Article
Telecommunications
Kiyoshy Nakamura, Pietro Manzoni, Alessandro Redondi, Edoardo Longo, Marco Zennaro, Juan-Carlos Cano, Carlos T. Calafate
Summary: This paper presents LoRaCTP, a flexible protocol based on LoRa technology, for transferring small data. The protocol provides reliable LoRa transfer with stability, low power usage, and the ability to cover long distances, using lightweight connection setup and sending of long data messages.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Chemistry, Analytical
Tariq Sadad, Mejdl Safran, Inayat Khan, Sultan Alfarhood, Razaullah Khan, Imran Ashraf
Summary: This research proposes an IoT-based monitoring and detection system for cardiac patients. The system achieves remarkable accuracy in classifying ECG images using a lightweight CNN and an attention module. The findings suggest that this system can effectively aid in the identification of cardiac disorders.
Review
Computer Science, Information Systems
Elham Fazel, Hamid Esmaeili Najafabadi, Mohammad Rezaei, Henry Leung
Summary: Fog computing enables real-time information processing on the native net, while Mist Computing principles are not mature enough for practical use. Clustering methods for fog nodes can address limitations in device resources, but challenges remain in developing real-time applications and implementing collaborative clustering techniques on IoT-mist networks.
INTERNET OF THINGS
(2023)
Review
Health Care Sciences & Services
Tomasz Rechcinski
Summary: The electrocardiogram (ECG) has gained new significance due to its digital recording, frequent use, and the application of artificial intelligence (AI) for processing. This literature review highlights various software tools used to extract new information from the ECG, such as natural language processing, backpropagation neural network, and convolutional neural network. These AI tools enable accurate predictions based on the ECG, including age, sex, abnormal heart valve structure, atrial electrical activity, immune response after transplantation, response to resynchronization therapy, and risk of sudden cardiac death. The simplicity, relatively low cost, and potential for remote examination make these results attractive. The presented studies represent only a fraction of the advancements expected in the current year.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Chemistry, Analytical
James Heaney, Jamie Buick, Muhammad Usman Hadi, Navneet Soin
Summary: Health monitoring and its associated technologies have become increasingly important. The electrocardiogram (ECG) has been widely used for assessing and diagnosing cardiovascular diseases (CVDs). With the growing literature on ECG monitoring devices, researchers and healthcare professionals face challenges in selecting and evaluating systems that meet their needs and monitoring standards. This paper presents a comprehensive ECG monitoring system that captures and displays patients' heart signals, heart rate, blood oxygen levels, and body temperature. The system utilizes an OLED display, an Android application, and MATLAB for data presentation. Internet of Things (IoT) approaches are advantageous in improving heart disease patient care and alerting healthcare services based on patients' physical condition. Additionally, a web server is included for monitoring patients' status via WiFi. The prototype, compliant with safety regulations and medical equipment design, demonstrated excellent accuracy of 99.56% compared to a commercially available device.
Review
Computer Science, Theory & Methods
Christine Mwase, Yi Jin, Tomi Westerlund, Hannu Tenhunen, Zhuo Zou
Summary: This paper discusses the impact of artificial intelligence (AI) in various industries and highlights its crucial role in emerging applications. It emphasizes the need to achieve good performance in resource-constrained edge environments to meet the requirements of latency, security, and privacy. The paper presents an edge-based AI architecture and strategies to address communication inefficiencies, showcasing performance improvements from state-of-the-art research and identifying future directions.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Savio Sciancalepore
Summary: This paper introduces PARFAIT, a framework for secure and low-delay access to services in fog-enabled IoT ecosystems. PARFAIT uses rolling ephemeral identities to provide unlinkability among access requests, preventing tracking of mobile IoT devices by compromised fog nodes.
Article
Computer Science, Information Systems
Farshad Firouzi, Bahar Farahani, Alexander Marinsek
Summary: The convergence of IoT and Cloud Computing has led to the emergence of cloud IoT, which provides advanced services for aggregating, storing, and processing data generated by IoT. Edge and Fog Computing have addressed the limitations of bandwidth, latency, and connectivity by supporting interaction between cloud and IoT. The hierarchical edge-fog-cloud architecture enables the distribution of intelligence and computation to achieve optimal solutions.
INFORMATION SYSTEMS
(2022)
Review
Computer Science, Information Systems
Mitra Pooyandeh, Insoo Sohn
Summary: The network edge is a new solution for reducing latency and saving bandwidth in IoT network, utilizing AI for smart processing is crucial, while addressing security concerns is a major focus.
Article
Computer Science, Information Systems
Vasileios Karagiannis, Pantelis A. Frangoudis, Schahram Dustdar, Stefan Schulte
Summary: Fog computing enables the execution of IoT applications on compute nodes in both the cloud and at the network edge. To optimize communication latency, a routing mechanism is proposed in this study, leveraging the history of nodes that have previously accepted data of the same context to directly send the data to the closest node. Experimental results show a significant reduction in communication latency (up to 23 percent) and number of hops traveled (up to 73 percent) compared to the state-of-the-art method.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Ibrahim Kok, Feyza Yildirim Okay, Suat Ozdemir
Summary: This paper presents a novel artificial intelligence-based fog controller, called FogAI, which provides a versatile control mechanism to the fog layer and potential solutions for the problems of fog-based NGIoT systems. The feasibility of the FogAI concept is illustrated through a use case scenario and the proposed FogAI-assisted DQL algorithm shows superior performance in task offloading compared to existing policies.
INTERNET OF THINGS
(2022)
Article
Computer Science, Information Systems
Quy Vu Khanh, Nam Vi Hoai, Anh Dang Van, Quy Nguyen Minh
Summary: History has shown that healthcare and medical systems are crucial for the advancement of science and technology. In the past decades, there has been an explosive growth of ehealth applications, with cloud computing dominating e-healthcare systems and various domains. However, the high response time of cloud-based e-health systems presents a primary barrier.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Hani Mohammed Alshahrani
Summary: This paper introduces a collaborative intruder detection system called CoLL-IoT, designed to detect malicious activities in IoT devices. The system consists of four main layers that work collaboratively by monitoring and analyzing all network traffic to improve the accuracy of malware detection. In experiments, CoLL-IoT outperformed other existing tools on the UNSW-NB15 dataset.
Article
Computer Science, Hardware & Architecture
Vipin Kumar Rathi, Nikhil Kumar Rajput, Shubham Mishra, Bhavya Ahuja Grover, Prayag Tiwari, Amit Kumar Jaiswal, M. Shamim Hossain
Summary: Healthcare systems have greatly benefited from AI and IoT, allowing continuous monitoring of patients and timely treatment. The proposed solution is scalable, responsive, and reliable, using AI and edge computing with low latency. The system involves data collection, processing at edge nodes, and storage/sharing at edge data centers, with real-time patient scheduling and resource allocation.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Ahmed M. Alwakeel
Summary: With the advancement of technologies, cloud computing becomes essential. Fog computing and edge computing are emerging cloud technologies aiming to simplify complexities of cloud computing and utilize local network computing capabilities. However, using these technologies introduces security and privacy challenges which require countermeasures to mitigate their impact.
Article
Chemistry, Analytical
Diego Gutierrez Martin, Sebastian Lopez Florez, Alfonso Gonzalez-Briones, Juan M. Corchado
Summary: The revolution brought by the Internet of Things (IoT) has completely transformed the world. With an abundance of interconnected objects that possess remote sensing, actuation, analysis, and sharing capabilities, all devices today can connect to the internet and contribute valuable information for decision-making. However, the different data formats collected by various devices present a major challenge for IoT environments. To address this, a proposed solution utilizes IoT semantic data to reason actionable knowledge, combining advanced semantic technologies and artificial intelligence components to enable easy integration and interoperability, ultimately improving analytics and decision-making efficiency in IoT environments.
Article
Computer Science, Information Systems
Javier Palanca, Jaime Andres Rincon, Carlos Carrascosa, Vicente Javier Julian, Andres Terrasa
Summary: Over the years, multi-agent systems (MAS) technologies have been proven useful in creating distributed applications focused on autonomous intelligent processes. However, there is a lack of flexibility in existing agent platforms when agents require different decision-making processes. To address this issue, this paper proposes the Flexible Agent Architecture (FAA) that allows agents to define their actions using different computational models and combine them as needed. The FAA architecture has been integrated into the SPADE platform, extending its capabilities to develop applications with reactive, deliberative, and hybrid agents.
Article
Chemistry, Analytical
Cedric Marco-Detchart, Carlos Carrascosa, Vicente Julian, Jaime Rincon
Summary: In recent years, there have been several studies using Artificial Intelligence (AI) techniques to enhance sustainable development in agriculture. One specific application is the automatic detection of plant diseases using deep learning models, which analyze and classify plants to detect potential diseases, enabling early detection and prevention of disease propagation. This paper proposes an Edge-AI device that can automatically detect plant diseases from images of plant leaves, aiming to design an autonomous device for the detection of possible diseases. Multiple tests have shown that this device significantly improves the robustness of classification responses to potential plant diseases.
Review
Biochemistry & Molecular Biology
Sol Guerra-Ojeda, Andrea Suarez, Alicia Valls, David Verdu, Javier Pereda, Elena Ortiz-Zapater, Julian Carretero, Maria D. Mauricio, Eva Serna
Summary: This systematic review summarizes current knowledge on the role of Aryl hydrocarbon receptor (AhR) in the endothelium and its cardiovascular implications. AhR activation leads to vascular oxidative stress and endothelial dysfunction, suggesting that blocking AhR signalling could provide a new target for the treatment of vascular disorders such as cardiovascular complications in patients with chronic kidney disease or pulmonary arterial hypertension.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Green & Sustainable Science & Technology
Anne David, Tan Yigitcanlar, Rita Yi Man Li, Juan M. Corchado, Pauline Hope Cheong, Karen Mossberger, Rashid Mehmood
Summary: Digital technologies are widely used in local government activities, and adopting suitable strategies could improve service efficiency, effectiveness, and accountability. However, there are challenges in technology adoption, particularly in terms of capacity and resource allocation. This study aims to provide insights into local government digital technology adoption strategies through systematic reviews and meta-analyses. The results highlight the importance of considering people, processes, and technology aspects, and provide specific strategies for each category.
Editorial Material
Computer Science, Information Systems
Juan M. M. Corchado, Sara Rodriguez, Fernando de la Prieta, Pawel Sitek, Vicente Julian, Rashid Mehmood
Article
Computer Science, Information Systems
Sergio Marquez-Sanchez, Jaime Calvo-Gallego, Aiman Erbad, Muhammad Ibrar, Javier Hernandez Fernandez, Mahdi Houchati, Juan Manuel Corchado
Summary: This article introduces a cutting-edge edge computing architecture based on virtual organizations, federated learning, and deep reinforcement learning algorithms, aiming to optimize energy consumption in buildings and homes and address the cybersecurity risks and data transmission overheads associated with cloud-based systems.
Article
Mathematics
Javier Parra-Dominguez, Maria Alonso-Garcia, Juan Manuel Corchado
Summary: Sustainable development and its challenges are driving various international organizations to unprecedented levels of motivation. Lead by Europe, countries like the United States are eager to be part of the progress and understand the importance of a clear commitment to sustainability for future generations. Our study aims to delve deeper into the tracking and monitoring of reliable indicators to ensure robust continuous improvement in sustainability. Using fuzzy logic methodology, we apply it to the Sustainable Development Report's 2022 edition and focus on the specific application of SDG 11. Our results highlight favorable positions for countries like Brunei Darussalam, Tonga, Tuvalu, Andorra, and the Netherlands, and demonstrate the importance of data quality and expert intervention in enhancing the implementation process.
Article
Computer Science, Artificial Intelligence
Kunj Joshi, Chintan Bhatt, Kaushal Shah, Dwireph Parmar, Juan M. Corchado, Alessandro Bruno, Pier Luigi Mazzeo
Summary: Security in the blockchain is a growing concern, particularly regarding the phishing attack. Current attempts at detection, such as the consensus protocol, fail when a genuine miner adds a new block. Zero-trust policies are gaining popularity but still in the process of deployment. Machine-learning models with specific features offer a more accurate measure of detecting phishing attempts and eradicating them.
Proceedings Paper
Computer Science, Artificial Intelligence
Maria Alonso-Garcia, Ruben Fuente-Alonso, Juan M. Corchado
Summary: As technologies play an increasingly important role in our daily lives, software security has become crucial to protect systems from malicious attacks and irreversible damage. By collecting data reflecting programming quality, machine learning algorithms can be used to predict potential software vulnerabilities in a script before its release. We investigate whether these algorithms can predict a score that reflects the severity of vulnerabilities. Through a crucial preprocessing stage, we define a metric to reflect the programmer's evolution over time and identify future flaws in their code. By using a private dataset of labeled vulnerabilities over time, we achieve an effective early diagnosis system.
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Inaki Fernandez Perez, Fernando de la Prieta, Sara Rodriguez-Gonzalez, Juan M. Corchado, Javier Prieto
Summary: Quantum Computing (QC) is gaining attention due to advances in quantum computers, materials, and cryptography. QC offers an alternative to binary computers, promising enhanced AI models. This review explores the convergence of AI and QC, discussing the history, current research, and future directions in Quantum Artificial Intelligence (QAI).
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Vraj Parikh, Jainil Shah, Chintan Bhatt, Juan M. Corchado, Dac-Nhuong Le
Summary: Abnormalities related to the chest are common in both infants and adults. Identifying these abnormalities is relatively easy, but classifying them into specific diseases is more challenging. With the increasing number of COVID-19 cases, healthcare systems worldwide are under pressure. Due to limited testing kits, traditional methods are impractical for testing every patient with respiratory ailments. In such circumstances, using modern deep learning techniques to detect and classify thoracic abnormalities from chest radiographs can be helpful. Our methods achieved a mean average precision of 0.246 for detecting 14 different thoracic abnormalities on a publicly available chest radiograph dataset.
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
