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IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 8077

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Special Issue Editors

Dr. Manuel José Cabral dos Santos Reis

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Guest Editor

Department of Engineering/IEETA, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: signal & image processing and applications; study and development of devices & systems for friendly smart environments; development of multimedia-based teaching/learning methods and tools, with particular emphasis on the use of the internet
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Carlos Serôdio

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Guest Editor

Department of Engineering, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: wireless sensors networks; precision agriculture; smart cities; indoor localization using wireless networks; 5G & 6G ecosystem to support IoE
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in embedded devices, computing, and sensor and actuator networks have enabled everyday objects to become smart devices, laying the groundwork for the Internet of Things (IoT). In particular, the advancements in wireless communication technology have truly enabled the widespread adoption of the IoT. For example, the introduction of 5G has led to significant improvements in data transfer speed, connection reliability, security, and energy efficiency, driving the rapid expansion of the IoT. This has created a new era of interconnected devices, systems, and data flows. Further technological advancements will unlock new possibilities for the IoT, with artificial intelligence (AI) poised to play a crucial role in the field. Additionally, these developments lead to a vast and ever-growing amount of data in various domains and modalities that is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from the data provided by the IoT devices, further enhancement is needed. The processing of the data itself and the consequent extraction of useful information are also vital and included in the topics of this Special Issue.

This Special Issue of Future Internet aims to highlight advances in the development, testing, and application of IoT for smart environments. Experimental and theoretical results, in as much detail as possible, are very welcome. Review papers are also very welcome. There is no restriction on the length of the papers.

Topics include, but are not limited to, the following:

Advanced IoT characterization techniques;
IoT and blockchains;
IoT and machine learning (e.g., deep learning);
IoT data fusion and integration;
IoT for ambient assisted living;
IoT for biomedical signal and image analysis;
IoT for smart environments and smart cities;
IoT signal and image processing applications;
Real-time IoT algorithms and architectures;
Wearable IoT (including signal processing and its applications).

Dr. Manuel José Cabral dos Santos Reis
Prof. Dr. Carlos Serôdio
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

IoT architecture
IoT for smart environments
IoT mechanisms
IoT approaches
IoT applications

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Published Papers (7 papers)

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Research

26 pages, 2006 KiB

Open AccessArticle

Edge AI for Real-Time Anomaly Detection in Smart Homes

by Manuel J. C. S. Reis and Carlos Serôdio

Future Internet 2025, 17(4), 179; https://doi.org/10.3390/fi17040179 - 18 Apr 2025

Viewed by 477

Abstract

The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. This paper presents an Edge AI-based anomaly detection framework that combines Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) models to identify anomalies in IoT sensor data. The system is evaluated on both synthetic and real-world smart home datasets, including temperature, motion, and energy consumption signals. Experimental results show that LSTM-AE achieves higher detection accuracy (up to 93.6%) and recall but requires more computational resources. In contrast, IF offers faster inference and lower power consumption, making it suitable for constrained environments. A hybrid architecture integrating both models is proposed to balance accuracy and efficiency, achieving sub-50 ms inference latency on embedded platforms such as Raspberry Pi and NVIDEA Jetson Nano. Optimization strategies such as quantization reduced LSTM-AE inference time by 76% and power consumption by 35%. Adaptive learning mechanisms, including federated learning, are also explored to minimize cloud dependency and enhance data privacy. These findings demonstrate the feasibility of deploying real-time, privacy-preserving, and energy-efficient anomaly detection directly on edge devices. The proposed framework can be extended to other domains such as smart buildings and industrial IoT. Future work will investigate self-supervised learning, transformer-based detection, and deployment in real-world operational settings. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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15 pages, 1635 KiB

Open AccessArticle

Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing

by David Ortiz-Perez, Pablo Ruiz-Ponce, David Mulero-Pérez, Manuel Benavent-Lledo, Javier Rodriguez-Juan, Hugo Hernandez-Lopez, Anatoli Iarovikov, Srdjan Krco, Daliborka Nedic, Dejan Vukobratovic and Jose Garcia-Rodriguez

Future Internet 2025, 17(2), 53; https://doi.org/10.3390/fi17020053 - 21 Jan 2025

Viewed by 761

Abstract

The rapid loss of biodiversity significantly impacts birds’ environments and behaviors, highlighting the importance of analyzing bird behavior for ecological insights. With the growing adoption of Machine Learning (ML) algorithms in the Internet of Things (IoT) domain, edge computing has become essential to ensure data privacy and enable real-time predictions by processing high-dimensional data, such as video streams, efficiently. This paper introduces a set of dimensionality reduction techniques tailored for video sequences based on cutting-edge methods for this data representation. These methods drastically compress video data, reducing bandwidth and storage requirements while enabling the creation of compact ML models with faster inference speeds. Comprehensive experiments on bird behavior classification in rural environments demonstrate the effectiveness of the proposed techniques. The experiments incorporate state-of-the-art deep learning techniques, including pre-trained video vision models, Autoencoders, and single-frame feature extraction. These methods demonstrated superior performance to the baseline, achieving up to a 6000-fold reduction in data size while reaching a classification accuracy of 60.7% on the Visual WetlandBirds Dataset and obtaining state-of-the-art performance on this dataset. These findings underline the potential of using dimensionality reduction to enhance the scalability and efficiency of bird behavior analysis. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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23 pages, 6742 KiB

Open AccessArticle

Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things

by Sumaiah Algarni and Fathi E. Abd El-Samie

Future Internet 2025, 17(1), 37; https://doi.org/10.3390/fi17010037 - 15 Jan 2025

Viewed by 1084

Abstract

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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41 pages, 6955 KiB

Open AccessArticle

Framework Design for the Dynamic Reconfiguration of IoT-Enabled Embedded Systems and “On-the-Fly” Code Execution

by Elmin Marevac, Esad Kadušić, Nataša Živić, Nevzudin Buzađija and Samir Lemeš

Future Internet 2025, 17(1), 23; https://doi.org/10.3390/fi17010023 - 7 Jan 2025

Viewed by 1161

Abstract

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and “on-the-fly” code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A “hardware-in-the-loop” workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM’s ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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26 pages, 8972 KiB

Open AccessArticle

IoT-Based LPG Level Sensor for Domestic Stationary Tanks with Data Sharing to a Filling Plant to Optimize Distribution Routes

by Roberto Morales-Caporal, Rodolfo Eleazar Pérez-Loaiza, Edmundo Bonilla-Huerta, Julio Hernández-Pérez and José de Jesús Rangel-Magdaleno

Future Internet 2024, 16(12), 479; https://doi.org/10.3390/fi16120479 - 21 Dec 2024

Viewed by 828

Abstract

This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user’s app and on the gas company’s web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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20 pages, 308 KiB

Open AccessArticle

Enhancing Autonomous Vehicle Safety with Blockchain Technology: Securing Vehicle Communication and AI Systems

by Stefan Iordache, Catalina Camelia Patilea and Ciprian Paduraru

Future Internet 2024, 16(12), 471; https://doi.org/10.3390/fi16120471 - 18 Dec 2024

Viewed by 1447

Abstract

In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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20 pages, 2570 KiB

Open AccessArticle

A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge

by Jinpeng Miao, Dasari Rajasekhar, Shivakant Mishra, Sanjeet Kumar Nayak and Ramanarayan Yadav

Future Internet 2024, 16(8), 296; https://doi.org/10.3390/fi16080296 - 16 Aug 2024

Cited by 1 | Viewed by 1431

Abstract

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers’ prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems. Full article

(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)

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