Article
Chemistry, Analytical
Walid Osamy, Ahmed M. Khedr, Bader Alwasel, Ahmed Salim
Summary: This work introduces a trust-enabled data-gathering technique called DGTTSSA for WSN-based applications, which modifies and adapts the swarm-based SSA optimization algorithm to develop a trust-aware CH selection method. Simulation results show that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime compared to previous efforts.
Article
Telecommunications
Yufei An, Ying He, F. Richard Yu, Jianqiang Li, Jianyong Chen, Victor C. M. Leung
Summary: This paper designs a new anomaly detection architecture based on the concept of the Internet of intelligence and proposes a novel method for detecting abnormal HTTP traffic in IoT. Simulation results show that the proposed architecture and method can enhance the detection performance of abnormal HTTP traffic in IoT.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Green & Sustainable Science & Technology
Amir Masoud Rahmani, Seyedeh Yasaman Hosseini Mirmahaleh
Summary: The Internet of Medical Things (IoMT) has addressed the privacy challenges of E-healthcare-based Internet of Things (IoT) systems and improved the dependability and privacy of healthcare applications. By deploying various medical applications, an intelligent algorithm for node mapping and flexible clustering (NFC) has been proposed to categorize healthcare service providers and enhance the application reliability and privacy.
Article
Computer Science, Information Systems
Bader Aldughayfiq, Srinivas Sampalli
Summary: The study found that using the NFC application can improve patient safety during the medication pickup process, with the vast majority of participants finding the NFC application easy to use. Participants scored lower when using the NFC application compared with the traditional method in identifying wrong medication after dispensing. 90% of participants successfully identified wrong medication when using the NFC application, compared to only 38% using the traditional method.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Khaled Alanezi, Shivakant Mishra
Summary: This paper introduces a context management framework for high performance, context-aware computing at the edge in an IoP environment. The prototype of this architecture has been implemented and experimental evaluation shows good performance in edge-computing environment.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Ashutosh Bhoi, Rajendra Prasad Nayak, Sourav Kumar Bhoi, Srinivas Sethi, Sanjaya Kumar Panda, Kshira Sagar Sahoo, Anand Nayyar
Summary: This study proposes an IoT-enabled ML-trained recommendation system for efficient water usage with minimal farmer intervention. By deploying IoT devices in crop fields to collect data and analyzing it using ML approaches, the system shows promising results in improving water resource utilization.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Hui Ren, Xiaochen Shen, Xiaojun Jia
Summary: This paper proposes a dual-biological-community swarm intelligence algorithm that can be used to handle multimodal problems and ensure regional changes and search accuracy through a commensal strategy. The algorithm's performance has been evaluated on 12 multimodal problems, showing great potential for more efficient work.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Engy El-shafeiy, Karam M. Sallam, Ripon K. Chakrabortty, Amr A. Abohany
Summary: Internet of Medical Things (IoMT) is an emerging technology used for diagnosis, treatment and monitoring of patients by connecting various devices. SIoMT technique, based on swarm intelligence optimization, is proposed in this paper for analyzing, clustering and managing patient data using Bee Colony Optimization algorithm. The approach aims to improve efficiency and reduce costs in healthcare systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Yasir Ali, Habib Ullah Khan
Summary: The supply chain management of COVID-19 vaccine is a complex task, and IoT technology is a suitable solution. This study proposes a decision making model to select the right IoT platform for the logistics and transportation process of COVID-19 vaccine. The model is validated and tested through surveys and shows high accuracy and reliability.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Dan Lan, Incheol Shin
Summary: This research investigates the application of deep reinforcement learning (DRL) for autonomous optimization of multimedia content delivery in advanced networks. The comparative analysis between DDQN and DQN algorithms demonstrates that DDQN outperforms DQN in terms of convergence speed and cumulative rewards, while DQN shows potential for gains over successive runs.
Article
Computer Science, Information Systems
Zhihua Cui, Xuechun Jing, Peng Zhao, Wensheng Zhang, Jinjun Chen
Summary: This study proposes a post-processing strategy for subspace clustering of hyperspectral image data to balance sparsity and connectivity, and experimental results demonstrate its effectiveness in improving clustering accuracy in the Internet of Things.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Armin Razmjoo, Amirhossein Gandomi, Maral Mahlooji, Davide Astiaso Garcia, Seyedali Mirjalili, Alireza Rezvani, Sahar Ahmadzadeh, Saim Memon
Summary: As smart cities emerge, the Internet of Things plays a crucial role in sectors such as environment monitoring, public transport, utilities, street lighting, waste management, public safety, and smart parking, enhancing city efficiency and improving quality of life.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Sherali Zeadally, Oladayo Bello
Summary: Connectivity and interoperability are crucial in ICT infrastructure, particularly in healthcare systems where IoT technologies are integrated for efficient and cost-effective services. The complex ecosystem of healthcare information systems can benefit from IoT-based solutions to improve quality and delivery of healthcare services. Future opportunities in healthcare solutions can be explored through IoT connectivity.
INTERNET OF THINGS
(2021)
Article
Computer Science, Information Systems
Joseph N. Mamvong, Gokop L. Goteng, Bo Zhou, Yue Gao
Summary: This research proposes an efficient security algorithm based on the Advanced Encryption Standard for constrained IoT devices. By providing a cryptanalytic overview on complexity reduction and mathematical support, along with a secure element as a tradeoff, it aims to address implementation attacks effectively. The software implementation of the algorithm shows a significant reduction in encryption time compared to current standards and literature results.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Mohammad Reza Nosouhi, Keshav Sood, Neeraj Kumar, Tricia Wevill, Chandra Thapa
Summary: This article presents a machine learning-based approach for early detection of bushfires by detecting anomalies in spatiotemporal measurements of environmental parameters. The proposed method trains a model to learn the normal behavior of environmental data and uses a classification model to verify detected anomalies. The effectiveness of the approach is confirmed through experiments.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Theory & Methods
Lidia Fotia, Flavia Delicato, Giancarlo Fortino
Summary: The Internet of Things (IoT) enables smart objects to provide smart services inserted into information networks for human beings. The introduction of edge computing in IoT reduces decision-making latency, saves bandwidth resources, and expands cloud services at the network's edge. However, decentralized trust management poses challenges for edge-based IoT systems. Trust management is crucial for reliable mining and data fusion, improved user privacy and data security, and context-aware service provisioning.
ACM COMPUTING SURVEYS
(2023)
Review
Computer Science, Information Systems
Claudia Greco, Giancarlo Fortino, Bruno Crispo, Kim-Kwang Raymond Choo
Summary: This paper provides a comprehensive review of literature on penetration testing of IoT devices and systems. It identifies existing and potential IoT penetration testing applications and proposed approaches, and highlights recent advances in AI-enabled penetration testing methods at the network edge.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiuwen Fu, Pasquale Pace, Gianluca Aloi, Antonio Guerrieri, Wenfeng Li, Giancarlo Fortino
Summary: In this study, a interdependent network model for cyber-manufacturing systems (CMS) is developed based on the perspective of physical-service networking. The proposed realistic cascading failure model takes into account the load distribution characteristics of the physical network and the service network. The experiments confirm that attacks on the physical network are more likely to trigger cascading failures and cause more damage, and interdependency failures are the main cause of performance degradation in the service network during cascading failures, while isolation failures are the main cause of performance degradation in the physical network during cascading failures.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
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
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)
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, Cybernetics
Giancarlo Fortino, Lidia Fotia, Fabrizio Messina, Domenico Rosaci, Giuseppe M. L. Sarne
Summary: This article introduces a multi-agent SIoT architecture that incorporates a reputation system based on clustering of smart objects, providing reliability for transactions in SIoT scenarios. By enabling feedback between smart objects, and communication between edge servers and the cloud, reputation values are updated, enhancing the trustworthiness of objects.
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.
Article
Computer Science, Theory & Methods
Sheng Wang, Shiping Chen, Fei Meng, Yumei Shi
Summary: This study proposes a Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model for accurately predicting GPU utilization rates in heterogeneous GPU clusters. By constructing multiple scenarios' undirected graphs and using Graph Convolution Neural (GCN) to capture spatial dependency relationships, the MSHGN model achieves superior accuracy and robustness in predicting resource utilization on a real-world Alibaba dataset.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Nik Amir Syafiq, Mohamed Othman, Norazak Senu, Fudziah Ismail, Nor Asilah Wati Abdul Hamid
Summary: This research investigates the multi-core architecture for solving the fractional Poisson equation using the modified accelerated overrelaxation (MAOR) scheme. The feasibility of the scheme in a parallel environment was tested through experimental comparisons and measurements. The results showed that the scheme is viable in a parallel environment.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Antonio F. Diaz, Beatriz Prieto, Juan Jose Escobar, Thomas Lampert
Summary: This paper presents the design and implementation of a low-cost energy monitoring system that synchronously collects the energy consumption of multiple devices using a specially designed wattmeter, and utilizes widely used technologies and tools in the Internet of Things for implementation.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Ritam Ganguly, Yingjie Xue, Aaron Jonckheere, Parker Ljung, Benjamin Schornstein, Borzoo Bonakdarpour, Maurice Herlihy
Summary: This paper presents a centralized runtime monitoring technique for distributed systems, which verifies the correctness of distributed computations by exploiting bounded-skew clock synchronization. By introducing a progression-based formula rewriting scheme and utilizing SMT solving techniques, the metric temporal logic can be monitored and the probabilistic guarantee for verification results can be calculated. Experimental results demonstrate the effectiveness of this technique in different application scenarios.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Arya Tanmay Gupta, Sandeep S. Kulkarni
Summary: Lattice-linear systems allow nodes to execute asynchronously. The eventually lattice-linear algorithms introduced in this study guarantee system transitions to optimal states within specified moves, leading to improved performance compared to existing literature. Experimental results further support the benefits of lattice-linearity.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Tim Breitenbach, Shrikanth Malavalli Divakar, Lauritz Rasbach, Patrick Jahnke
Summary: With the trend towards multi-socket server systems, the demand for RAM per server has increased, resulting in more DIMM sockets per server. RAM issues have become a dominant failure pattern for servers due to the probability of failure in each DIMM. This study introduces an ML-driven framework to estimate the probability of memory failure for each RAM module. The framework utilizes structural information between correctable (CE) and uncorrectable errors (UE) and engineering measures to mitigate the impact of UE.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Carlos Ansotegui, Eduard Torres
Summary: This paper presents an incomplete algorithm for efficiently constructing Covering Arrays with Constraints of high strength. The algorithm mitigates memory blow-ups and reduces run-time consumption, providing a practical tool for Combinatorial Testing.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Lucas Perotin, Sandhya Kandaswamy, Hongyang Sun, Padma Raghavan
Summary: Resource scheduling is crucial in High-Performance Computing systems, and previous research has mainly focused on a single type of resource. With advancements in hardware and the rise of data-intensive applications, considering multiple resources simultaneously is necessary to improve overall application performance. This study presents a Multi-Resource Scheduling Algorithm (MRSA) that minimizes the makespan of computational workflows by efficiently allocating resources and optimizing scheduling order. Simulation results demonstrate that MRSA outperforms baseline methods in various scenarios.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Yue Li, Han Liu, Jianbo Gao, Jiashuo Zhang, Zhi Guan, Zhong Chen
Summary: The processing of block lifecycles is crucial to the efficiency of a blockchain. The FASTBLOCK framework, which introduces fine-grained concurrency, accelerates the execution and validation steps. It outperforms state-of-the-art solutions significantly in terms of performance.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Roberto Carrasco, Hector Ferrada, Cristobal A. Navarro, Nancy Hitschfeld
Summary: The experimental evaluation of GPU filters for computing the 2D convex hull shows significant performance improvement. The different point distributions have a noticeable impact on the results, with the greatest improvement seen in the case of uniform and normal distributions.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Keqin Li
Summary: In this paper, the authors study task scheduling with or without energy constraint in mobile edge computing. They propose heuristic algorithms to solve these problems and analyze them using the methods of communication unification, effective speed concept, and virtual task construction. The experimental results show that the performance of the heuristic algorithms is close to the optimal algorithm. This is the first paper in the literature to optimize the makespan of task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Hongliang Li, Hairui Zhao, Ting Sun, Xiang Li, Haixiao Xu, Keqin Li
Summary: This paper studies the problem of job placement in shared GPU clusters and proposes an opportunistic memory sharing model and algorithms to solve the problem. Extensive experiments on a GPU cluster validate the correctness and effectiveness of the proposed approach.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Lucas Ruchel, Edson Tavares de Camargo, Luiz Antonio Rodrigues, Rogerio C. Turchetti, Luciana Arantes, Elias Procopio Duarte Jr.
Summary: LHABcast is a leaderless hierarchical atomic broadcast algorithm that improves scalability by being fully decentralized and hierarchical. It uses local sequence numbers and timestamps to order messages and achieves significantly lower message count compared to an all-to-all strategy, both in fault-free and faulty scenarios.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Xiangyu Wu, Xuehui Du, Qiantao Yang, Na Wang, Wenjuan Wang
Summary: This paper proposes a new method to address the immutability issue of consortium blockchains by introducing a verifiable distributed chameleon hash (VDCH) function and a consensus protocol called CVTSS based on verifiable threshold signatures. The proposed method enhances the flexibility, fault tolerance, and redaction efficiency of consortium blockchains.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Theory & Methods
Ipsita Behera, Srichandan Sobhanayak
Summary: Task scheduling in cloud computing is a challenging problem, and researchers propose a hybrid algorithm that aims to minimize makespan, energy consumption, and cost. Evaluation using the Cloudsim toolkit demonstrates the algorithm's effectiveness and efficiency.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
