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Future Internet
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Joint Design and Integration in Smart IoT Systems
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Joint Design and Integration in Smart IoT Systems

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

Deadline for manuscript submissions: closed (10 April 2025) | Viewed by 9119

Special Issue Editors

Prof. Dr. Wei Yu

grade E-Mail Website
Guest Editor
Department of Computer & Information Sciences, Towson University, Towson 21252, MD, USA
Interests: cyber security and privacy; data- and machine-learning-driven applications in cyber-physical systems/Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Guobin Xu

E-Mail Website
Guest Editor
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
Interests: cyber-physical systems; Internet of Things; computer security; quantum cryptography; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of Internet of Things (IoT) technology spans various application domains (e.g., transportation, energy, healthcare, manufacturing, and agriculture). A smart system empowered by IoT technology (also called a smart IoT system) integrates sensing, communication, and computation components into one system. The architecture of smart systems is composed of physical, communication, and computation layers. The physical layer comprises heterogeneous devices that facilitate sensing and actuation and capture and preprocess data from the surrounding environment. The communication layer enables efficient and reliable data transmission among components by leveraging network protocols, standards, and technologies. The computation layer provides intelligent decision making and automation through the use of edge/cloud computing and AI technologies.

Smart systems are large, distributed, and complex systems, empowered through diverse IoT technologies and involving the integration and management of heterogeneous components in different layers. However, there are numerous challenges associated with the design, integration, management, and coordination of smart systems. Developing cross-component and cross-layer architectures, standards, and frameworks is necessary to support efficient smart systems. Likewise, the scalability, interoperability, connectivity, security, and resilience requirements of smart systems must also be carefully considered. The papers in this Special Issue address the above challenges and propose solutions for the joint design and integration of smart systems at various levels.

We invite original research papers that focus on, but are not limited to, the following topics related to smart systems:

  • Integration design architecture and frameworks;
  • Co-design of sensing and communication components;
  • Co-design of communication and computing components;
  • Co-design of sensing and computing components;
  • Co-design of sensing, communication, and computing components;
  • AI-assisted co-design of sensing, communication, and computing components;
  • Modeling and simulation environments for evaluating co-design methodologies;
  • Security and privacy in the co-design and integration of smart systems;
  • Data science, engineering, and practice in the co-design and integration of smart systems;
  • Machine learning models and architectures in the co-design and integration of smart systems.

Prof. Dr. Wei Yu
Dr. Guobin Xu
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

  • Internet of Things
  • smart systems
  • integration design architecture
  • co-design modeling and simulation
  • machine learning and data science/engineering in IoT
  • security in IoT

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Related Special Issue

Published Papers (7 papers)

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Research

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30 pages, 5361 KiB  
Article
Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
by Negin Jahanbakhsh, Mario Vega-Barbas, Iván Pau, Lucas Elvira-Martín, Hirad Moosavi and Carolina García-Vázquez
Future Internet 2025, 17(5), 198; https://doi.org/10.3390/fi17050198 (registering DOI) - 29 Apr 2025
Abstract
The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, [...] Read more.
The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
21 pages, 3321 KiB  
Article
A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles
by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada and Easa Alalwany
Future Internet 2025, 17(3), 131; https://doi.org/10.3390/fi17030131 - 19 Mar 2025
Viewed by 232
Abstract
Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the [...] Read more.
Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the sheer volume of traffic data that needs to be processed in real-time. Thus, it is not scalable and lacks fault tolerance and data privacy. This study designs and evaluates a scalable distributed machine learning-based scheme to predict highway traffic flows in real-time. The proposed system is segment-based where the vehicles in each segment form a cluster. We train and validate a local Random Forest Regression (RFR) model for each vehicle’s cluster (highway-segment) using six different hyper parameters. Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. Kappa Architecture is utilized to enable real-time prediction. The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R2), and Adjusted R-Squared (AR2). The proposed scheme demonstrates high accuracy in predicting abnormal traffic flows while maintaining scalability and data privacy. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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19 pages, 1230 KiB  
Article
A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
by Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè
Future Internet 2025, 17(3), 116; https://doi.org/10.3390/fi17030116 - 6 Mar 2025
Viewed by 413
Abstract
Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar [...] Read more.
Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar measures to represent different dimensions of trust, which are then integrated into a single global trustworthiness value. Nevertheless, this scalar approach within the IoT context holds a few limitations that emphasize the need for models that can capture complex trust relationships beyond vector-based representations. To overcome these limitations, we already proposed a novel trust model where the trust perceived by one object with respect to another is represented by a directed, weighted graph. In this model, called T-pattern, the vertices represent individual trust dimensions, and the arcs capture the relationships between these dimensions. This model allows the IoT community to represent scenarios where an object may lack direct knowledge of a particular trust dimension, such as reliability, but can infer it from another dimension, like honesty. The proposed model can represent trust structures of the type described, where multiple trust dimensions are interdependent. This work represents a further contribution by presenting the first real implementation of the T-pattern model, where a neural-symbolic approach has been adopted as inference engine. We performed experiments that demonstrate the capability in inferring trust of both the T-pattern and this specific implementation. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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20 pages, 1820 KiB  
Article
Hybrid Solution Through Systematic Electrical Impedance Tomography Data Reduction and CNN Compression for Efficient Hand Gesture Recognition on Resource-Constrained IoT Devices
by Salwa Sahnoun, Mahdi Mnif, Bilel Ghoul, Mohamed Jemal, Ahmed Fakhfakh and Olfa Kanoun
Future Internet 2025, 17(2), 89; https://doi.org/10.3390/fi17020089 - 14 Feb 2025
Viewed by 605
Abstract
The rapid advancement of edge computing and Tiny Machine Learning (TinyML) has created new opportunities for deploying intelligence in resource-constrained environments. With the growing demand for intelligent Internet of Things (IoT) devices that can efficiently process complex data in real-time, there is an [...] Read more.
The rapid advancement of edge computing and Tiny Machine Learning (TinyML) has created new opportunities for deploying intelligence in resource-constrained environments. With the growing demand for intelligent Internet of Things (IoT) devices that can efficiently process complex data in real-time, there is an urgent need for innovative optimisation techniques that overcome the limitations of IoT devices and enable accurate and efficient computations. This study investigates a novel approach to optimising Convolutional Neural Network (CNN) models for Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which requires complex signal processing, energy efficiency, and real-time processing, by simultaneously reducing input complexity and using advanced model compression techniques. By systematically reducing and halving the input complexity of a 1D CNN from 40 to 20 Boundary Voltages (BVs) and applying an innovative compression method, we achieved remarkable model size reductions of 91.75% and 97.49% for 40 and 20 BVs EIT inputs, respectively. Additionally, the Floating-Point operations (FLOPs) are significantly reduced, by more than 99% in both cases. These reductions have been achieved with a minimal loss of accuracy, maintaining the performance of 97.22% and 94.44% for 40 and 20 BVs inputs, respectively. The most significant result is the 20 BVs compressed model. In fact, at only 8.73 kB and a remarkable 94.44% accuracy, our model demonstrates the potential of intelligent design strategies in creating ultra-lightweight, high-performance CNN-based solutions for resource-constrained devices with near-full performance capabilities specifically for the case of HGR based on EIT inputs. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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23 pages, 2981 KiB  
Article
IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model
by Huu Dang Quoc, Loc Nguyen The, Truong Bui Quang and Phuong Han Minh
Future Internet 2025, 17(2), 56; https://doi.org/10.3390/fi17020056 - 26 Jan 2025
Viewed by 1007
Abstract
Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In [...] Read more.
Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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12 pages, 747 KiB  
Article
Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
by Nikolaos Baras, Antonios Chatzisavvas, Dimitris Ziouzios, Ioannis Vanidis and Minas Dasygenis
Future Internet 2023, 15(11), 353; https://doi.org/10.3390/fi15110353 - 26 Oct 2023
Cited by 1 | Viewed by 1900
Abstract
In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within [...] Read more.
In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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Review

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25 pages, 2498 KiB  
Review
Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions
by Abubakar Ahmad Musa, Adamu Hussaini, Cheng Qian, Yifan Guo and Wei Yu
Future Internet 2023, 15(12), 380; https://doi.org/10.3390/fi15120380 - 27 Nov 2023
Cited by 4 | Viewed by 4051
Abstract
The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT [...] Read more.
The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT empowers extensive monitoring and control over a myriad of objects, enabling them to gather and disseminate data that bolster applications, thereby enhancing the system’s capacity for informed decision making, environmental surveillance, and autonomous inter-object interaction, all without the need for direct human involvement. These systems have achieved seamless connectivity requirements using the next-generation wireless network infrastructures (5G, 6G, etc.), while their diverse reliability and quality of service (QoS) requirements across various domains require more efficient solutions. Open RAN (O-RAN), i.e., open radio open access network (RAN), promotes flexibility and intelligence in the next-generation RAN. This article reviews the applications of O-RAN in supporting the next-generation smart world IoT systems by conducting a thorough survey. We propose a generic problem space, which consists of (i) IoT Systems: transportation, industry, healthcare, and energy; (ii) targets: reliable communication, real-time analytics, fault tolerance, interoperability, and integration; and (iii) artificial intelligence and machine learning (AI/ML): reinforcement learning (RL), deep neural networks (DNNs), etc. Furthermore, we outline future research directions concerning robust and scalable solutions, interoperability and standardization, privacy, and security. We present a taxonomy to unveil the security threats to emerge from the O-RAN-assisted IoT systems and the feasible directions to move this research forward. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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