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Hybrid Raft-PoW Blockchain Consensus Algorithm

IEEE ACCESS 2025年 13卷 72067-72076页

作者： Ulukok, Mehtap Kose Sariyildiz, Irfan Evrim, Vesile Cyprus Aydin Univ Dept Software Engn TR-99010 Mersin Northern Cyprus Turkiye European Univ Lefke Dept Comp Engn TR-99010 Mersin Northern Cyprus Turkiye

The Raft consensus algorithm is widely used in private networks as an alternative to the energy-intensive PoW consensus algorithm in blockchains. The Raft consensus algorithm's voting mechanism performs well in reliable and well-planned networks with optimized timeouts. However, in unreliable or poorly configured networks, it encounters several challenges. These include multiple candidacies, repeated election cycles, insufficient or failed leader elections during network splits, prolonged leader election times and vulnerability to Sybil attacks. In this study, a novel Hybrid Raft-PoW consensus algorithm is introduced. It integrates the hash puzzle-based competition of the PoW consensus algorithm with the fast leader election mechanism of the Raft consensus algorithm. This combination ensures both speed and certainty in leader election, ensuring that leadership is delegated to the most capable nodes. At the same time, it promotes decentralization by ensuring a fair distribution across nodes, achieving at least 80% leadership distribution. Therefore, the proposed Hybrid Raft-PoW consensus algorithm improves or eliminates problems caused by Raft consensus algorithm's leader election mechanism.

关键词： Voting Consensus algorithm Blockchains Security Leadership Partitioning algorithms Energy consumption Internet of Things clustering algorithms Simulation Blockchain technology consensus algorithms proof of work raft

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Industrial Anomaly Detection System: A Multicase Algorithm Leveraging Feature Information and Memory Bank

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Shi, Yunhan Gao, Bin Yang, Geng Li, Haoran Woo, Wai Lok Univ Elect Sci & Technol China UESTC Sch Automat Engn Chengdu 610056 Peoples R China Northumbria Univ Newcastle Upon Tyne NE1 8ST England

The conventional anomaly detection (AD) methods typically rely on training normal samples without defects to identify deviations from the background. However, these methods suffer from issues, such as missing detection or false detection. Although subsequent research has attempted to improve algorithm performance, this often results in overfitting to specific cases within the AD framework. To address these challenges, this article proposes a semi-supervised AD algorithm that combines improved metric learning techniques with a memory bank (MB) update module. In order to enhance the algorithm's generalization capabilities across different cases, a multicases MB inheritance approach is introduced. This approach facilitates rapid generalization to unknown test cases with minimal iterative learning ( $\le 5$ epochs). Additionally, a bank-case matching module is designed to select the appropriate MB and calculate anomaly scores within our framework. The effectiveness of the proposed algorithm has been validated through real industrial tests and ablation experiments, demonstrating its capability in detecting anomalies accurately and reliably. Code is available on: https://***/FrankCloud-UESTC/Multi-case-AD.

关键词： Vectors Training Feature extraction Measurement Testing Anomaly detection Overfitting Input variables Extraterrestrial measurements clustering algorithms Anomaly detection (AD) defect localization and segmentation metric learning multicase generalization nondestructive testing

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TidyBlock: A Novel Consensus Mechanism for DAG-based Blockchain in IoT

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第2期24卷 722-735页

作者： Qu, Xidi Wang, Shengling Li, Kun Huang, Jianhui Cheng, Xiuzhen Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100045 Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 250100 Peoples R China

The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms.

关键词： Blockchains Internet of Things Consensus protocol Throughput Memory clustering algorithms Sorting Blockchain Internet of Things consensus mechanism formal methods

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Algorithm-Supervised Millimeter Wave Indoor Localization Using Tiny Neural Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3177-3191页

作者： Shastri, Anish Blandino, Steve Gentile, Camillo Lai, Chiehping Casari, Paolo Univ Trento Dept Informat Engn & Comp Sci DISI I-38123 Povo Italy NIST Gaithersburg MD 20899 USA Prometheus Comp LLC Bethesda MD 20814 USA NIST Radio Access & Propagat Metrol Grp Gaithersburg MD 20899 USA

The quasi-optical propagation of millimeter-wave (mmWave) signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most algorithms require information on the indoor environment, may entail the collection of large training datasets, or bear an infeasible computational burden for commercial off-the-shelf (COTS) devices. In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment. To relieve training data collection efforts, we resort to an algorithm-supervised approach by bootstrapping the training of our neural network through location estimates obtained from a state-of-the-art localization algorithm. We evaluate our scheme via mmWave measurements from indoor 60-GHz double-directional channel sounding. We process the measurements to yield dominant multipath components, use the corresponding angles to compute ADoA values, and finally obtain location fixes. Results show that the tiny NN achieves sub-meter errors in 74% of the cases, thus performing as good as or even better than the state-of-the-art algorithm, with significantly lower computational complexity.

关键词： Millimeter wave communication Location awareness Artificial neural networks Training Training data Signal to noise ratio Receivers Computational modeling Fingerprint recognition clustering algorithms Millimeter waves (mmWave) indoor localization neural networks (NNs) experimental evaluation channel sounders multipath clustering

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Ensemble-Based AI Learning Algorithm to Predict Drag Force for Particle Clusters

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IEEE ACCESS 2025年 13卷 47004-47012页

作者： Mark, Andreas Jonsson, Benjamin Elm Persson, Niclas Fraunhofer Chalmers Ctr Ind Math Computat Engn & Design S-41288 Gothenburg Sweden

In this work, we propose a novel approach that uses an ensemble of deep neural networks (DNN) and convolutional neural networks (CNN) to predict the drag coefficient of small, irregular-shaped particles in low-Reynolds-number flows. In addition, a dataset containing 710 simulations of randomly shaped particle clusters are generated, on which the proposed model is trained upon. Existing literature within the field have mainly focused on symmetrical or regularly shaped objects. The DNN model is trained on tabular data, capturing flow characteristics and particle properties, whereas the CNN processes images of particles taken from three different angles. This combination allows the model to account for the different orientations and shapes of particles. By integrating visual information, the model provides a better understanding of particle geometry, which improves its predictive capabilities compared with methods that rely solely on tabular data. To provide a baseline, we implemented support vector regression (SVR) as a simpler approach, allowing us to assess the added value of our ensemble model. The comparison shows that our ensemble method achieves a notable improvement over SVR and DNN, effectively capturing the influence of the particle structure on drag behavior. The result is validated using k-folds cross-validation with ten folds resulting in only a small deviation from the model presented, which is expected due to each fold having less data to train on. This work demonstrates the potential of ensemble learning as surrogate modeling to address complex fluid dynamics problems with irregular-shaped particles, which are common in environmental and industrial contexts.

关键词： Drag Computational modeling clustering algorithms Data models Predictive models Force Mathematical models Geometry Fluids Shape Drag coefficient ensemble model machine learning non-spherical particles support vector regression artificial intelligence

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Near-Global-Optimal Resource Allocation for Uplink SCMA Systems Based on Whale Optimization Algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1089-1103页

作者： Soltanpour, Meysam Zhang, Haixia Shandong Key Lab Intelligent Commun Sensing Comp I Jinan 250061 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

This paper investigates a joint resource allocation (RA) algorithm to maximize the average sum rate (ASR) of the sparse code multiple access (SCMA) random model-based networks. The ASR optimization problem turns out to be non-convex mixed-integer nonlinear programming (MINLP) problem which is challenging to address. The previous works strongly depend on the upper/lower bound of ASR, successive convex approximation (SCA) methods, initial assignment definition, post-processing procedures, and generating/training the data sets, thus cannot be applied. Given the above challenges, considering the ability of the whale optimization algorithm (WOA) in solving the non-convex MINLP NP-hard problems, we first construct the resource element assignment (REA) matrix utilizing the binary WOA (BWOA) through the reshaping the pattern of the arrangement of non-zero elements (PANs) and converting the structural constraints. Then we enhance the continuous WOA (CWOA) to mine the power allocation (PA) coefficients by using an acceleration factor that improves the exploration and exploitation phases. We also develop a Kuhn-Munkres (KM)-based algorithm as an REA benchmark which needs no for post-processing requirements. Finally, our proposed algorithms are compared with the state-of-the-art works in the literature. Results show that SCMA CWOA-BWOA algorithm (CBWOA) provides more robust optimality in massive connectivity situations with a larger number of users. Besides, the CBWOA method guarantees the structural constraints by increasing the number of search agents (NSAs) through the exploitation phase.

关键词： Resource management Uplink Interference Whale optimization algorithms Vectors clustering algorithms Training Measurement Industrial Internet of Things Wireless networks SCMA whale optimization algorithm non-convex MINLP resource allocation metaheuristic

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SNAP-CSI: Personalized Neural Compression for Enhanced CSI Compression in Wireless Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第3期24卷 2083-2093页

作者： Askar, Nurassyl Rini, Stefano Natl Yang Ming Chiao Tung Univ NYCU Dept Elect & Comp Engn Hsinchu 300 Taiwan

This paper introduces the Selection Network Assisted Personalized CSI Compression (SNAP-CSI) algorithm, a novel approach for efficient Channel State Information (CSI) compression in wireless networks. Focusing on scenarios where CSI from User Equipment (UE) is transmitted to a Base Station (BS) via a rate-limited channel, SNAP-CSI employs Deep Neural Networks (DNNs) trained on historical CSI data for enhanced compression. Central to SNAP-CSI is the exploitation of CSI heterogeneity to cluster users, enabling the training of tailored personalized models. These models comprise an encoder at the UE and a decoder at the BS, optimized for efficient compression with minimal parameters, specific to each user cluster. A key innovation in SNAP-CSI is the development of a Selection Network (SN). This network predicts cluster membership from compressed CSI data, allowing UEs to select the most fitting personalized model for the lowest data distortion. Concurrently, the BS utilizes the SN for accurate reconstruction of compressed CSI, thus negating the need for further synchronization. The effectiveness of SNAP-CSI is validated through simulations with the ultra-dense indoor maMIMO dataset, evaluating performance across diverse heterogeneity conditions and UE-to-BS channel rates.

关键词： clustering algorithms Wireless communication Quantization (signal) Wireless networks OFDM Computer architecture Accuracy Training Estimation Decoding Channel state information CSI compression wireless networks deep neural networks personalized models heterogeneous environments

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Max-Sum-Based Data Associations for Tracking Point and Extended Targets

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 3059-3075页

作者： Ma, Weizhen Jing, Zhongliang Dong, Peng Leung, Henry Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

For multitarget tracking applications, data association is a fundamental problem of assigning measurements to their corresponding targets. In this article, we propose two algorithms for tracking point and extended targets, respectively, based on factor graph representations of the joint probability density functions. Both employ the max-sum (MS) algorithm to find the maximum a posteriori assignment such that the state of each target is updated with the most probable measurement(s). We model the single target densities as Gaussian distribution for point targets and gamma Gaussian inverse Wishart distribution for extended targets. Under linear Gaussian assumptions on the target models, the proposed algorithms provide analytical solutions to multitarget tracking problems. Specifically, the messages flowed in the factor graphs, existence probabilities and states of the targets are analytically calculated. These two algorithms have reduced computational load compared to the particle-based sum-product (SP) algorithms and avoid gating or clustering used by traditional multitarget tracking methods. We compare the proposed MS-based algorithms (MSAs) with the Poisson multi-Bernoulli mixture filters and the SP-based algorithms, and simulation results show that the MSAs have comparable or improved tracking performance.

关键词： Target tracking Filters clustering algorithms Approximation algorithms Probability density function Heuristic algorithms Aerospace and electronic systems Probabilistic logic Kinematics Extraterrestrial measurements Data association (DA) extended target tracking (ETT) factor graph max-sum (MS) algorithm point target tracking (PTT)

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Improved NSGA-II Algorithm-Based SDVRP Considering Simultaneously Pickup and Delivery of Multi-Commodity

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Wang, Heng Yin, Xiaoyi Wang, Fang Meng, Lingxi Wang, Zhanwu Wang, Zhenfeng Henan Agr Univ Coll Mech & Elect Engn Zhengzhou 450002 Peoples R China Zhengzhou Comprehens Transportat Res Inst Co Ltd Zhengzhou 450008 Peoples R China

In the process of goods delivery, it is often necessary to split and deliver (pick-up and delivery) multiple products from multiple orders. How to economically and reasonably formulate vehicle delivery routes is a challenge. Firstly, the order splitting and vehicle delivery path formulation was analyzed from the perspective of enterprises. Meanwhile, the impact of order splitting and delivery on customer satisfaction was analyzed from the perspective of consumers. Based on the above analysis, a multi-objective optimization model was established with the objectives of minimizing costs and maximizing customer satisfaction. Then, an improved NSGA-II algorithm was proposed to solve the model. In this algorithm, K-means and ant colony algorithm were used to obtain the optimal paths for two objectives, respectively. Then, the customer satisfaction problem caused by splitting was quantified using the information entropy TOPSIS method. Subsequently, the improved VNS algorithm was used to expand the solution sets of the two optimal paths for dual objective optimization. The experimental results show that the algorithm obtained Pareto front and achieved ideal experimental results.

关键词： Costs Customer satisfaction Optimization Transportation Logistics Windows Vehicle routing clustering algorithms Genetic algorithms Time factors Vehicle routing problem with simultaneous pickup and delivery split delivery vehicle routing problems k-medoids multi-objective optimization multi-commodity

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Determining Metaheuristic Similarity Using Behavioral Analysis

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 262-274页

作者： Hayward, Lauren Engelbrecht, Andries Stellenbosch Univ Comp Sci Div ZA-7602 Stellenbosch South Africa Stellenbosch Univ Dept Ind Engn ZA-7602 Stellenbosch South Africa Gulf Univ Sci & Technol Ctr Appl Math & Bioinformat Hawally 32093 Kuwait

Many nature-inspired metaheuristics have been published, with claims of originality based on the metaphor that inspired the algorithm. Rarely is empirical evidence given to show algorithmic originality. In order to provide an easy and computationally cheap approach to characterize algorithm search behavior, a suite of 20 behavioral characteristics is proposed. This behavioral characteristic suite allows for the search behavior of an algorithm to be quantified without manual inspection. By doing so, behavioral novelty of any given algorithm may be determined by comparing the behavioral characteristics to those of well-known metaheuristics. To illustrate this use, and to evaluate whether metaheuristics are behaviorally distinct, a host of metaheuristics is run on various benchmark functions. To evaluate behavioral similarity across all problems, while acknowledging behavior to be problem dependant, a novel method is proposed. In addition to this method, new behavioral characteristics are also proposed. The behavioral vectors generated for each benchmark function are clustered. The relationships and trends present in the different clusters are summarized by creating a pair-wise matrix for every metaheuristic pair, which tallies the number of times that the pair are found within the same cluster. The tallies are then analyzed in order to make inference regarding the distinctness of any metaheuristic's behaviors, across many different benchmark functions. The analysis finds that the range of unique search behaviors is small and that most metaheuristics share their behaviors with most other metaheuristics. The analysis also identifies both unique algorithms, as well as algorithms which have no unique behaviors.

关键词： Metaheuristics Benchmark testing clustering algorithms Statistics Sociology Search problems Space exploration Evolutionary computation data analysis genetic algorithms particle swarm optimization pattern analysis data engineering

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