks for novel contributions related to the application of the IoT and sensing systems to cloud computing-powered infrastructures. The main topics of interest include but are not limited to the following:

  • GPU, FPGA, and heterogeneous processing algorithms;
  • IoT microservice applications;
  • Urban informatics and cloud applications;
  • Novel protocols for fast, secure, reliable, and resilient data transfer;
  • Workflows and orchestration systems involving the IoT, sensing, and cloud resources;
  • CPU, GPU, and FPGA offloading at the edge;
  • Cloud-native pattern recognition algorithms for sensors and IoT-produced data;
  • Osmotic computing and other edge computing paradigms;
  • Security and reliability for IoT data;
  • Cloud computing data distribution and provisioning;
  • Artificial Intelligence for the IoT and sensors in the Cloud;
  • Computational intelligence and machine learning for the IoT and cloud-based smart systems for sensor networks;
  • Federated learning;
  • Scheduling of IoT-sensing hybrid/cloud applications;
  • IoT solutions for coastal and ocean monitoring;
  • Long-range systems for ocean vessel-to-cloud data transfer;
  • Sensors and IoT data mining on the Cloud.

Dr. Rongxing Lu
Dr. Yunguo Guan
Dr. Xichen Zhang
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.

Keywords

  • Internet of Things
  • sensing
  • cloud computing
  • wireless network security

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Published Papers (1 paper)

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Research

19 pages, 964 KiB  
Article
Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques
by Muhammad Bisri Musthafa, Samsul Huda, Yuta Kodera, Md. Arshad Ali, Shunsuke Araki, Jedidah Mwaura and Yasuyuki Nogami
Sensors 2024, 24(13), 4293; https://doi.org/10.3390/s24134293 - 1 Jul 2024
Cited by 1 | Viewed by 1655
Abstract
Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial [...] Read more.
Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs’ ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models’ performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model’s ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively. Full article
(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing—2nd Edition)
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