Article
Computer Science, Information Systems
Jing Xu, Kai Xing, Chi Zhang, Shuo Zhang, Zhonghu Xu, Chunlin Zhong, Haojin Zhu, Zheng Yang, Yunhao Liu
Summary: This article discusses the threat of clone attacks in the Internet of Things and proposes a solution for global topology and identity tracing through a localized computing paradigm, which can reduce communication, storage, and computation overhead while ensuring deterministic detection.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Sankar Sennan, Somula Ramasubbareddy, Sathiyabhama Balasubramaniyam, Anand Nayyar, Mohamed Abouhawwash, Noha A. Hikal
Summary: In the field of Internet of Things (IoT), increasing network lifetime is a challenging task, with clustering being an effective method. The proposed T2FL-PSO algorithm aims to extend network lifetime by forming virtual clusters to address energy consumption issues in network nodes. Experimental results show that T2FL-PSO is more effective in increasing network lifetime compared to other algorithms.
Article
Computer Science, Information Systems
Ibrahim S. Alsukayti
Summary: This research paper proposes an innovative approach called Dynamic-RPL to address the challenge of achieving high performance in adverse network conditions. By making limited protocol modifications to RPL, Dynamic-RPL provides effective support for dynamic topology management and maintains high overall network performance.
Article
Computer Science, Hardware & Architecture
Elham Dalirinia, Mehrdad Jalali, Mahdi Yaghoobi, Hamid Tabatabaee
Summary: This article introduces a new evolutionary algorithm called the Lotus Effect Algorithm (LEA), which combines efficient operators from the dragonfly algorithm with the self-cleaning feature of the lotus effect for extraction and local search operations. The LEA outperformed other methods in terms of benchmark functions and showed practical application in reducing energy consumption in IoT nodes and solving real-world problems with multiple constraints. The results demonstrated that LEA performs better than other methods in terms of accuracy.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Gazi M. E. Rahman, Khan A. Wahid
Summary: This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a wireless sensor network with limited processing resources. The algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy, reducing the energy requirement by 33% through reducing concurrent clusters and hops.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Liang Liu, Xiangyu Xu, Yulei Liu, Zuchao Ma, Jianfei Peng
Summary: This article proposes a targeted insider attack model and a malicious nodes detection framework to address targeted insider attacks in IoT networks. By maintaining partial trust metrics and utilizing regression and clustering algorithms, the detection accuracy is improved. Experimental results demonstrate that the proposed scheme can effectively detect and identify malicious nodes' attack modes with high accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yu Chen, Shengbin Hao, Habibeh Nazif
Summary: This article introduces some issues and solutions in cloud-based Internet of Things (IoT), including resource energy consumption and privacy concerns. By using an improved ant colony optimization algorithm combined with a new local search algorithm, higher-quality responses can be obtained. The proposed method shows good ability in terms of network lifespan and energy consumption.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Ashish Kumar Tripathi, Kapil Sharma, Manju Bala, Akshi Kumar, Varun G. Menon, Ali Kashif Bashir
Summary: This study introduces a novel clustering method based on metaheuristic and MapReduce to address big data problems. By leveraging the searching potential of military dog squad and utilizing the MapReduce architecture to handle large datasets, the optimization effectiveness is improved. Experimental results demonstrate that the new method outperforms other algorithms in terms of clustering accuracy and computation times.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Lisa Liu, Daryl Essam, Timothy Lynar
Summary: This article examines the complexity of IoT traffic and proposes two new metrics. Through comparative experiments, the new methods are proven to outperform existing approaches, particularly in heterogeneous conditions.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Rakesh Kumar, Mayank Swarnkar, Gaurav Singal, Neeraj Kumar
Summary: IoT refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments. Regular monitoring of IoT network traffic is crucial for proper functioning and detection of malicious activities, with classification of IoT devices in the network traffic being a key activity. Various machine learning algorithms are proposed for IoT traffic classification, but their accuracy depends on data sources, features extracted, and deployment locations. Identifying network traffic characteristics and suitable machine learning algorithms is important for accurate and optimized IoT traffic classification, with a comparative analysis of popular machine learning algorithms conducted in this study.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Peng-Yong Kong
Summary: This article addresses the issue of distributed sensor clustering in the Internet of Things (IoT). Due to a lack of global information, the distributed scheme may lead to more clusters. To tackle this, the article proposes using an artificial neural network (ANN) to summarize the experience of a centralized scheme and transfer the knowledge to distributed decision makers.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Bingxian Lu, Lei Wang, Wei Wang, Keping Yu, Sahil Garg, Md. Jalil Piran, Atif Alamri
Summary: The ability to identify which wireless devices belong to the same person from Wi-Fi access point (AP) enables various IoT applications. Existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. This article proposes an on-body device clustering (OBDC) scheme that extracts trajectory and gait patterns from wireless signals and utilizes a hierarchical clustering algorithm to measure the similarity between devices. Experimental results show that about 90% of users' devices can be accurately clustered, with a low 0.7% misclustering rate.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Merim Dzaferagic, Neal McBride, Ryan Thomas, Irene Macaluso, Nicola Marchetti
Summary: The study presents a novel approach to considering in-network computing by using ideas from statistical mechanics. They model the execution of distributed computation with functional topologies, providing formal definitions for degeneracy and redundancy in the context of INC. Results show that exploiting possible degenerate alternatives can significantly improve successful computation rates while still meeting requirements such as delay or energy consumption.
IEEE SYSTEMS JOURNAL
(2021)
Article
Computer Science, Information Systems
Fanrong Shi, Simon X. Yang, Mithun Mukherjee, Hong Jiang, Daniel Benevides da Costa, Wing-Kwong Wong
Summary: This article proposes a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm to address the time synchronization issue in IoT networks. By utilizing multihop average-consensus and forwarding time information quickly, the algorithm achieves reduced iteration number and convergence time.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Morteza Biabani, Nasser Yazdani, Hossein Fotouhi
Summary: This paper introduces a clustering approach called REFIT to enhance the robustness of IoT network topology by using nodes' residual energy. The method balances load distribution and selects optimal paths, effectively improving network resilience against cascading failures.
Article
Automation & Control Systems
Fanhui Kong, Jiandiang Li, Bin Jiang, Huihui Wang, Houbing Song
Summary: This article proposes a novel integrated deep generative model, called AMBi-GAN, which uses bidirectional long short-term memory and attention mechanism to build a generative adversarial network for detecting multidimensional time-series anomaly in the industrial Internet of Things (IIoT). Experimental results show the potential of AMBi-GAN in improving the detection accuracy of industrial multidimensional time-series anomaly in the era of artificial intelligence.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Ge Wang, Di Li, Houbing Song
Summary: This article proposes a model-data-driven framework for the formal analysis of production resources, by combining an IoT-based DD technology with a model-driven approach. The method ensures dependability verification of formal models in the operational production line phase through real-time resource status feedback. Experimental results show a reduction of at least 75% in the time-cost of the formal modeling phase.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Jaganathan Logeshwaran, Nallasamy Shanmugasundaram, Jaime Lloret
Summary: Recently, research on wireless personal area network (WPAN) has focused on network protocols, scheduling, and security, but resource utilization has been neglected. This paper presents a wireless resource utilization algorithm for a bi-partite scatternet, aiming to enhance bandwidth allocation and power utilization. The algorithm shows promising performance compared to existing algorithms in terms of reliability, throughput, collision probability, transmission probability, and SINR.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Muhammad Adil, Jehad Ali, Muhammad Mohsin Jadoon, Sattam Rabia Alotaibi, Neeraj Kumar, Ahmed Farouk, Houbing Song
Summary: The number of confirmed COVID-19 cases has significantly increased globally. There is a pressing need to maximize the use of existing healthcare technologies, such as the healthcare Internet of Things (H-IoT), to combat this devastating virus. Patient wearable devices in healthcare are considered a promising technology with capabilities to assess and combat various diseases. However, the security of these devices is a major concern, especially in the context of COVID-19, as they accumulate and transmit sensitive data over wireless communication channels.
ACM TRANSACTIONS ON SENSOR NETWORKS
(2023)
Article
Chemistry, Analytical
Ali Mohd Ali, Mohammad R. Hassan, Ahmad al-Qerem, Ala Hamarsheh, Khalid Al-Qawasmi, Mohammad Aljaidi, Ahmed Abu-Khadrah, Omprakash Kaiwartya, Jaime Lloret
Summary: This research paper investigates the spatial distributions of five different services (VoIP, VC, HTTP, and Electronic Mail) using three different approaches (circular, random, and uniform). It establishes a new algorithm to assess the real-time and best-effort services of IEEE 802.11 technologies and proposes a network prioritization framework for smart environments. The framework is validated using a simulation setting.
Article
Engineering, Electrical & Electronic
Shumaila Javaid, Hamza Fahim, Sherali Zeadally, Bin He
Summary: Self-powered sensors have gained significant attention in recent years due to their ability to scavenge energy from the environment to sustain their operations. This article provides a comprehensive review of the state-of-the-art architectures of self-powered sensors and their applications in various domains. It also discusses the implementation challenges and possible solutions for designing cost-effective self-powered sensor-based monitoring systems.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Muhammad Adil, Varun G. Menon, Venki Balasubramanian, Sattam Rabia Alotaibi, Houbing Song, Zhanpeng Jin, Ahmed Farouk
Summary: The rapid growth of patient-wearable devices and implantable biosensors in digital healthcare has raised concerns about their security. This article presents a detailed survey of the literature from 2019 to 2022, discussing the security issues of self-empowered wireless sensor networks (SWSNs) and proposing future research directions.
IEEE SENSORS JOURNAL
(2023)
Review
Chemistry, Analytical
Safa Hamdare, Omprakash Kaiwartya, Mohammad Aljaidi, Manish Jugran, Yue Cao, Sushil Kumar, Mufti Mahmud, David Brown, Jaime Lloret
Summary: The growing popularity of Electric Vehicles (EVs) is leading to a shift away from traditional gasoline-powered vehicles. As a result, there is an increasing demand for Electric Vehicle Charging Systems (EVCS) and the significant growth of EVCS as a public and private charging infrastructure. However, with the expanding network of EVCS, cybersecurity-related risks have also greatly increased. This paper provides a cybersecurity risk analysis of the EVCS network by examining recent advancements in EVCS, EV adaptation trends, charging use cases, vulnerabilities in infrastructure and protocols, possible cyber-attack scenarios, and real-time data analysis of EV charging sessions. It also highlights potential open research issues in EV cyber research for domain researchers and practitioners.
Proceedings Paper
Computer Science, Cybernetics
Yuntong Zhang, Jingye Xu, Mimi Xie, Dakai Zhu, Houbing Song, Wei Wang
Summary: Heart Rate Variability (HRV) is an important indicator of physical and mental health, and can be inferred using photoplethysmography (PPG) sensors. Previous studies had high errors due to the use of only signal processing or machine learning (ML), indirect inference of HRV, or lack of large training datasets. To address these issues, we collected a large dataset of PPG signals and HRV ground truth, and developed HRV models combining signal processing and ML. Evaluation results show that our method outperformed signal-processing-only and ML-only methods, with errors ranging from 3.5% to 25.7%. We also explored different ML models and found that Decision Trees and Multi-level Perceptrons had average errors of 13.0% and 9.1% respectively, with models of at most hundreds of KB and inference time less than 1ms.
2023 IEEE/ACM CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE
(2023)
Article
Engineering, Electrical & Electronic
Dongjia Wang, Huaiyuan Qi, Baowang Lian, Yangyang Liu, Houbing Song
Summary: This article proposes a resilient decentralized CL (RDCL) algorithm to improve the accuracy and resilience of localization algorithms for multirobot systems. The measurement update procedure of the traditional decentralized CL algorithm is modified to track inter-robot correlations and ensure the independence of the elemental filters' measurement update procedure. Optimal information fusion algorithms are used to fuse multisource information, and the overall estimate of every robot is determined through a weighted sum of multisource estimates, achieving accurate localization. An online validation module is added to enhance the robustness of the multirobot localization system. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Hardware & Architecture
Amjad Rehman, Ibrahim Abunadi, Khalid Haseeb, Tanzila Saba, Jaime Lloret
Summary: Artificial intelligence (AI) is experiencing significant growth in the areas of smart cities, agriculture, food management, and weather forecasting, primarily due to the limitations of computing power on sensing devices. The integration of AI with IoT and ubiquitous sensors has led to improvements in the agricultural sector and reduced management costs. However, optimizing resource management and data load for forwarding nodes near edge boundaries remains a challenging issue due to limited wireless technology resources.
COMPUTER STANDARDS & INTERFACES
(2024)
Article
Engineering, Multidisciplinary
Muhammad Tayyeb, Muhammad Umer, Khaled Alnowaiser, Saima Sadiq, Ala' Abdulmajid Eshmawi, Rizwan Majeed, Abdullah Mohamed, Houbing Song, Imran Ashraf
Summary: Cardiovascular problems have become a leading cause of death globally, with a recent increase in the number of patients. Currently, the analysis of electrocardiogram (ECG) data for cardiac abnormality detection is time-consuming and prone to errors. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction, achieving better outcomes than existing approaches with a 94.40% accuracy score. The findings suggest that the proposed system has high potential for real-world deployment in practical medical settings.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yun-Shan Wei, Jin-Fan Wang, Jia-Xuan Wang, Qing-Yuan Xu, Jaime Lloret
Summary: This article proposes an open-closed-loop iterative learning control (ILC) strategy for linear time varying multiple input multiple output (MIMO) systems with vector relative degree, where the time interval of operation depends on the number of iterations. A feedback control is introduced in the ILC design to compensate for the missing tracking signal caused by the iteration-dependent interval. The study shows that under certain assumptions, the ILC tracking error can converge to zero as the number of iterations tends to infinity. Additionally, the effectiveness of the developed method is illustrated through a simulation example.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Faeze Behzadipour, Mahmod Ghasemi Nezhad Raeini, Saman Abdanan Mehdizadeh, Morteza Taki, Bijan Khalil Moghadam, Mohammad Reza Zare Bavani, Jaime Lloret
Summary: Implementing intelligent irrigation and adjusting the system is crucial for modern agriculture. This study utilized data from sensors and image processing to analyze and optimize the irrigation system, resulting in an 11% water saving compared to traditional user-controlled irrigation.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yingqi Peng, Wei Liu, Anfeng Liu, Tian Wang, Houbing Song, Shaobo Zhang
Summary: This paper proposes a truth-based Three-tier Combinatorial Multi-Armed Bandit (TCMAB) incentive mechanism for selecting each other to maximize their revenues in Mobile Crowd Sensing (MCS). The mechanism optimizes the interaction between the platform and the worker, as well as between the task requestor and the platform, to establish a balanced MCS ecosystem and improve the utilities, data quality, and applications quality of MCS.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
