Article
Computer Science, Theory & Methods
Shivananda R. Poojara, Chinmaya Kumar Dehury, Pelle Jakovits, Satish Narayana Srirama
Summary: With the growth of IoT devices, the need for efficient data processing and analytics is increasing. This study explores the benefits of using Serverless data pipelines for IoT data analytics and evaluates different approaches for designing such pipelines. The results reveal the suitability of different design methods for different types of applications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Yulei Wu
Summary: The Internet of Things is widely utilized in various critical sectors, requiring efficient data processing. AI-powered cloud-edge orchestration provides crucial computing support for IoT applications.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Computer Science, Interdisciplinary Applications
Rohit Kumar, Neha Agrawal
Summary: Cloud computing is transforming traditional computing methods through various forms and architectural types, such as Edge and Fog computing. These extensions of the basic cloud computing model promise improved network performance. Industrial applications rely on cloud resources to process a large volume of power-sensitive Industrial IoT (IIoT) data, which requires careful analysis to enhance system performance. This paper explores the Edge-Fog-Cloud architectural frameworks, compares their advantages and disadvantages, and delves into the scientific side of multi-dimensional IIoT data. It also highlights the current state-of-the-art and implementation challenges.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Article
Computer Science, Information Systems
Samuel D. Okegbile, Jun Cai, Attahiru S. Alfa
Summary: In this article, we investigated a collaborative data-sharing scheme using blockchain and cloud-edge computing techniques. The results showed that the proposed analysis method can be beneficial for studying the performance of any blockchain-enabled data-sharing system.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Hailin Feng, Liang Qiao, Zhihan Lv
Summary: The research aims to reduce network resource pressure, improve service quality and optimize network performance in cloud centers and edge nodes. A edge-cloud collaboration framework based on IoT is designed, using raspberry pi cards as working nodes. The framework consists of three layers, including edge RP, monitoring & scheduling RP, and CC. The task delay in the edge-cloud collaboration mode is the least among different working modes, and real-time object detection can be achieved.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Marek Pawlicki, Aleksandra Pawlicka, Rafal Kozik, Michal Choras
Summary: Cloud computing, edge computing, and Internet-of-Things have significantly impacted people's lives, but their security should not be taken for granted. These paradigms are constantly under attack, and the potential breaches can have severe consequences. This systematic review aims to analyze the overlap of attacks in cloud, edge, and IoT and provide solutions and countermeasures to enhance their security. It fills the gap by constructing a concise threat catalogue and offering a more universal approach to ensure the safety of the entire ecosystem.
Review
Computer Science, Information Systems
Mohammed Laroui, Boubakr Nour, Hassine Moungla, Moussa A. Cherif, Hossam Afifi, Mohsen Guizani
Summary: The Internet of Things (IoT) enables communication between devices and digital assets over a network without human intervention. Traditional cloud computing is not efficient in analyzing large amounts of data quickly, prompting the proposal of edge computing to decentralize data processing to solve this issue. Edge computing supports IoT applications requiring quick response times, leading to improved energy consumption and resource utilization.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Peiying Zhang, Xue Pang, Neeraj Kumar, Gagangeet Singh Aujla, Haotong Cao
Summary: This article presents a data transmission mechanism based on blockchain and edge computing to improve the reliability of data transmission in the IoT. The mechanism utilizes the distributed architecture of blockchain to ensure data immutability and introduces a three-tier structure and four working steps to achieve this goal.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Industrial
Moritz von Stietencron, Karl Hribernik, Katerina Lepenioti, Alexandros Bousdekis, Marco Lewandowski, Dimitris Apostolou, Gregoris Mentzas
Summary: Logistics 4.0 focuses on sustainable customer satisfaction and cost optimization using emerging technologies like IoT and streaming analytics. This paper introduces a software framework for streaming analytics in an edge-cloud environment to advance Logistics 4.0, with a specific application and evaluation in the aerospace industry.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Information Systems
Yongkai Fan, Guanqun Zhao, Xia Lei, Wei Liang, Kuan-Ching Li, Kim-Kwang Raymond Choo, Chunsheng Zhu
Summary: The article proposes a secure Blockchain-based scheme to guarantee the credibility of nodes and data in IoT and fog environments, demonstrating its feasibility through experiments.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Analytical
Muhammad Waleed, Tariq Kamal, Tai-Won Um, Abdul Hafeez, Bilal Habib, Knud Erik Skouby
Summary: The remote monitoring of patients using the internet of things (IoT) is crucial for continuous observation, healthcare improvement, and cost reduction. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system consists of sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers) modules.
Article
Computer Science, Information Systems
Chuan Pham, Duong Tuan Nguyen, Yosra Njah, Nguyen H. Tran, Kim Khoa Nguyen, Mohamed Cheriet
Summary: In this article, a resource allocation and sharing model in the edge cloud network is proposed to maximize the utility of IoT services and satisfy service constraints. The proposed method is evaluated through several simulation cases, showing faster convergence, increased utilization, and acceptance rate compared to the nonoptimal approach.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Yongqiang Zhang, Hongchang Yu, Wanzhen Zhou, Menghua Man
Summary: Edge computing is deployed at the edge of the network to provide intelligent services for IoT application scenarios, with low bandwidth consumption, low latency, and high security. An IoT edge computing reference architecture is proposed, consisting of the end edge, network edge, and cloud edge layers. The key technologies of applying AI in the EC-IoT reference architecture are analyzed. Platforms for different edge locations are classified, and solutions for IIoT, IoV, and smart home based on the EC-IoT reference architecture are proposed. Finally, the trends and challenges of EC-IoT are examined, showing promising applications for the EC-IoT architecture.
Article
Computer Science, Information Systems
Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti, Mehdi Hosseinzadeh
Summary: This paper highlights the importance of cloud computing and fog computing in the Internet of Things, the critical role of data management, and the essential techniques for data size reduction in fog computing. The study focuses on classifying and analyzing FDR studies from 2016 to 2022, presenting relevant topics and methods, and identifying open issues and challenges for future research.
INTERNET OF THINGS
(2022)
Article
Computer Science, Information Systems
Fang Fang, Xiaolun Wu
Summary: This article explores the complementarity and coexistence of 5G networks and edge computing, proposing a win-win mode for their cooperation. It summarizes applicable scenarios and potential challenges, providing valuable insights for research and development in integration deployment.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Alessandro Sabato, Shweta Dabetwar, Nitin Nagesh Kulkarni, Giancarlo Fortino
Summary: Engineering structures and infrastructure are still being used beyond their design lifetime. Noncontact methods, such as photogrammetry and infrared thermography, provide accurate and continuous spatial information to assess the condition of these structures. The incorporation of artificial intelligence algorithms expedites and improves the assessment process. This article summarizes the recent efforts in utilizing AI-aided noncontact sensing techniques, particularly image-based methods, for structural health monitoring (SHM) and discusses future directions to advance AI-aided image-based SHM techniques for engineering structures.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Ke Wang, Zicong Chen, Mingjia Zhu, Siu-Ming Yiu, Chien-Ming Chen, Mohammad Mehedi Hassan, Stefano Izzo, Giancario Fortino
Summary: Artificial intelligence-driven automation is becoming the technical trend in the new automation era. Convolutional neural network (CNN) technology has been widely used in industrial automation for defect detection and machine vision-driven automation for robot-human tracking. However, the high dependence on neural networks can lead to potential failures in defect detection system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Peng Xu, Ke Wang, Mohammad Mehedi Hassan, Chien-Ming Chen, Weiguo Lin, Md Rafiul Hassan, Giancarlo Fortino
Summary: This paper employs a One-Shot Neural Architecture Search (NAS) to generate derivative models with different scales and studies the relationship between network sizes and model robustness. The experimental results show that reducing model parameters can increase model robustness under maximum adversarial attacks, while increasing model parameters can enhance model robustness under minimum adversarial attacks. This analysis helps to understand the adversarial robustness of models with different scales for edge AI transportation systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Kai Lin, Jian Gao, Yihui Li, Claudio Savaglio, Giancarlo Fortino
Summary: This paper investigates the quality and real-time assurance problem of collaborative decision-making in large-scale intelligent transportation systems during multi-task parallel execution. It develops a collaborative decision architecture with cognitive networking and proposes an AI-driven cognitive networking collaborative decision-making algorithm.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Analytical
Diego Avellaneda, Diego Mendez, Giancarlo Fortino
Summary: Positioning systems are important in many different sectors, but traditional systems like GPS are not accurate or scalable for indoor positioning. Fingerprinting is an alternative solution that uses RF signals to recognize location characteristics. This project uses a machine learning approach to classify RSSI information from scanning stations. The implementation uses TinyML, a growing technological paradigm for ML on resource-constrained embedded devices. The deployed system achieves a classification accuracy of 88%, which can be increased to 94% with post-processing.
Review
Chemistry, Analytical
Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi, Giancarlo Fortino
Summary: Continuous advancements in technologies like the internet of things and big data analysis have enabled information sharing and smart decision-making using everyday devices. Swarm intelligence algorithms facilitate constructive interaction among individuals regardless of their intelligence level to address complex nonlinear problems. This paper examines the application of swarm intelligence algorithms in the internet of medical things, with a focus on wearable devices in healthcare. It reviews existing works on utilizing swarm intelligence in tackling IoMT problems such as disease prediction, data encryption, and resource allocation. The paper concludes with research perspectives and future trends.
Article
Chemistry, Analytical
Alaa Menshawi, Mohammad Mehedi Hassan, Nasser Allheeib, Giancarlo Fortino
Summary: A generic framework has been developed for heart problem diagnosis using a hybrid of machine learning and deep learning techniques. The framework utilizes a novel voting technique based on the prediction probabilities of multiple models to eliminate bias. Experimental results show that the framework outperforms single machine learning models, classical stacking techniques, and traditional voting techniques, achieving an accuracy of 95.6%.
Review
Chemistry, Analytical
Amira Bourechak, Ouarda Zedadra, Mohamed Nadjib Kouahla, Antonio Guerrieri, Hamid Seridi, Giancarlo Fortino
Summary: Given its advantages, edge computing has emerged as key support for intelligent applications and 5G/6G IoT networks. However, there are concerns about its capabilities to handle the computational complexity of machine learning techniques for big IoT data analytics. This paper aims to explore the confluence of AI and edge computing in various application domains to leverage existing research and identify new perspectives.
Review
Computer Science, Artificial Intelligence
Vincenzo Barbuto, Claudio Savaglio, Min Chen, Giancarlo Fortino
Summary: The Edge Intelligence (EI) paradigm is a promising solution to the limitations of cloud computing in the development and provision of next-generation Internet of Things (IoT) services. This paper provides a systematic analysis of the state-of-the-art manuscripts on EI, exploring the past, present, and future directions of the EI paradigm and its relationships with IoT and cloud computing.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Editorial Material
Computer Science, Artificial Intelligence
David B. Kaber, Andreas Nuernberger, Giancarlo Fortino, David Mendonca
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2023)
Article
Computer Science, Information Systems
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
Summary: This study proposes a novel approach for human activity monitoring and recognition that combines multihead convolutional neural networks and long short-term memory techniques, and enhances activity detection accuracy and feature extraction through attention mechanism. The results show that the proposed method performs well in real-time human activity recognition.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Aitizaz Ali, Muhammad Fermi Pasha, Antonio Guerrieri, Antonella Guzzo, Xiaobing Sun, Aamir Saeed, Amir Hussain, Giancarlo Fortino
Summary: This paper proposes a hybrid deep learning model for Industrial Internet of Medical Things (IIoMT) that addresses security challenges using homomorphic encryption (HE) and blockchain technology, providing higher privacy and security. By deploying a pre-trained model on edge devices and utilizing a consortium blockchain for data sharing and updating, the model can effectively classify and train local models while delivering higher efficiency and low latency.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Zhihan Lv, Chen Cheng, Antonio Guerrieri, Giancarlo Fortino
Summary: More data are generated through mobile network technology, giving birth to the cyber-physical social intelligent ecosystem (C & P-SIE). This survey studies the development of physical social intelligence, discussing its applications in various domains such as intelligent transportation, healthcare, public service, economy, and social networking. It also explores the future prospects of behavior modeling in C & P-SIE under information security, data-driven techniques, and cooperative artificial intelligence technologies. This research provides a theoretical foundation and new opportunities for the digital and intelligent development of smart cities and social systems.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Syed Tauhidun Nabi, Md. Rashidul Islam, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Salman A. AlQahtani, Gianluca Aloi, Giancarlo Fortino
Summary: This research utilizes 6.2 million real network time series LTE data traffic and other associated parameters to build a traffic forecasting model using multivariate feature inputs and deep learning algorithms, which can forecast traffic at a granular eNodeB-level and provide eNodeB-wise forecasted PRB utilization.
