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3rd International conference on Advanced Network Technologies and intelligent Computing (ANTIC)

作者： Angel, D. Jothi, R. Mary Jeya Vidhya, M. Sathyabama Inst Sci & Technol Dept Math Chennai Tamil Nadu India SIMATS Div Math Saveetha Sch Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031640636;9783031640643

Locating positions of the sensors is a primary concern for WSNs as these are susceptible to multiple forms of attacks. The problem of detecting the position of placement of sensor nodes so that the entire communication area is covered is called the sensor deployment problem. This problem is equivalently the classical NP-complete optimization graph problem called the node cover. The node cover problem finds application across a range of WSN scenarios, including sensor deployment and the monitoring of communication links. In this paper, the node coverage and link coverage problems are solved in tree-based networks and the exact values of the node and edge covers for trees such as n-centipedes, paths, stars, spike trees and banana trees are determined. Furthermore, a use case of node cover within WSNs is presented in the context of disaster response and emergency management in earthquake early warning systems.

关键词： Node cover tree-based topologies wireless sensor networks graph theory

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Distributed Data Collection Algorithm With Unknown Objective Function in Adaptive Duty-Cycle WSNs

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IEEE sensors JOURNAL 2023年第3期23卷 3283-3295页

作者： Zhao, Duqiao Liu, Ding Cao, Xudong Xian Univ Technol Dept Control Sci & Control Engn Xian 710048 Peoples R China Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Peoples R China

With the rapid development of wireless sensor networks (WSNs), higher requirements are raised for the flexibility and applicability of data collection algorithms. Therefore, a novel distributed algorithm for data collection in adaptive duty-cycle WSNs is proposed in this article. On the one hand, the priority in data transmission is considered, in which the queues are used to realize the data storage and data transmission with different priorities and then a scheme for selecting the optimal transmission path for the sensor nodes is developed to ensure that high-priority data can be transmitted more efficiently. Next, a method that can adaptively adjust the duty cycle of the sensor nodes is designed to reduce the transmission delay, where each sensor can update its own duty cycle according to the amount of data with different priorities. On the other hand, the relation between the objective function and the variables in the data collection problem may be difficult to establish accurately or even unknown due to the complexity of WSNs, while the most existing data collection algorithms require the objective function to be explicitly expressed, making them inapplicable to the above scenarios. Hence, the data collection problem with unknown objective function in adaptive duty-cycle WSNs is also investigated in this article. Since the objective function cannot be established, a distributed optimization algorithm with randomized gradient-free is developed to solve that problem. In particular, the proposed algorithm solely relies on the row-stochastic weight matrix, where each sensor needs to communicate only with its neighbors to make decision and locally allocates the information weight of its neighbors, making the algorithm easier to implement. Under some reasonable assumptions, the explicit analysis for the convergence of the proposed algorithm is provided. Finally, the convergence and effectiveness of the proposed algorithm are substantiated through numerical simulatio

关键词： Wireless sensor networks Data collection sensors Linear programming Optimization Data communication Convergence Adaptive duty cycle data collection data priority distributed optimization randomized gradient-free

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Simulation implementation of intelligent track-to-track association algorithm for multi-sensor network 4

Simulation implementation of intelligent track-to-track asso...

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4th International conference on sensors and information Technology, ICSI 2024

作者： Wang, Jin Lu, Wenjia Cao, Gang Guo, Jiadong Information Systems Department Shanghai Aerospace Electronic and Communication Equipment Research Institute Shanghai China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

Multi-sensor network joint detection is a newly emerging interdisciplinary detection method that has been developing rapidly in recent years. Compared with traditional single sensor detection, it can enhance the robustness and reliability of the entire system by using multi sensor network track-to-track association technology in solving problems such as targeting, detection, and positioning. It can also improve target accuracy, expand system time, and improve sensor coverage Advantages such as improving the information utilization rate of the system. This article proposes a multi sensor track association algorithm based on Convolutional Neural Network(CNN). Through three steps of constructing, initializing, and training the neural network, a multi sensor track association model based on CNN is established, which solves the problem of automatic track association under the background of multi-sensor network detection. Simulation experiments on multiple sets of data are conducted, through data proof, the intelligent method of using neural network algorithms can effectively improve the track association of multiple sensor stations. © 2024 SPIE.

关键词： Convolutional neural networks

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Design of real time monitoring and intelligent analysis system for fiber optic sensor networks driven by big data 3

Design of real time monitoring and intelligent analysis syst...

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3rd International conference on Electronic information Engineering and Data processing, EIEDP 2024

作者： Gang, Huang Like, Pan Chengzhang, He Chengfeng, Yang Zhong, Li Xiuyi, Pan Yongjia County Electric Power Industry Co. Ltd. Zhejiang Wenzhou325100 China State Grid Yongjia County Power Supply Co. Ltd. Zhejiang Wenzhou325100 China

ISBN: (纸本)9781510680531

This article explores the application of big data technology in the design of real-time monitoring and intelligent analysis systems for fiber optic sensor networks (FOSN). An analysis was conducted on the limiting factors of current FOSN, such as cross sensitivity, data processing speed, signal-to-noise ratio, etc. This paper proposes an advanced data analysis engine based on CNN (Convolutional Neural Network) algorithm model and artificial intelligence to overcome these challenges. In the real-time data processing capability experiment, the average time for processing 10000 pieces of data based on CNN algorithm system was only 0.05 seconds, while the time for processing 100000 pieces of data was controlled within 0.5 seconds, demonstrating the efficiency of processing large-scale data. In the accuracy verification experiment of monitoring, the average error of the CNN algorithm system for 1000 data is only 0.03, indicating the high accuracy performance of the system in real-time monitoring. © 2024 SPIE.

关键词： sensor networks

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Joint Design for Energy Replenishment and Data Collection in AUV-Based Underwater Wireless sensor networks 16

Joint Design for Energy Replenishment and Data Collection in...

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16th International conference on Wireless Communications and Signal processing, WCSP 2024

作者： Zhao, Menghan Ji, Fei Ren, Zhangrui Zhong, Yingying Cheng, Mingyue School of Electronic and Information Engineering South China University of Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (纸本)9798350390643

Underwater wireless sensor networks (UWSNs) rely heavily on data collection from underwater sensors, which is crucial for the functionality of these networks. However, the limited energy of sensors depletes over time as tasks are performed, leading to sensor failures and subsequently reducing the network's lifespan. While most existing research introduces autonomous underwater vehicles (AUVs) for close-range data collection to mitigate sensor energy consumption, this method cannot prevent the ultimate death of the network. To enhance the functionality of UWSNs, we jointly consider energy replenishment and data collection for UWSNs. Therefore, an AUV-based charging and data collection scheme (CDCS) is proposed. CDCS will allow the AUV to charge sensors closely and plan a charging path for the AUV. In addition, CDCS proposes that during the charging time of sensors, the AUV performs cluster-based data collection from sensors to fully utilize the charging time. Simulation results demonstrate the feasibility of the scheme. © 2024 IEEE.

关键词： Wireless sensor networks

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Effective detection algorithm of electronic information and signal processing based on multi-sensor data fusion

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2023年第3期26卷 519-526页

作者： Cao, Ting Nanyang Inst Technol Sch Informat Engn Nanyang Peoples R China

Appropriate vibration diagnostics when cutting a variety of products is critical for cutting quality and scrap and consumables reduction. Collecting and combining data from various sensors allows for realtime data collection during processing for system monitoring. The purpose of this study is to develop a method for effectively diagnosing milling machine vibrations using multi-sensor data fusion. It is proposed that sound and frequency signals be preprocessed to isolate specific vibration features, and then classified and aggregated for a complete vibration diagnostics system. After preliminary wavelet signal conversion, a deep belief network has been developed for each sensor to classify vibration features. When combining classification results, the fuzzy Choquet integral algorithm is used. Thus, a comprehensive judgment is obtained. It has been demonstrated that for neural networks, 100 epochs are sufficient for learning to take place. It has been discovered that the accuracy of classification and inference of the correct solution increases from 75% to 98% as the number of vibration feature combinations increases. When compared to the Dempster-Schafer evidence theory, the fuzzy integral fusion algorithm provides a high level of vibration detection accuracy of up to 98.7%. The resulting diagnostic procedure is simple and effective. It can be used to control the cutting process on machine tools in industrial settings. Improvements to the scheme are suggested, including the use of intelligent technologies to automate the process.

关键词： Multi-sensor data fusion Vibration diagnostics Signal processing Deep belief network Fuzzy Choquet integral Dempster-Schafer evidence theory

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Convex Combination of Adaptive Filters for Channel Estimation of Wireless sensor networks

Convex Combination of Adaptive Filters for Channel Estimatio...

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2024 IEEE International conference on intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Singh, Anurag Verma, Ankit Prasad, Binod Patel, Vinal ABV-Indian Institute of Information Technology and Management Department of Electrical and Electronics Engineering Madhya Pradesh Gwalior474015 India

ISBN: (纸本)9798350379525

In Wireless sensor networks (WSNs), power man-agement techniques and less power-consuming relay policies should be used to achieve improved quality of service (QoS) for the limited power supply, computational power, and memory capacity of sensor nodes. Most of these parameters are affected by the presence of modeling errors in the channel estimation process. Therefore, in this paper, a set of novel channel estimation algorithms that can achieve both faster convergence and smaller steady-state errors are designed to reduce the channel estimation error to improve the performance of WSNs. © 2024 IEEE.

关键词： Adaptive filtering

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Fault-Tolerant Humidity sensor Based on Neural networks 5

Fault-Tolerant Humidity Sensor Based on Neural Networks

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5th International conference on Neural networks and Neurotechnologies, NeuroNT 2024

作者： Artemova, Svetlana V. Shmeleva, Anna G. Ladynin, Andrey I. Ershov, Nikita S. Kamenskaya, Maria A. Mirea - Russian Technological University Cybersecurity and Digital Technologies Institute Informatics Department Moscow Russia Tambov State Technical University Department of Power Engineering Tambov Russia

ISBN: (纸本)9798350363739

The article discusses fault-tolerant neural network moisture sensors development methodology for pasty and bulk materials. The input of the neural network humidity sensor receives information from primary measuring transducers that control materials' drying process in real time inside various drying installations. The relative moisture value obtained from the sensor output. Proposed methodology is based on primary measuring transducers failure situations classification. The following failure classes are introduced: failure-free operation of all primary measuring transducers, soft failure, medium failure, hard failure. Depending on the class, models are created to estimate materials relative moisture by training neural networks with failures. Neural-based moisture sensors for bulk and pasty materials development examples are given. Proposed fault-tolerant neural network humidity sensors are already used in information and control systems for pasty materials control. Based on the information received, a decision is made to control the drying installation to minimize losses in drying process quality and productivity. Industrial complexes modernization through intelligent information and control systems introduction offers a significant products competitiveness increase manufactured by domestic enterprises. © 2024 IEEE.

关键词： Fault tolerance

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A High Efficiency Task Allocation Method Based on Adaptive Hybrid Particle Swarm Optimization for intelligent Unmanned Wireless sensor networks 12

A High Efficiency Task Allocation Method Based on Adaptive H...

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12th IEEE International conference on information and Communication networks, ICICN 2024

作者： Zhang, Baitao Luo, Tao Zhu, Yuanjiao Chen, Zi Luo, Yunchen Zhou, Jie Xiao, Jing College of Information Science and Technology Shihezi University Shihezi China College of Computer Information and Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350355802

In recent years, with the continuous application of intelligent unmanned technology, network optimization of intelligent Unmanned Wireless sensor networks (IUWSNs) has emerged as a research hotspot. However, due to the limitations imposed by sensor size, the sensing capabilities of small wireless sensors in IUWSN s are restricted. To achieve higher network performance, it is crucial to devise suitable and efficient task allocation schemes. Therefore, a novel Adaptive Hybrid Particle Swarm Optimization algorithm (AHPSO) is proposed for efficient task allocation in IUWSN s. Firstly, AHPSO significantly enhances the efficiency of task distribution in IUWSN s, thereby markedly improving network benefits. Secondly, the new adaptive mechanism, by dynamically adjusting algorithm parameters, greatly increases the diversity of population solutions and accelerates the convergence speed of the algorithm. Additionally, a new simulated annealing strategy created in this study effectively prevents the algorithm from falling into local optima. Finally, extensive simulation experiments were conducted comparing AHPSO with Genetic Algorithms (GA) and Simulated Annealing (SA). The results demonstrate that AHPSO markedly outperforms other methods in enhancing task allocation efficiency and overall network performance of IUWSNs. © 2024 IEEE.

关键词： Genetic algorithms

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Joint Beamforming for RIS-Assisted Integrated Communication, Sensing and Power Transfer Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第2期13卷 288-292页

作者： Yang, Yue Gao, Hui Yang, Xiaoyu Cao, Ruohan Fan, Yongjian Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Hebei Univ Engn Key Lab Secur & Protect Informat Sensing & Proc Handan 056038 Peoples R China

This letter studies a reconfigurable intelligent surface (RIS)-aided integrated communication, sensing and power transfer systems. We aim to maximize the received energy from the energy harvester by jointly optimizing the (active) transmit beamforming at the BS and (passive) reflect beamforming by the phase shift of the RIS, subject to the Cramer-Rao bound for the target, the received signal-to-noise ratio (SNR) at the information decoding receiver and the transmit power constraint at the BS. To solve this non-convex problem with coupled variables, we decouple it into two subproblems and introduce an alternating optimization framework. The transmit beamforming sub-problem is recast into a convex form using the Schur complement and semidefinite relaxation techniques. Subsequently, auxiliary variables are introduced to transform the phase shift sub-problem into a convex problem. Simulation results show that the proposed algorithm can significantly improve the received energy while ensuring communication and sensing performance.

关键词： sensors Array signal processing Optimization Signal to noise ratio Simultaneous wireless information and power transfer Internet of Things Wireless sensor networks Integrated communication sensing and power transfer reconfigurable intelligent surfaces beamforming design

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