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Edge Computing in IoT Networks Based on Artificial Intelligence

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

Deadline for manuscript submissions: 25 October 2025 | Viewed by 21317

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Special Issue Editors

Dr. Ricardo S. Alonso Rincón

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1. AIR Institute, Deep Tech Lab, Paseo de Belén 9A, 47011 Valladolid, Spain
2. BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
3. Higher School of Engineering and Technology, International University of La Rioja (UNIR), Logroño, Spain
Interests: Internet of Things; edge computing; distributed ledger and blockchain technologies; embedded systems; indoor location systems; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Iñaki Fernández Pérez

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BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; neural networks; evolutionary computation; distributed computing; transfer learning; swarm robotics; collective behavior; smart grids

Dr. Sara Rodriguez

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Guest Editor

BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Edge computing and Internet of Things (IoT) are synergic disruptive computational technologies that are already having a massive impact in a wide span of application areas, including connected Industry 4.0, precision agriculture and smart farming, robotics, transportation, energy and smart grids, health, and Fintech, among others. Furthermore, synergies between edge and IoT in such applications are enhanced with more cohesive functioning and improved functionalities by means of advanced processing based on artificial intelligence (AI) methods: for example, edge architectures for processing data from IoT devices can be leveraged by federated learning methods (both hierarchical and P2P), to optimize local AI analysis models while providing enhanced privacy by restricting the communication of raw data outside of edge nodes. These synergies between such edge techniques and architectures in IoT networks using AI have set exceptional grounds for impactful advances in many areas, which motivates this Special Issue, with the goal of providing a platform for researchers to contribute findings on these technologies and establish synergies to further advance these joint disciplines and their application. Authors are invited to submit high-quality research articles on topics including (but not restricted to):

Internet of Things (IoT) and Industrial IoT;
Sensor and actuator distributed networks;
Edge computing, edge architectures;
Edge continuum (edge/fog/cloud computing);
Machine learning for IoT sensor networks;
Federated learning;
Knowledge representation and AI processing in IoT networks;
Distributed learning AI methods and transfer learning;
Industrial applications leveraging edge, IoT and AI;
Adversarial learning and security in edge architectures and IoT networks.

Dr. Ricardo S. Alonso Rincón
Dr. Iñaki Fernández Pérez
Dr. Sara Rodriguez
Guest Editors

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Keywords

edge computing
Internet of Things
artificial intelligence
federated learning
distributed learning

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Published Papers (9 papers)

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Research

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20 pages, 5971 KiB

Open AccessArticle

Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking

by Vesna Knights, Olivera Petrovska, Jasmina Bunevska-Talevska and Marija Prchkovska

Sensors 2025, 25(7), 2065; https://doi.org/10.3390/s25072065 - 26 Mar 2025

Viewed by 338

Abstract

This paper aims to create an innovative approach to improving IoT-based smart parking systems by integrating machine learning (ML) and Artificial Intelligence (AI) with mathematical approaches in order to increase the accuracy of the parking availability predictions. Three regression-based ML models, random forest, gradient boosting, and LightGBM, were developed and their predictive capability was compared using data collected from three parking locations in Skopje, North Macedonia from 2019 to 2021. The main novelty of this study is based on the use of autoregressive modeling strategies with lagged features and Z-score normalization to improve the accuracy of regression-based time series forecasts. Bayesian optimization was chosen for its ability to efficiently explore the hyperparameter space while minimizing RMSE. The lagged features were able to capture the temporal dependencies more effectively than the other models, resulting in lower RMSE values. The LightGBM model with lagged data produced an R² of 0.9742 and an RMSE of 0.1580, making it the best model for time series prediction. Furthermore, an IoT-based system architecture was also developed and deployed which included real-time data collection from sensors placed at the entry and exit of the parking lots and from individual slots. The integration of ML, AI, and IoT technologies improves the efficiency of the parking management system, reduces traffic congestion and, most importantly, offers a scalable approach to the development of urban mobility solutions. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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33 pages, 4529 KiB

Open AccessArticle

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation

by Weronika Symbor and Łukasz Falas

Sensors 2025, 25(1), 247; https://doi.org/10.3390/s25010247 - 4 Jan 2025

Viewed by 964

Abstract

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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15 pages, 680 KiB

Open AccessArticle

Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching

by Chuangqiang Zhan, Shaojie Zheng, Jingyu Chen, Jiachao Liang and Xiaojie Zhou

Sensors 2024, 24(14), 4677; https://doi.org/10.3390/s24144677 - 18 Jul 2024

Viewed by 1251

Abstract

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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19 pages, 1964 KiB

Open AccessArticle

Minimizing Task Age upon Decision for Low-Latency MEC Networks Task Offloading with Action-Masked Deep Reinforcement Learning

by Zhouxi Jiang, Jianfeng Yang and Xun Gao

Sensors 2024, 24(9), 2812; https://doi.org/10.3390/s24092812 - 28 Apr 2024

Cited by 1 | Viewed by 1085

Abstract

In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wirelessly reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce the task of Age upon Decision (AuD) aimed at the timeliness of tasks used for decision-making, which highlights the timeliness of the information at decision-making moments. For the case in which dynamic task generation and random fading channels are considered, we provide a task AuD minimization design by jointly selecting UE and allocating blocklength. In particular, to solve the task AuD minimization problem, we transform the optimization problem to a Markov Decision Process problem and propose an Error Probability-Controlled Action-Masked Proximal Policy Optimization (EMPPO) algorithm. Via simulation, we show that the proposed design achieves a lower AuD than baseline methods across various network conditions, especially in scenarios with significant channel Signal-to-Noise Ratio (SNR) differences and low average SNR, which shows the robustness of EMPPO and its potential for real-time applications. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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15 pages, 2712 KiB

Open AccessArticle

An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection

by Bruno Gomes, Christophe Soares, José Manuel Torres, Karim Karmali, Salim Karmali, Rui S. Moreira and Pedro Sobral

Sensors 2024, 24(7), 2236; https://doi.org/10.3390/s24072236 - 31 Mar 2024

Cited by 2 | Viewed by 1802

Abstract

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system’s success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system’s maintenance intervals. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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19 pages, 2471 KiB

Open AccessArticle

Digital Twin Platform for Water Treatment Plants Using Microservices Architecture

by Carlos Rodríguez-Alonso, Iván Pena-Regueiro and Óscar García

Sensors 2024, 24(5), 1568; https://doi.org/10.3390/s24051568 - 29 Feb 2024

Cited by 9 | Viewed by 4330

Abstract

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant’s systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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20 pages, 4608 KiB

Open AccessArticle

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks

by Aakanksha Sharma, Venki Balasubramanian and Joarder Kamruzzaman

Sensors 2024, 24(4), 1216; https://doi.org/10.3390/s24041216 - 14 Feb 2024

Cited by 7 | Viewed by 2158

Abstract

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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16 pages, 3076 KiB

Open AccessArticle

Statement Recognition of Access Control Policies in IoT Networks

by Li Ma, Zexian Yang, Zhaoxiong Bu, Qidi Lao and Wenyin Yang

Sensors 2023, 23(18), 7935; https://doi.org/10.3390/s23187935 - 16 Sep 2023

Cited by 2 | Viewed by 1599

Abstract

Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the challenge of limited labeled data from individual clients is often overlooked, which impedes the training of highly accurate models. To address this issue and harness the potential of IoT networks, this paper presents FL-Bert-BiLSTM, a novel model that combines federated learning and pre-trained word embedding techniques for access control policy recognition. By leveraging the capabilities of IoT networks, the proposed model enables real-time and distributed training on IoT devices, effectively mitigating the scarcity of labeled data and enhancing accessibility for IoT applications. Additionally, the model incorporates pre-trained word embeddings to leverage the semantic information embedded in textual data, resulting in improved accuracy for access control policy recognition. Experimental results substantiate that the proposed model not only enhances accuracy and generalization capability but also preserves data privacy, making it well-suited for secure and efficient access control in IoT networks. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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Review

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29 pages, 815 KiB

Open AccessReview

Literature Review of Deep-Learning-Based Detection of Violence in Video

by Pablo Negre, Ricardo S. Alonso, Alfonso González-Briones, Javier Prieto and Sara Rodríguez-González

Sensors 2024, 24(12), 4016; https://doi.org/10.3390/s24124016 - 20 Jun 2024

Cited by 5 | Viewed by 5425

Abstract

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant. Full article

(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)

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