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Applied Sciences
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Advances in Wireless Sensor Networks and the Internet of Things
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Advances in Wireless Sensor Networks and the Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 2931

Special Issue Editors

Dr. Kashif Naseer Qureshi

E-Mail Website
Guest Editor
Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
Interests: vehicular ad hoc networks; wireless sensor networks; computer network
Special Issues, Collections and Topics in MDPI journals
Dr. Thomas Newe

E-Mail Website
Guest Editor
Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
Interests: wireless sensor metworks; ad hoc networks; information security; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is one of the areas based on smart networks where interconnected and autonomously managed interactive devices and sensor nodes are connected for different services. The main objective of these networks is to intelligently sense and monitor the data and forward them for further processing. Edge- and cloud-based networks are connected with IoT networks to facilitate users by using wireless and wireless mediums. Routing and data communication is one of the fundamental requirements for IoT networks. Routing is always one of the significant areas for these networks. Advanced communication technologies are adopted such as Wi-Fi, 5G, and short- and long-range communication technologies. However, traditional wired network solutions do not perform well against more complex IoT services and massive data processing. This Special Issue covers the IoT network requirements, data center networks, massively parallel mining networks, hyperconnected networks, and edge-based analytics networks protocols and communication standards. Additionally, review articles which provide detailed 5G mobile network integration for high-speed data communication in IoT networks are also welcome. The potential topics include but are not limited to:

  • Routing protocols for IoT and sensor networks;
  • Layer-based data communication solutions;
  • New emerging standards and architectures for IoT and sensor networks;
  • New applications and services for IoT and sensor networks;
  • Green energy-efficient protocols for IoT and WSN;
  • Energy harvesting/scavenging for WSN and IoT;
  • Security, trust, and privacy architectures for WSN and IoT;
  • Future trends for IoT and WSN networks;
  • IoT resource management and monitoring;
  • IoT platforms for smart homes and industries;
  • Big data analytics and distributed networks;
  • Blockchain solutions for IoT and WSN networks;
  • SDN and edge networks for IoT and WSN networks.

Dr. Kashif Naseer Qureshi
Dr. Thomas Newe
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. Applied Sciences 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 2400 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

  • routing protocols for IoT and sensor networks
  • layer-based data communication solutions
  • new emerging standards and architectures for IoT and sensor networks
  • new applications and services for IoT and sensor networks
  • green energy-efficient protocols for IoT and WSN
  • energy harvesting/scavenging for WSN and IoT
  • security, trust, and privacy architectures for WSN and IoT
  • future trends for IoT and WSN networks
  • IoT resource management and monitoring
  • IoT platforms for smart homes and industries
  • big data analytics and distributed networks
  • blockchain solutions for IoT and WSN networks
  • SDN and edge networks for IoT and WSN networks

Published Papers (2 papers)

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Research

18 pages, 2144 KiB  
Article
Efficient Node Insertion Algorithm for Connectivity-Based Multipolling MAC Protocol in Wi-Fi Sensor Networks
by Woo-Yong Choi
Appl. Sci. 2023, 13(21), 11974; https://doi.org/10.3390/app132111974 - 02 Nov 2023
Cited by 1 | Viewed by 1058
Abstract
Since low-power Wi-Fi sensors are connected to the Internet, effective radio spectrum use is crucial for developing an efficient Medium Access Control (MAC) protocol for Wi-Fi sensor networks. A connectivity-based multipolling mechanism was employed for Access Points to grant uplink transmission opportunities to [...] Read more.
Since low-power Wi-Fi sensors are connected to the Internet, effective radio spectrum use is crucial for developing an efficient Medium Access Control (MAC) protocol for Wi-Fi sensor networks. A connectivity-based multipolling mechanism was employed for Access Points to grant uplink transmission opportunities to Wi-Fi nodes with a reduced number of multipolling frame transmissions. The existing connectivity-based multipolling mechanism in IEEE 802.11 wireless LANs with many nodes may require excessive time to derive the optimal number of serially connected sequences due to the backtracking algorithm based on the Traveling Salesman Problem model. This limitation hinders the real-time implementation of the connectivity-based multipolling mechanism in Wi-Fi sensor networks. In this study, an efficient node insertion algorithm is proposed, by which the number of derived serially connected multipolling sequences that cover nodes in Wi-Fi sensor networks converges to only one as the number of Wi-Fi sensors increases in Wi-Fi sensor networks. As verified by simulation experiments for Wi-Fi sensor networks, the proposed node insertion algorithm produces a near-optimal number of multipolling sequences that cover the nodes in Wi-Fi sensor networks. This study proposes a node insertion algorithm for the real-time implementation of the connectivity-based multipolling mechanism in MAC protocol for Wi-Fi sensor networks. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks and the Internet of Things)
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16 pages, 1860 KiB  
Article
Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection
by Bader Alwasel, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti and Mohammed Alsuhaibani
Appl. Sci. 2023, 13(13), 7774; https://doi.org/10.3390/app13137774 - 30 Jun 2023
Cited by 1 | Viewed by 1222
Abstract
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to [...] Read more.
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks and the Internet of Things)
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