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The Internet of Things—Current Trends, Applications, and Future Challenges (2nd...
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The Internet of Things—Current Trends, Applications, and Future Challenges (2nd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 687

Special Issue Editors

Dr. Sergio Correia

E-Mail Website
Guest Editor
1. Center of Technology and Systems (UNINOVA-CTS) and Associated Lab of Intelligent Systems (LASI), 2829-516 Caparica, Portugal
2. VALORIZA-Research Centre for Endogenous Resource Valorization, Instituto Politécnico de Portalegre, Portalegre, Portugal
3. Cognitive and People-Centric Computing Laboratories (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal
Interests: embedded artificial intelligence, soft computing, embedded systems, and computer architecture
Special Issues, Collections and Topics in MDPI journals
Dr. João Pedro Matos-Carvalho

E-Mail Website
Guest Editor
1. Center of Technology and Systems (UNINOVA-CTS) and Associated Lab of Intelligent Systems (LASI), 2829-516 Caparica, Portugal
2. Cognitive and People-Centric Computing Laboratories (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal
Interests: computer vision, artificial intelligence, image processing, aerial robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) paradigm has changed traditional living into a high-tech lifestyle. Pervasive environments that interconnect many heterogeneous physical objects (or things) to enhance the efficiency of everyday tasks are becoming common in an increasing number of fields of application. However, many open challenges and obstacles still need to be addressed to achieve the full potential of the IoT. These challenges and combined issues must be considered from various aspects of the IoT, such as applications, challenges, enabling technologies, or social and environmental impacts.

This Special Issue aims to bring together researchers and professionals to discuss a collection of ideas on the advancements in technology, application areas, or social and environmental impacts that the IoT currently faces and to state possible future challenges and directions of research.

Dr. Sergio Correia
Dr. João Pedro Pedro Matos-Carvalho
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. Computers 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 1800 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 (IoT)
  • artificial intelligence and machine learning
  • blockchain
  • privacy and security
  • industry 4.0
  • cloud computing
  • big data
  • embedded computing
  • sensor networks
  • distributed computing
  • green computing and energy efficiency
  • internet of Wearable Things
  • healthcare
  • testbed and experimental results for the IoT
  • smart sensors
  • drones and UAVs
  • 5G and beyond-5G networks
  • software-defined wireless access networks
  • pervasive computing
  • social and environmental impacts

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Published Papers (2 papers)

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23 pages, 11427 KiB  
Article
Kalman Filter-Enhanced Data Aggregation in LoRaWAN-Based IoT Framework for Aquaculture Monitoring in Sargassum sp. Cultivation
by Misbahuddin Misbahuddin, Nunik Cokrowati, Muhamad Syamsu Iqbal, Obie Farobie, Apip Amrullah and Lusi Ernawati
Computers 2025, 14(4), 151; https://doi.org/10.3390/computers14040151 - 18 Apr 2025
Viewed by 191
Abstract
This study presents a LoRaWAN-based IoT framework for robust data aggregation in Sargassum sp. cultivation, integrating multi-sensor monitoring and Kalman filter-based data enhancement. The system employs water quality sensors—including temperature, salinity, light intensity, dissolved oxygen, total dissolved solids, and pH—deployed in 6 out [...] Read more.
This study presents a LoRaWAN-based IoT framework for robust data aggregation in Sargassum sp. cultivation, integrating multi-sensor monitoring and Kalman filter-based data enhancement. The system employs water quality sensors—including temperature, salinity, light intensity, dissolved oxygen, total dissolved solids, and pH—deployed in 6 out of 14 cultivation containers. Sensor data are transmitted via LoRaWAN to The Things Network (TTN) and processed through an MQTT-based pipeline in Node-RED before visualization in ThingSpeak. The Kalman filter is applied to improve data accuracy and detect faulty sensor readings, ensuring reliable aggregation of environmental parameters. Experimental results demonstrate that this approach effectively maintains optimal cultivation conditions, reducing ecological risks such as eutrophication and improving Sargassum sp. growth monitoring. Findings indicate that balanced light intensity plays a crucial role in photosynthesis, with optimally exposed containers exhibiting the highest survival rates and biomass. However, nutrient supplementation showed limited impact due to uneven distribution, highlighting the need for improved delivery systems. By combining real-time monitoring with advanced data processing, this framework enhances decision-making in sustainable aquaculture, demonstrating the potential of LoRaWAN and Kalman filter-based methodologies for environmental monitoring and resource management. Full article
(This article belongs to the Special Issue The Internet of Things—Current Trends, Applications, and Future Challenges (2nd Edition))
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30 pages, 3310 KiB  
Article
Enhancing Scalability and Network Efficiency in IOTA Tangle Networks: A POMDP-Based Tip Selection Algorithm
by Mays Alshaikhli, Somaya Al-Maadeed and Moutaz Saleh
Computers 2025, 14(4), 117; https://doi.org/10.3390/computers14040117 - 24 Mar 2025
Viewed by 445
Abstract
The fairness problem in the IOTA (Internet of Things Application) Tangle network has significant implications for transaction efficiency, scalability, and security, particularly concerning orphan transactions and lazy tips. Traditional tip selection algorithms (TSAs) struggle to ensure fair tip selection, leading to inefficient transaction [...] Read more.
The fairness problem in the IOTA (Internet of Things Application) Tangle network has significant implications for transaction efficiency, scalability, and security, particularly concerning orphan transactions and lazy tips. Traditional tip selection algorithms (TSAs) struggle to ensure fair tip selection, leading to inefficient transaction confirmations and network congestion. This research proposes a novel partially observable Markov decision process (POMDP)-based TSA, which dynamically prioritizes tips with lower confirmation likelihood, reducing orphan transactions and enhancing network throughput. By leveraging probabilistic decision making and the Monte Carlo tree search, the proposed TSA efficiently selects tips based on long-term impact rather than immediate transaction weight. The algorithm is rigorously evaluated against seven existing TSAs, including Random Walk, Unweighted TSA, Weighted TSA, Hybrid TSA-1, Hybrid TSA-2, E-IOTA, and G-IOTA, under various network conditions. The experimental results demonstrate that the POMDP-based TSA achieves a confirmation rate of 89–94%, reduces the orphan tip rate to 1–5%, and completely eliminates lazy tips (0%). Additionally, the proposed method ensures stable scalability and high security resilience, making it a robust and efficient solution for decentralized ledger networks. These findings highlight the potential of reinforcement learning-driven TSAs to enhance fairness, efficiency, and robustness in DAG-based blockchain systems. This work paves the way for future research into adaptive and scalable consensus mechanisms for the IOTA Tangle. Full article
(This article belongs to the Special Issue The Internet of Things—Current Trends, Applications, and Future Challenges (2nd Edition))
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