Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Feature Papers in the Internet of Things Section 2024

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issues
Published Papers

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 28219

Share This Special Issue

Special Issue Editor

Prof. Dr. Klaus Moessner

E-Mail Website1 Website2
Guest Editor

1. Faculty of Electrical Engineering and Information Technology, Institute for Information Technology, Technische Universität Chemnitz, Str. der Nationen 62, 09111 Chemnitz, Germany
2. Department of Electrical and Electronic Engineering, Institute for Communication Systems, University of Surrey, Guildford GU2 7XH, Surrey, UK
Interests: reliable communications; cognitive networks; IoT deployments; sensor data fusion; situation awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the section Internet of Things is now compiling a collection of papers submitted by Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from EBMs.

We expect original papers and review articles that show state-of-the-art, theoretical, and applicative advances, new experimental discoveries, and novel technological improvements regarding the Internet of Things. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collated into a printed-edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more excellent scholars to join the section Internet of Things so that we can work together to further develop this exciting field of research.

Prof. Dr. Klaus Moessner
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 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 multimedia things
industrial Internet of Things (IIoT)
underwater IoT communication and networks
machine-type communications
low-power and energy harvesting technologies
real-time systems for the IoT
service middleware and device management for the IoT
privacy, security, and trust in IoT systems
cyber–physical system (CPS) platforms
edge/fog/cloud computing in the IoT
data management and mining platforms for the IoT
IoT architectures and standards
future internet design for the IoT
IoT pilots and testbeds
5G and beyond 5G architectures and protocols for the IoT
AI/ML and distributed intelligence for the IoT
IoT applications and uses (smart factory, smart city, smart health, smart transportation, and smart agriculture)

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.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issues

Feature Papers in the Internet of Things Section 2023 in Sensors (8 articles)
Feature Papers in the Internet of Things Section 2025 in Sensors (5 articles)

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

31 pages, 17034 KiB

Open AccessArticle

IoT-Enabled Real-Time Monitoring of Urban Garbage Levels Using Time-of-Flight Sensing Technology

by Luis Miguel Pires, João Figueiredo, Ricardo Martins and José Martins

Sensors 2025, 25(7), 2152; https://doi.org/10.3390/s25072152 - 28 Mar 2025

Cited by 1 | Viewed by 1618

Abstract

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

16 pages, 2004 KiB

Open AccessArticle

DC-NFC: A Custom Deep Learning Framework for Security and Privacy in NFC-Enabled IoT

by Abdul Rehman, Omar Alharbi, Yazeed Qasaymeh and Amer Aljaedi

Sensors 2025, 25(5), 1381; https://doi.org/10.3390/s25051381 - 24 Feb 2025

Viewed by 886

Abstract

NFC has emerged as a critical technology in IoET ecosystems, facilitating seamless data exchange in proximity-based systems. However, the security and privacy challenges associated with NFC-enabled IoT devices remain significant, exposing them to various threats such as eavesdropping, relay attacks, and spoofing. This paper introduces DC-NFC, a novel deep learning framework designed to enhance the security and privacy of NFC communications within IoT environments. The proposed framework integrates three innovative components: the CE for capturing intricate temporal and spatial patterns, the PML for enforcing end-to-end privacy constraints, and the ATF module for real-time threat detection and dynamic model adaptation. Comprehensive experiments were conducted on four benchmark datasets—UNSW-NB15, Bot-IoT, TON-IoT Telemetry, and Edge-IIoTset. The results of the proposed approach demonstrate significant improvements in security metrics across all datasets, with accuracy enhancements up to 95% on UNSW-NB15, and consistent F1-scores above 0.90, underscoring the framework’s robustness in enhancing NFC security and privacy in diverse IoT environments. The simulation results highlight the framework’s real-time processing capabilities, achieving low latency of 20.53 s for 1000 devices on the UNSW-NB15 dataset. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

29 pages, 3949 KiB

Open AccessArticle

RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT

by Samia Tasnim, Niki Pissinou, S. Sitharama Iyengar, Kianoosh G. Boroojeni and Kishwar Ahmed

Sensors 2025, 25(4), 1171; https://doi.org/10.3390/s25041171 - 14 Feb 2025

Cited by 1 | Viewed by 832

Abstract

The rapid development of mobile sensing technologies (e.g., smart devices embedded with various powerful sensors) has encouraged the proliferation of the Internet of Things (IoT). Although data reliability and accuracy are crucial in many sensor applications (e.g., air-quality monitoring), it is often difficult to ensure these properties. Mobile IoT’s people-centric architecture allows for more inaccurate and corrupted data. In this manuscript, we are addressing the problem of how to predict data more accurately in the presence of malicious participants who inject false data to manipulate the system. Our goal is to recover those missing or imprecise data values from the correlated data streams. To do so, we propose a Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism that is resilient against on–off and data-corruption attacks. Furthermore, the Contextual Hidden Markov Model-based data prediction facilitates more accurate real-time data prediction. We tested the scenarios where most participants were malicious, injecting false data at varied rates. Our method accurately identified the honest participants based on their reported data and context. We empirically evaluate the performance using Beijing’s air-quality dataset. We compared the performance of our RCoD method against four state-of-the-art methods, and the results justify its superiority. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

19 pages, 1693 KiB

Open AccessArticle

Real-Time Anomaly Detection in Physiological Parameters: A Multi-Squad Monitoring and Communication Architecture

by Razaq Jinad, Khushi Gupta, Damilola Oladimeji, Amar Rasheed and Cihan Varol

Sensors 2025, 25(3), 929; https://doi.org/10.3390/s25030929 - 4 Feb 2025

Cited by 2 | Viewed by 1866

Abstract

In military operations, real-time monitoring of soldiers’ health is essential for ensuring mission success and safeguarding personnel, yet such systems face challenges related to accuracy, security, and resource efficiency. This research addresses the critical need for secure, real-time monitoring of soldier vitals in the field, where operational security and performance are paramount. The paper focuses on implementing a machine-learning-based system capable of predicting the health states of soldiers using vitals such as heart rate (HR), respiratory rate (RESP), pulse, and oxygen saturation SpO₂. A comprehensive pipeline was developed, including data preprocessing, the addition of noise, and model evaluation, to identify the best-performing machine learning algorithm. The system was tested through simulations to ensure real-time inference on real-life data, with reliable and accurate predictions demonstrated in dynamic environments. The gradient boosting model was selected due to its high accuracy, robustness to noise, and ability to handle complex feature interactions efficiently. Additionally, a lightweight cryptographic security system with a 16-byte key was integrated to protect sensitive health and location data during transmission. The results validate the feasibility of deploying such a system in resource-constrained field conditions while maintaining data confidentiality and operational security. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

25 pages, 1815 KiB

Open AccessArticle

Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires

by Abdul Mutakabbir, Chung-Horng Lung, Kshirasagar Naik, Marzia Zaman, Samuel A. Ajila, Thambirajah Ravichandran, Richard Purcell and Srinivas Sampalli

Sensors 2025, 25(3), 792; https://doi.org/10.3390/s25030792 - 28 Jan 2025

Cited by 1 | Viewed by 993

Abstract

Natural disasters are mostly seasonal and caused by anthropological, climatic, and geological factors that impact human life, economy, ecology, and natural resources. This paper focuses on increasingly widespread forest fires, causing greater destruction in recent years. Data obtained from sensors for predicting forest fires and assessing fire severity, i.e., area burned, are multivariate, seasonal, and highly imbalanced with a ratio of 100,000+ non-fire events to 1 fire event. This paper presents Spatio-Temporal Agnostic Sampling (STAS) to overcome the challenge of highly imbalanced data. This paper first presents a mathematical understanding of fire and non-fire events and then a thorough complexity analysis of the proposed STAS framework and two existing methods, NearMiss and SMOTE. Further, to investigate the applicability of STAS, binary classification models (to determine the probability of forest fire) and regression models (to assess the severity of forest fire) were built on the data generated from STAS. A total of 432 experiments were conducted to validate the robustness of the STAS parameters. Additional experiments with a temporal data split were conducted to further validate the results. The results show that 180 of the 216 binary classification models had an

F 1 s c o r e > 0.9

and 150 of the 216 regression models had an

R^{2} s c o r e > 0.75

. These results indicate the applicability of STAS for fire prediction with highly imbalanced multivariate seasonal time series data. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

34 pages, 6387 KiB

Open AccessArticle

CANGuard: An Enhanced Approach to the Detection of Anomalies in CAN-Enabled Vehicles

by Damilola Oladimeji, Razaq Jinad, Amar Rasheed and Mohamed Baza

Sensors 2025, 25(1), 278; https://doi.org/10.3390/s25010278 - 6 Jan 2025

Cited by 2 | Viewed by 1902

Abstract

As modern vehicles continue to evolve, advanced technologies are integrated to enhance the driving experience. A key enabler of this advancement is the Controller Area Network (CAN) bus, which facilitates seamless communication between vehicle components. Despite its widespread adoption, the CAN bus was not designed with security as a priority, making it vulnerable to various attacks. In this paper, we propose CANGuard, an Intrusion Detection System (IDS) designed to detect attacks on the CAN network and identify the originating node in real time. Using a simulated CAN-enabled system with four nodes representing diverse vehicle components, we generated a dataset featuring Denial-of-Service (DoS) attacks by exploiting the arbitration feature of the CAN bus, which prioritizes high-criticality messages (e.g., engine control) over lower-criticality ones (e.g., infotainment). We trained and evaluated several machine learning models for their ability to detect attacks and pinpoint the responsible node. Results indicate that Gradient Boosting outperformed other models, achieving high accuracy in both attack detection and node identification. While the Multi-Layer Perceptron (MLP) model demonstrated strong attack detection performance, it struggled with node identification, achieving less than 50% accuracy. These findings underscore the potential of tree-based models for real-time IDS applications in CAN-enabled vehicles. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

26 pages, 2285 KiB

Open AccessArticle

Formal Security Reassessment of the 5G-AKA-FS Protocol: Methodological Corrections and Augmented Verification Techniques

by Yongho Ko, I Wayan Adi Juliawan Pawana and Ilsun You

Sensors 2024, 24(24), 7979; https://doi.org/10.3390/s24247979 - 13 Dec 2024

Cited by 1 | Viewed by 1095

Abstract

The 5G-AKA protocol, a foundational component for 5G network authentication, has been found vulnerable to various security threats, including linkability attacks that compromise user privacy. To address these vulnerabilities, we previously proposed the 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol, which introduces an ephemeral key pair within the home network (HN) to support forward secrecy and prevent linkability attacks. However, a re-evaluation uncovered minor errors in the initial BAN-logic verification and highlighted the need for more rigorous security validation using formal methods. In this paper, we correct the BAN-logic verification and advance the formal security analysis by applying an extended SVO logic, which was adopted as it provides a higher level of verification compared to BAN logic, incorporating a new axiom specifically for forward secrecy. Additionally, we enhance the ProVerif analysis by employing a stronger adversarial model. These refinements in formal verification validate the security and reliability of 5G-AKA-FS, ensuring its resilience against advanced attacks. Our findings offer a comprehensive reference for future security protocol verification in 5G networks Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

17 pages, 4486 KiB

Open AccessArticle

Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning Model

by Yuansu Zou, Qixian Gao, Hao Wu and Nianbo Liu

Sensors 2024, 24(23), 7461; https://doi.org/10.3390/s24237461 - 22 Nov 2024

Cited by 2 | Viewed by 1061

Abstract

Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs. With a graph attention network structure adopted to describe node locations, accessible paths, and data with collection windows for path planning, it integrates to solve for the optimal distribution routes, taking into account carbon emissions and cooling costs under varying temperatures. Extensive simulation experiments and comparative analyses demonstrate that the proposed time-window-constrained reinforcement learning model provides effective decision-making information for optimizing fresh product fresh food supply chain transportation and distribution, controlling logistics costs, and reducing carbon emissions. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

26 pages, 17800 KiB

Open AccessArticle

MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks

by Rachele Catalano, Myriam Giusy Tibaldi, Lucia Lombardi, Antonella Santone, Mario Cesarelli and Francesco Mercaldo

Sensors 2024, 24(21), 7022; https://doi.org/10.3390/s24217022 - 31 Oct 2024

Viewed by 1360

Abstract

Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

17 pages, 5170 KiB

Open AccessFeature PaperArticle

A Self-Powered Wireless Temperature Sensor Platform for Foot Ulceration Monitoring

by Joseph Agyemang Duah, Kye-Shin Lee and Byung-Gyu Kim

Sensors 2024, 24(20), 6567; https://doi.org/10.3390/s24206567 - 12 Oct 2024

Cited by 3 | Viewed by 3567

Abstract

This work describes a self-powered wireless temperature sensor platform that can be used for foot ulceration monitoring for diabetic patients. The proposed self-powered sensor platform consists of a piezoelectric bimorph, a power conditioning circuit, a temperature sensor readout circuit, and a wireless module. The piezoelectric bimorph mounted inside the shoe effectively converts the foot movement into electric energy that can power the entire sensor platform. Furthermore, a sensor platform was designed, considering the energy requirement of 4.826 mJ for transmitting one data packet of 18 bytes. The self-powered sensor platform prototype was evaluated with five test subjects with different weights and foot shapes; the test results show the subjects had to walk an average of 119.6 s to transmit the first data packet and an additional average of 71.2 s to transmit the subsequent data packet. The temperature sensor showed a resolution of 0.1 °C and a sensitivity of 56.7 mV/°C with a power conditioning circuit efficiency of 74.5%. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

21 pages, 2726 KiB

Open AccessArticle

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

by Alexios Papaioannou, Asimina Dimara, Charalampos S. Kouzinopoulos, Stelios Krinidis, Christos-Nikolaos Anagnostopoulos, Dimosthenis Ioannidis and Dimitrios Tzovaras

Sensors 2024, 24(7), 2125; https://doi.org/10.3390/s24072125 - 26 Mar 2024

Cited by 7 | Viewed by 1871

Abstract

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Figure 1

Review

Jump to: Research

19 pages, 1459 KiB

Open AccessReview

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study

by Umair Khadam, Paul Davidsson and Romina Spalazzese

Sensors 2024, 24(20), 6511; https://doi.org/10.3390/s24206511 - 10 Oct 2024

Cited by 3 | Viewed by 9739

Abstract

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)

► Show Figures

Journal Menu

Journal Browser

Feature Papers in the Internet of Things Section 2024

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issues

Published Papers (12 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI