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Semantic Communication for the Internet of Things
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Semantic Communication for the Internet of Things

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

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 994

Special Issue Editor

Dr. Samer Lahoud

E-Mail Website
Guest Editor
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
Interests: wireless communications; cellular networks; Internet of Things; network energy efficiency

Special Issue Information

Dear Colleagues,

Semantic communication redefines how Internet of Things (IoT) devices generate, convey, and interpret information by focusing on the conveyed meaning rather than on traditional bit accuracy. This paradigm promises to alleviate bandwidth scarcity, reduce energy consumption, and enable robust operation in noisy or resource-constrained environments by transmitting only context-relevant semantic features. Recent advances in edge intelligence, knowledge graphs, large language and vision models, and goal-oriented networking create fertile ground for integrating semantics throughout the IoT protocol stack.

For this Special Issue of Sensors, we welcome high-quality original research and comprehensive surveys that advance theory, algorithms, and implementations of semantic communication for IoT applications. Topics of interest include, but are not limited to, semantic source and channel coding, ontology-driven sensing, cross-layer semantic metrics, intent-based networking, joint perception–communication design, semantic distortion and reliability analysis, distributed representation learning at the edge, task-oriented federated learning, semantic middleware for heterogeneous sensors, security and privacy of semantic exchange, standardization efforts, and real-world testbeds or datasets. Submissions should articulate clear novelty, methodological rigor, and practical relevance, highlighting how semantic principles enhance efficiency, reliability, or interpretability in IoT systems. The Special Issue aims to bring together researchers from communications, signal processing, artificial intelligence, and domain-specific IoT fields to shape the next generation of meaning-centric networks.

Dr. Samer Lahoud
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • semantic communication
  • Internet of Things
  • edge intelligence
  • semantic source coding
  • intent-based networking
  • ontology-driven sensing
  • federated learning
  • cross-layer metrics
  • task-oriented communication
  • energy-efficient IoT

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Published Papers (1 paper)

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21 pages, 1220 KB  
Article
ML-FSID-FIS: A Multi-Level Feature Selection and Fuzzy Inference System for Intrusion Detection in IoMT
by Ghaida Balhareth, Mohammad Ilyas and Basmh Alkanjr
Sensors 2026, 26(8), 2501; https://doi.org/10.3390/s26082501 - 18 Apr 2026
Viewed by 394
Abstract
The Internet of Medical Things (IoMT) is becoming a vital part of modern healthcare, enabling ongoing patient monitoring and remote diagnosis. However, as more devices connect to the internet, healthcare systems become more vulnerable to serious security issues such as unauthorized access, patient [...] Read more.
The Internet of Medical Things (IoMT) is becoming a vital part of modern healthcare, enabling ongoing patient monitoring and remote diagnosis. However, as more devices connect to the internet, healthcare systems become more vulnerable to serious security issues such as unauthorized access, patient data manipulation, and Man-in-the-Middle attacks. Conventional Intrusion Detection Systems (IDSs) often struggle with the unclear and uncertain characteristics of IoMT traffic, which leads to reduced detection accuracy and increased false alarms. To address these challenges, this paper proposes ML-FSID-FIS, a multi-level feature selection-based Intrusion Detection System that employs a fuzzy inference system (FIS) for classification in IoMT networks. The model combines multiple feature selection techniques into a three-stage multi-level feature selection strategy to improve detection efficiency and strengthen the security of IoMT networks. In the first stage, four feature selection techniques—Random Forest, XGBoost, ReliefF, and Mutual Information—are applied to identify the most relevant features. In the second stage, a frequency-based consensus strategy is utilized to extract consistently selected features from the four top-ranked sets. In the third stage, an ensemble refinement using bagging-based ranking is employed to rank the remaining features, resulting in the selection of the top five features. From these, three candidate 3-feature groups are formed and evaluated, and the best-performing group is selected as the final input set for the fuzzy logic classifier. The FIS produces a continuous risk score that is mapped to a binary decision using a validation-selected threshold. When the proposed method was tested on the WUSTL-EHMS-2020 dataset and compared with other recent work using the same dataset, it showed strong detection performance while maintaining a very low false positive rate of 0.3%. This study is distinguished by its integrated design, which combines a three-stage multi-level feature selection strategy with fuzzy logic-based intrusion classification to improve feature efficiency and support interpretable intrusion detection in IoMT. Full article
(This article belongs to the Special Issue Semantic Communication for the Internet of Things)
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