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Future Internet
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Wireless Sensor Networks and Internet of Things
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Wireless Sensor Networks and Internet of Things

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4858

Special Issue Editors

Dr. Pranav Gangwani

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Guest Editor
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: generative AI; deep learning; IoT; blockchain; wireless sensor networks
Dr. Alexander Perez-Pons

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Guest Editor
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: IoT; blockchain; cybersecurity; wireless sensor networks; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) are two modern and crucial technologies in today’s era of digital connectivity. WSNs are equipped with numerous sensors that collect, process, and transmit surrounding data from different locations to centralized devices. This capability is extended by IoT by promoting a seamless network of connected devices that communicate across the internet. Together, these technologies open numerous opportunities for monitoring, automation, and smart control in various sectors, making them critical for technological research.

This Special Issue aims to explore the key developments, applications, and challenges of WSNs and IoT technologies. It seeks to collect significant research solutions that not only address the inherent security risks and threats these technologies face but also to propose novel contributions and enhancements. The idea is to promote deep understanding and show that WSNs and IoT can be integrated for enhancing efficiency, security, and functionality.

We invite researchers and practitioners to submit their original research articles, comprehensive reviews, and case studies. Submissions should not have been published previously nor be under consideration for publication elsewhere. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Innovative Architectures for IoT and WSNs: Designs that enhance scalability, reliability, and energy efficiency.
  • Security and Privacy Challenges: Solutions for securing devices and protecting data within IoT and WSN ecosystems.
  • Advanced IoT Integration: Methods for integrating complex systems and legacy technologies into contemporary IoT frameworks.
  • Machine Learning and AI Applications: Leveraging AI to improve network intelligence and operational efficiency.
  • Environmental Monitoring: Novel applications and techniques in WSNs for tracking and analyzing environmental changes.
  • Healthcare Applications: IoT and WSN innovations for telemedicine and remote health monitoring.
  • Smart City Applications: Integration of IoT and WSNs in urban development for enhanced civic management and services.
  • Agricultural and Industrial IoT: Case studies and research on IoT applications in agriculture and industry for increased productivity.
  • Trust Management: Calculating trust in WSNs to identify malicious or faulty sensor nodes.
  • Cloud Computing for WSNs and IoT: Investigating the integration of cloud computing to enhance data storage, processing, and analytics capabilities for Wireless Sensor Networks and IoT.
  • Blockchain for IoT Security: Utilizing blockchain technology to enhance security and trust in IoT networks, especially for critical infrastructures and smart contracts.
  • WSNs and IoT in Logistics and Supply Chain Management: Implementing IoT to enhance transparency, efficiency, and management in supply chains.

Dr. Pranav Gangwani
Dr. Alexander Perez-Pons
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • WSNs
  • IoT
  • 5G/6G communication networks
  • artificial intelligence
  • trust management
  • cloud computing
  • edge computing
  • fog computing
  • wireless body area networks
  • Industry 4.0

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Published Papers (5 papers)

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29 pages, 1644 KB  
Article
Commercial Off-the-Shelf IoT-Based Infant Car Seat Application for Preventing the Forgotten Baby Syndrome
by Apostolos Panagiotopoulos and Vasileios Karyotis
Future Internet 2025, 17(10), 443; https://doi.org/10.3390/fi17100443 - 29 Sep 2025
Viewed by 319
Abstract
The Forgotten Baby Syndrome (FBS), the accidental abandonment of infants in vehicles, continues to result in otherwise preventable tragedies worldwide. This work presents a prototype system called SafeCuddle, designed to mitigate the risks associated with FBS. The proposed solution utilizes an Arduino [...] Read more.
The Forgotten Baby Syndrome (FBS), the accidental abandonment of infants in vehicles, continues to result in otherwise preventable tragedies worldwide. This work presents a prototype system called SafeCuddle, designed to mitigate the risks associated with FBS. The proposed solution utilizes an Arduino UNO R4 WiFi microcontroller integrated with low-cost IoT sensors for real-time data acquisition and processing. Processed signals are visualized via a Python-based desktop application. A key feature of the system is its ability to issue immediate alerts to the driver upon detecting their departure from the vehicle while an infant remains seated in the vehicle. An extensive review of the syndrome’s etiology identifies disrupted routines and the high demands of modern life as primary contributing factors. In response, the proposed system can be easily implemented with commercial off-the-shelf components and aims to support caregivers by acting as a fail-safe mechanism. The paper is structured into two primary sections: (i) an analytical overview of FBS and its contributing factors and (ii) a detailed account of the system’s design, implementation, operational workflow, and evaluation results. The unique contribution of this work lies in the integration of a low-cost, real-time alert system within a modular and easily deployable architecture that can be integrated in existing infant car seats as an aftermarket solution, if properly commercialized, specifically tailored to prevent FBS through immediate driver feedback at the critical moment of risk. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
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19 pages, 1718 KB  
Article
Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning
by Jingshi Qian, Nobuyoshi Komuro, Won-Suk Kim and Younghwan Yoo
Future Internet 2025, 17(10), 440; https://doi.org/10.3390/fi17100440 - 26 Sep 2025
Viewed by 302
Abstract
Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and [...] Read more.
Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
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34 pages, 7213 KB  
Article
Design and Implementation of a Scalable LoRaWAN-Based Air Quality Monitoring Infrastructure for the Kurdistan Region of Iraq
by Nasih Abdulkarim Muhammed and Bakhtiar Ibrahim Saeed
Future Internet 2025, 17(9), 388; https://doi.org/10.3390/fi17090388 - 28 Aug 2025
Viewed by 788
Abstract
Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has [...] Read more.
Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has a major environmental issue in that it ranks second worst in 2022 due to the high level of particulate matter, specifically PM2.5. In this paper, a LoRa-based infrastructure for environmental monitoring in the Kurdistan Region has been designed and developed. The infrastructure encompasses end-node devices, an open-source network server, and an IoT platform. Two AirQNodes were prototyped and deployed to measure particulate matter values, temperature, humidity, and atmospheric pressure using manufacturer-calibrated PM sensors and combined temperature, humidity, and atmospheric sensors. An open-source network server is adopted to manage the AirQNodes and the entire network. In addition, an IoT platform has also been designed and implemented to visualize and analyze the collected data. The platform processes and stores the data, making it accessible for the public and decision-making parties. The infrastructure was tested and results validated by deploying two AirQNodes at separate locations adjacent to existing air quality monitoring stations as reference points. The findings demonstrated that the AirQNodes reliably mirrored the trends and patterns observed in the reference monitors. This research establishes a comprehensive infrastructure for monitoring air quality in the Kurdistan Region of Iraq. Furthermore, complete ownership of the system can be attained by possessing and overseeing the critical components of the infrastructure, which encompass the end devices, network server, and IoT platform. This integrated strategy is especially crucial for the Kurdistan Region of Iraq, where cost-efficiency and enduring sustainability are vital, yet such a structure is absent. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
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26 pages, 11496 KB  
Article
Parallel Algorithm for NP-Hard Problem of Channel Resource Allocation Optimization in Ad Hoc and Sensor Networks
by Valeriy Ivanov and Maxim Tereshonok
Future Internet 2025, 17(8), 362; https://doi.org/10.3390/fi17080362 - 8 Aug 2025
Viewed by 588
Abstract
This paper proposes a technique to estimate the minimal quantity of orthogonal channel resources required for ad hoc and sensor network connectivity. Simultaneously, the resource allocation to each specific line is conducted by grouping lines into concurrent transmission sets. Our proposed technique uses [...] Read more.
This paper proposes a technique to estimate the minimal quantity of orthogonal channel resources required for ad hoc and sensor network connectivity. Simultaneously, the resource allocation to each specific line is conducted by grouping lines into concurrent transmission sets. Our proposed technique uses the physical-based interference model assumption, where each node interferes with every other node, turning ad hoc and sensor network performance optimization problems into the NP-hard ones. In contrast to most of the other works with the physical-based interference model assumption, we mitigate the combinatorial explosion of concurrently transmitting line sets considering the global interference instead of localizing the interference with line or space partitioning. We have performed the mitigation, firstly, using pairwise mutually acceptable line sets for each line. Then, based on the limitations of pairwise sets, we construct the tree of mutually acceptable interfering line sets. Then, from the created tree, we find the minimal set cover of concurrently transmitting line sets. The tree construction has the exponential worst-case time and space complexity if all lines in the network can transmit together. By randomly pruning the tree and using the genetic algorithm to find the pruned tree which gives the same minimal set cover as the full tree, we have reduced the worst space and time complexities to the polynomial ones. We have devised our technique with parallelism in mind to speed up the resource allocation optimization even more. Based on an extensive simulation study with random network topologies of sizes up to 250 nodes and the average number of lines up to 2000, we estimated the time and space complexity for different tree pruning and optimization techniques and found the effective ones. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
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12 pages, 386 KB  
Article
A Transformer-Based Autoencoder with Isolation Forest and XGBoost for Malfunction and Intrusion Detection in Wireless Sensor Networks for Forest Fire Prediction
by Ahshanul Haque and Hamdy Soliman
Future Internet 2025, 17(4), 164; https://doi.org/10.3390/fi17040164 - 9 Apr 2025
Cited by 4 | Viewed by 2321
Abstract
Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring and early forest fire detection. However, they are susceptible to sensor malfunctions and network intrusions, which can compromise data integrity and lead to false alarms or missed detections. This study presents a [...] Read more.
Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring and early forest fire detection. However, they are susceptible to sensor malfunctions and network intrusions, which can compromise data integrity and lead to false alarms or missed detections. This study presents a hybrid anomaly detection framework that integrates a Transformer-based Autoencoder, Isolation Forest, and XGBoost to effectively classify normal sensor behavior, malfunctions, and intrusions. The Transformer Autoencoder models spatiotemporal dependencies in sensor data, while adaptive thresholding dynamically adjusts sensitivity to anomalies. Isolation Forest provides unsupervised anomaly validation, and XGBoost further refines classification, enhancing detection precision. Experimental evaluation using real-world sensor data demonstrates that our model achieves 95% accuracy, with high recall for intrusion detection, minimizing false negatives. The proposed approach improves the reliability of WSN-based fire monitoring by reducing false alarms, adapting to dynamic environmental conditions, and distinguishing between hardware failures and security threats. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
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