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Enabling Variable High Spatial Resolution Retrieval From a Long Pulse BOTDA Sensor

IEEE INTERNET OF THINGS JOURNAL 2023年第2期10卷 1813-1821页

作者： Ge, Zhao Shen, Li Zhao, Can Wu, Hao Zhao, Zhiyong Tang, Ming Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan 430074 Peoples R China

resolution (SR) is one of the most important parameters of Brillouin optical time-domain analysis (BOTDA) sensors, which determines the minimum length that a perturbation event can be distinguished. In the field of Internet of Things (IoT), there is an urgent need for sensors with large-scale high-precision sensing capability for scenarios, such as intelligent monitoring of production lines and urban infrastructure. Conventionally, the SR is normally restricted to be longer than 1 m due to the similar to 10-ns acoustic lifetime limitation in silica optical fibers. For long-distance smart monitoring systems, the SR is generally on the order of several meters or even worse. However, it does not meet the needs of many applications. Therefore, there is an urgent need to achieve SR in the submeter magnitude. In this work, for the first time to the best of our knowledge, we propose a convolutional neural network (CNN) to process the data of conventional BOTDA sensors, which achieves unprecedented performance improvement that allows to directly retrieve submeter SR from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR Brillouin frequency shift (BFS) as the output target, the trained CNN is able to obtain an SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a twofold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training data sets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. It is worth mentioning

关键词： Brillouin scattering convolutional neural network (CNN) distributed optical fiber sensors signal processing

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A Review of Machine Learning Methods for IoT network-Centric Anomaly Detection 47

A Review of Machine Learning Methods for IoT Network-Centric...

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47th International Conference on Telecommunications and Signal processing, TSP 2024

作者： Nag, Anindya Hassan, Md. Mehedi Mandal, Dishari Chand, Nisarga Islam, Md Babul Meena, V.P. Benedetto, Francesco Khulna University Computer Science and Engineering Discipline Khulna9208 Bangladesh Adamas University Department of Cse Kolkata India University of Calabria Department of Dimes Cosenza Italy Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept of Eee Bangalore India University of Roma Tre SP4TE - Laboratory Economics Department Via Silvio D'Amico 77 Rome00145 Italy

ISBN: (纸本)9798350365597

Anomaly detection in IoT infrastructure is a growing idea in the IoT area. The IoT enables the linking of many devices through the use of wireless and mobile communication technologies. Data received from distributed sensing devices in a certain location is relayed to a central processing center, where it is collected and processed. Data dependability and the quality of IoT services are strongly related. To discover abnormalities, one alternative is to employ Machine Learning (ML) models that have been trained on both normal and aberrant behavior. Only a few are the methods effectively used to discover and evaluate anomalous data with varying degrees of success including Artificial neural networks (ANN), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Convolutional neural networks (CNN), and the Unsupervised Machine Learning. The findings of the study indicate that, with an accuracy of 99.87%, Unsupervised Machine Learning exceeds the other strategies in terms of performance. © 2024 IEEE.

关键词： Wireless sensor networks

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Learning from distributed Users in Contextual Linear Bandits Without Sharing the Context 36

Learning from Distributed Users in Contextual Linear Bandits...

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36th Conference on neural Information processing Systems (NeurIPS)

作者： Hanna, Osama A. Yang, Lin F. Fragouli, Christina Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781713871088

Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wireless where communication constraints can be a performance bottleneck, especially when the contexts come from a large d-dimensional space. In this paper, we consider a distributed memoryless contextual linear bandit learning problem, where the agents who observe the contexts and take actions are geographically separated from the learner who performs the learning while not seeing the contexts. We assume that contexts are generated from a distribution and propose a method that uses approximate to 5d bits per context for the case of unknown context distribution and 0 bits per context if the context distribution is known, while achieving nearly the same regret bound as if the contexts were directly observable. The former bound improves upon existing bounds by a log(T) factor, where T is the length of the horizon, while the latter achieves information theoretical tightness.

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Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第4期22卷 2435-2445页

作者： Gao, Mingjin Shen, Rujing Shi, Long Qi, Wen Li, Jun Li, Yonghui Chinese Acad Sci Inst Comp Technol Beijing 100864 Peoples R China Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China China Telecom Res Inst Beijing Peoples R China Univ Sydney Camperdown NSW Australia

Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

关键词： Task analysis Servers Delays Computational modeling Energy consumption Resource management Pricing Deep neural networks mobile edge computing task partitioning and offloading aggregative game

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CD-Sched: An Automated Scheduling Framework for Accelerating neural network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep neural networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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Adaptive ResNet Architecture for distributed Inference in Resource-Constrained IoT Systems 19

Adaptive ResNet Architecture for Distributed Inference in Re...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Khan, Fazeela Mazhar Baccour, Emna Erbad, Aiman Hamdi, Mounir Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar

ISBN: (纸本)9798350333398

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.

关键词： optimization distributed inference neural networks resilience ResNet

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A Moving Target Defense Approach for the distributed Dynamic network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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21st IEEE International Symposium on Parallel and distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected neural network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 IEEE.

关键词： network security

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BHMVD: Binary Code-based Hybrid neural network for Multiclass Vulnerability Detection 20

BHMVD: Binary Code-based Hybrid Neural Network for Multiclas...

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20th IEEE Int Symposium on Parallel and distributed processing with Applicat / 15th IEEE Int Conf on Social Comp and networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Cui, Ningning Chen, Liwei Du, Gewangzi Wu, Tongshuai Zhu, Chenguang Shi, Gang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781665464970

Precise binary code vulnerability detection is a significant research topic in software security. Currently, the majority of software is released in binary form, and the corresponding vulnerability detection approaches for binary code are desired. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot precisely identify the types of vulnerabilities. This paper proposes a Binary code-based Hybrid neural network for Multiclass Vulnerability Detection, dubbed BHMVD. BHMVD generates binary slices according to the control dependence and data dependence of library/API function calls, and then extracts syntax features from binary slices to generate type slices, which can help identify vulnerability types. This paper uses a hybrid neural network of CNN-BLSTM to extract vulnerability features from binary and type slices. The former extracts local features, while the latter extracts global features. Experiment results on 19 types of vulnerabilities show that BHMVD is effective for binary code-based multiclass vulnerability detection, and using a hybrid neural network can improve detection ability.

关键词： Binary Code Multiclass Vulnerability Detection Hybrid neural network Type Slices Binary Slices

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Scalable Perception-Action-Communication Loops With Convolutional and Graph neural networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION processing OVER networkS 2022年 8卷 12-24页

作者： Hu, Ting-Kuei Gama, Fernando Chen, Tianlong Zheng, Wenqing Wang, Zhangyang Ribeiro, Alejandro Sadler, Brian M. Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA Rice Univ Dept Comp & Elect Engn Houston TX 77005 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA US Army Res Lab Adelphi MD 20783 USA

In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-control framework maps raw visual observations to agent actions, aided by local communication among neighboring agents. Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN/GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively. By jointly training the CNN and GNN, image features and communication messages are learned in conjunction to better address the specific task. We use imitation learning to train the VGAI controller in an offline phase, relying on a centralized expert controller. This results in a learned VGAI controller that can be deployed in a distributed manner for online execution. Additionally, the controller exhibits good scaling properties, with training in smaller teams and application in larger teams. Through a multi-agent flocking application, we demonstrate that VGAI yields performance comparable to or better than other decentralized controllers, using only the visual input modality and without accessing precise location or motion state information.

关键词： Vision based control graph neural networks convolutional neural networks flocking decentralized control

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