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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Multi-Agent Reinforcement Learning with Clustering and Experience Sharing 28th

Multi-Agent Reinforcement Learning with Clustering and Exper...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Inokuchi, Kaname Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University Japan

We propose a training method for a heterogeneous multi-agent system to improve the learning efficiency in sparse-reward environments. Although extensive research on multi-agent deep reinforcement learning are conducted actively, these studies often assume that all agents are homogeneous to share/utilize learning parameters in their networks. Unfortunately, this is not always the case in real-world applications where heterogeneous autonomous agents, i.e., those with different capabilities and perspectives, must properly cooperate and coordinate with each other. In our learning method, which is an extension of the shared experience actor-critic (SEAC) for a heterogeneous agent environment, agents are classified depending on their features (such as trajectories of the observations, actions and received rewards) using variational autoencoder, and share their experience among agents within each cluster to train their individual agents for improving the learning efficiency in a sparse-reward environment. Our experimental evaluation shows that the proposed method is capable of more efficient cooperative/coordinated behaviors than the baselines while remaining the advantages of SEAC. © 2024 The Authors.

关键词： Deep reinforcement learning

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Gen-Power: Anomaly Detection in IoT Devices Utilizing Generated Power Waveforms

Gen-Power: Anomaly Detection in IoT Devices Utilizing Genera...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Hisafuru, Kota Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

Recently, IoT (Internet-of-Things) devices are very widely used in consumer electronics areas and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunctions may be inserted into them intentionally by malicious third parties. Utilizing power waveforms measured from IoT devices is one of the effective ways to detect its anomalous behaviors. Most IoT devices regularly consume steady-state power due to the operating system and/or hardware components. However, the existing methods manually or semi-manually find out the steady-state power in the IoT device and extract pre-determined features from the application power waveform. Hence, they cannot well detect its anomalous behaviors automatically. In this paper, we propose a method, called Gen-Power, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates application power waveforms by inferring the steady-state power by machine learning from the observed total power waveform. Then, the anomalous application behaviors are detected by automatically extracting the latent features from the generative application power waveform. Experimental evaluations show that Gen-Power detects anomalous application behaviors successfully, while the recent state-of-The-Art method cannot detect them. © 2024 IEEE.

关键词： anomaly detection generated power waveform generative machine learning IoT device power analysis side-channel analysis

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Carrying-Mode-Free Stair Ascent and Descent Estimation using Smartphones

Carrying-Mode-Free Stair Ascent and Descent Estimation using...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kajimoto, Dai Saeki, Etsushi Bao, Siya Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

This paper proposes a smartphone-based pedestrian dead reckoning to track stair ascent and descent. By using accelerometer and barometer information, pedestrians' gait patterns are collected under different carrying modes to detect the stair ascent and descent motions. Also, weather influences are considered to eliminate the barometer measurement errors. We evaluate the proposed method in a seven-floor environment, and we compare our method with a conventional method without multiple carrying modes and weather considerations. The minimal error rate of the proposed method is only 4.2% when ascending or descending seven floors, and at most 14.4% error are reduced compared with the conventional method. © 2024 IEEE.

关键词： indoor localization PDR smartphone stair ascent/descent

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Random Frame: a Data Augmentation for Glass Detection 27th

Random Frame: a Data Augmentation for Glass Detection

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liang, Yiming Ishikawa, Hiroshi Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9783031801358

Glass, though ubiquitous, is difficult to recognize in an image due to its transparency. Fine-grained low-level features indicating the presence of glass, such as refraction and reflection, are weak and subtle. This causes difficulties for existing glass detection models in learning those features, pushing them to rely on more overt cues, especially the frame surrounding the glass. Consequently, they can be fooled easily by frame-like objects. Here, we propose a simple data augmentation scheme called Random Frame to address this problem. Random Frame inserts a frame into an image to create an area with a frame but no glass. The model will receive a penalty if it only relies on the frame. The performances of existing models on various datasets improve when Random Frame is applied while being trained. Our comprehensive experiments demonstrate that our data augmentation can make models utilize more low-level features with more confidence in their predictions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image recognition

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Overlap-Free Pattern Formation Method Using Particle Swarm Optimization 28th

Overlap-Free Pattern Formation Method Using Particle Swarm O...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Yamada, Kotaro Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University Japan

We propose a method based on particle swarm optimization (PSO) for the multi-agent pattern formation problem (MAPFP), which allows agents to form formation patterns with a high completion rate without overlaps. MAPFP is a problem in which a large number of agents autonomously move to appropriate locations to fill in a target pattern, such as line drawings, letters, and geometric figure, in a cooperative manner. They are used in several applications such as route planning and object exploration. Although some studies consider the pattern-formation problem using swarm intelligence (SI), including PSO, they do not often consider conflicts such as collisions and overlaps of particles, and thus cannot be used in real-world applications. Our proposed method, based on PSO, an SI algorithm, allows agents to act independently, while avoiding overlaps, to form a pattern by introducing a local field of view to each particle and secreting pheromones to their neighbor points to attract other agents that have not yet found places to stay. Through experimental evaluation, we show that the proposed method not only eliminates overlaps during movement and unwanted overlaps on a target pattern but also improves the pattern completion rate compared to the baseline. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Evaluation of Ensemble Learning Models for Hardware-Trojan Identification at Gate-level Netlists

Evaluation of Ensemble Learning Models for Hardware-Trojan I...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Negishi, Ryotaro Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

IoT (Internet-of-Things) devices are tremendously widespread in our daily lives and these devices are very often outsourced to third-party companies to save cost. However, it is pointed out that the risk to insert malicious circuitry, called hardware Trojans (HTs), much increases there. The methods using machine learning for detecting HTs at gate-level netlists have been proposed, and those based on ensemble learning models are considered the most effective among them. This paper evaluates the performance of HT detection at gate-level netlists using various machine learning models based on ensemble learning, including random forest, XGBoost, LightGBM, and CatBoost. In particular, we optimize HT features for each machine-learning model and perform HT detection for various gate-level netlists, including intellectual property core netlists. The detailed HT detection results are thoroughly summarized and compared. © 2024 IEEE.

关键词： ensemble learning hardware security hardware Trojan machine learning netlist

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UAV-Enabled Image Capture and Wireless Delivery for On-Demand Surveillance Tasks

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IEEE Transactions on Wireless communications 2024年第10期23卷 12995-13010页

作者： Van Cuong, Nguyen Hong, Y.-W. Peter Sheu, Jang-Ping National Tsing Hua University Institute of Communications Engineering the Department of Computer Science Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

This work examines the task assignment, transmission scheduling, and trajectory design of image-surveillance UAVs dispatched to serve on-demand image capture and delivery services to ground users, e.g., from drivers seeking images of traffic jams or security units requesting images of private homes. In each task, the UAVs are required to capture the image of a specified surveillance region and deliver the image to the requesting user before the deadline. The task assignment, transmission scheduling, and trajectory design are jointly determined to maximize the total surveillance area of the completed tasks. We first examine the single-UAV problem and propose an alternating optimization approach that adopts the exact penalty method to promote near-binary solutions and employ successive convex approximation to deal with the nonconvex trajectory optimization. Then, we extend to the multiple-UAV scenario where cross-UAV tasks may require images to be captured and delivered by different UAVs. To enable distributed implementation, we introduce auxiliary deadlines to limit the time available for local tasks and, thus, decouple the joint optimization problem into multiple single-UAV problems that can be solved in parallel following the procedure derived in the previous case. Numerical simulations are provided to demonstrate the effectiveness of the proposed solutions. © 2024 IEEE.

关键词： Trajectories

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Passersby-Anonymizer: Safeguard the Privacy of Passersby in Social Videos 18

Passersby-Anonymizer: Safeguard the Privacy of Passersby in ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Ma, Jingzhe Luo, Haoyu Huang, Zixu Jin, Dongyang Wang, Rui Briffa, Johann A. Poh, Norman Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China University of Malta Department of Communications and Computer Engineering Malta

ISBN: (纸本)9798350364132

In the current era of pervasive short video content, the exposure of passersby's data frequently raises privacy concerns. Traditional anonymization techniques for passersby, like blurring and mosaicing, are often used before uploading such videos. However, these methods tend to degrade the informational richness of the visual content, markedly reducing the quality of the anonymized videos. Recent advancements of diffusion models have paved the way for text-guided image and video synthesis, yet applying these models to the anonymization of passersby poses three main challenges: i) bridging the domain gap between specific passersby data and high-quality image/video datasets that are used for pre-training diffusion models, ii) ensuring temporal consistency in the anonymized videos, and iii) preserving the integrity of video subjects' content while exclusively anonymizing passersby-related information. To address these challenges, we propose the Passersby-Anonymizer, a novel diffusion-based framework for anonymizing identity-specific attributes in video content. At its core, our model introduces a spatial content adapter (SCA) to adapt to the visual patterns of passersby image datasets. We introduce a Temporal Content Stabilizer (TCS) to maintain the temporal consistency of the anonymized videos. Furthermore, we design a mask-aware training strategy that specifically targets the anonymization of the mask region while preserving the integrity of other contents. Our experimental evaluations demonstrate that our model effectively addresses the challenge of anonymizing passersby without compromising the informational integrity of the social videos. The source code is available at https://***/HappyDeepLearning/Passersby-Anonymizer. © 2024 IEEE.

关键词： Anonymity

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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