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AoI Minimizing of Wireless Rechargeable sensor Network Based on Trajectory Optimization of Laser-Charged UAV 16th

AoI Minimizing of Wireless Rechargeable Sensor Network Based...

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16th International conference on Algorithmic Aspects in information and Management, AAIM 2022

作者： Luo, Chuanwen Hou, Yunan Hong, Yi Chen, Zhibo Liu, Ning Li, Deying School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783031160806

This paper investigates a new UAV-assisted Wireless Rechargeable sensor Network (WRSNs) based on wireless powered technologies, where the Unmanned Aerial Vehicle (UAV) can not only be used as wireless aerial mobile base station for gathering data from sensors but also be used as mobile charger to replenish energy for sensors, and it also can be charged energy by Laser Beam Directors (LBDs) to overcome the limitation of on-board energy shortage. In such a network, we study the average Age of information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors by UAV. We first prove that the AoIO problem is NP-hard. Since the average AoI depends on UAV’s flight trajectory, the hovering time required for data collection, energy power transfer and laser charging, we propose an approximation algorithm to solve the AoIO problem by jointly optimizing these factors. Afterwards, we conduct extensive simulation experiments to validate effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR sensor

Toward Neuromorphic Perception: Spike-driven Lane Segmentati...

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International conference on intelligent Transportation

作者： Genghang Zhuang Zhenshan Bing Kai Huang Alois Knoll School of Computation Information and Technology Technical University of Munich. School of Computer Science and Engineering Sun Yat-sen University

As a prerequisite of high vehicle autonomy, lane segmentation is a significant perception task for advanced autonomous driving. In recent years, spiking neural networks (SNNs) have garnered the attention of researchers due to their appealing power efficiency, which provides the potential to improve energy consumption for the perception system on power-constrained autonomous vehicles. In this paper, we propose a spiking neural network targeted for LiDAR sensors to solve the lane segmentation problem. By encoding the LiDAR point cloud into spikes, the proposed SNN constructed in an end-to-end fully convolutional network structure is capable of processing the LiDAR input through the network to segment the lane area effectively. Experiments conducted on the KITTI dataset for urban scenes and the power consumption evaluation demonstrate the high performance and energy efficiency of the proposed SNN for LiDAR-based lane segmentation.

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A Digital Emergency Communication Method for Coal Mining Accident Based on Distributed Acoustic sensor

A Digital Emergency Communication Method for Coal Mining Acc...

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International conference on Optical Communications and networks (ICOCN)

作者： Yazhuo Li Jiaqi Ma Xinying Zhao Hongren Li Shuai Tong Ningmu Zou Xuping Zhang Fei Xiong Yixin Zhang Huanhuan Liu College of Engineering and Applied Sciences Nanjing University Nanjing China Key Laboratory of Intelligent Optical Sensing and Manipulation Ministry of Education Nanjing University Nanjing China School of Information Science and Engineer Shandong University Qingdao China Inner Mongolia DT Electric Power Design Co. Ltd Hohhot China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

Reliable emergency communication after coal mining accidents can ensure carrying out effective rescue work and reducing casualties. This paper presents a digital emergency communication method for coal mining accident based on distributed acoustic sensor (DAS). The rescue information such as number of trapped workers and injuries, oxygen content, gas consistency, food surplus and water surplus is encoded into digital signals and modulated into vibration signals, which can be generated by intrinsically safe vibrator and monitored by DAS. Experimental results demonstrate that this method can achieve simultaneous multi-point communication utilizing the existing optical cables under coal mine conditions, with a symbol error rate (SER) of less than 1.25%, showing enormous application potential.

关键词： Vibrations Optical fibers Optical fiber sensors Symbols Acoustic sensors Optical fiber networks Coal mining

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Broadcast Tree Construction for Secure and Shortest Path Finding Techniques in WSN for Smart Farming Application

Broadcast Tree Construction for Secure and Shortest Path Fin...

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Communications and information Technologies (GCCIT), Global conference on

作者： Ashish Bapurao Manwatkar Dr. Rajesh Keshavrao Deshmukh Aparna Atul Junnarkar Shubhangi Jagdish Kamble Pranita Kishor Kachare Snehal Prashant Latkar Computer Engineering Kalinga University Chattisgarh India Computer Science and Engineering Kalinga University Chattisgarh India Information Technology Vishwakarma Institute of Information Technology Pune India Mechanical Engineering Indira College of Engineering & Management Pune India Computer Engineering Nutan Maharashtra Institute of Engg. and Technology Pune India Electronics and Telecommunications Nutan Maharashtra Institute of Engg. and Technology Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A wireless sensor network (WSN) consists of a large number of distributed sensing devices that use little power and processing capabilities. Data packet switching in sensing devices is often achieved by using multi-hop transmission due to the limited range of connection and the dense distribution of sensor nodes. Dynamic routing in wireless sensor networks (WSN) has been more popular in recent years. In modern times, sensor networks often use the multipath data transmitting approach to optimize system performance by using the available bandwidth. The main objective of this paper is to elucidate the concept of multipath routing, along with its inherent challenges and fundamental justifications for using this approach in sensor networks. This study presents a method for determining the shortest route using the Broadcast Tree Construction (BTC) algorithm, which incorporates data aggregation methods to reduce network overhead during communication. This method is suitable for both cluster networks and Wireless sensor networks. We conducted a comprehensive experimental study to assess the performance of our proposed system in the NS2 environment. We tested the system using four distinct protocols and examined its efficiency. The system conserves 15% of the energy of internal nodes and improves network life by 7% when it operates with SAODV.

关键词： Smart agriculture Wireless sensor networks Costs Scheduling algorithms System performance Clustering algorithms Data aggregation Routing sensors Data communication

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Enhancing Energy Efficiency and Scalability in Wireless sensor networks Using a Slime Mould Algorithm

Enhancing Energy Efficiency and Scalability in Wireless Sens...

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2nd International conference on Signal processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Dharmaraj, N. Ramalingam, S. Ganapathy, S.Harish Abirami, S. Rakesh, S. Reddy, Penumalli Venkata Damsh Rajalakshmi Institute of Technology Department of Ece Thandalam India Sri eashwar college of engineering Department of Ece Coimbatore India Rajalakshmi Institute of Technology Department of Cce Thandalam India Thandalam India

ISBN: (纸本)9798331506452

Wireless sensor networks are vital regarding an assortment of tasks were acquiring information from disseminated sensors is crucial. Nonetheless, maintaining energy conservation while increasing data collecting efficiency is still an exhausting challenge. A promising bio-inspired solution to these challenges is provided by the Slime Mould Algorithm, which emphasizes inspiration via the chemical reactions of slime moulds. To optimize routing circuits for accumulating data from sensor nodes to a central sink node, slime mould algorithm is explicitly implemented. The decentralized algorithm empowers nodes to autonomously modify and rebuild their routes in anticipation of local info and external influences, notably network topology changes or node failures. As demonstrated by our investigations, Slime mould algorithm-based technologies may substantially boost the scalability and robustness of wireless sensor networks, making them suited to dynamic and resource- constrained situations. © 2024 IEEE.

关键词： sensor nodes

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From GPS to Maps: Path Reconstruction Techniques for Understanding People Mobility with Low-Sampling-Rate Data 3

From GPS to Maps: Path Reconstruction Techniques for Underst...

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3rd International conference on intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Rakhman, Arkham Zahri Nugroho, Lukito Edi Widyawan, Widyawan Utami, Nurul Khairiza Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Urban mobility is a critical aspect of city planning and transportation management, with the Global Positioning System (GPS) playing an essential role in analyzing and understanding patterns of human movement. Particularly using smartphones, GPS provides highly detailed digital trace data, enabling in-depth analysis of human movement behavior. However, there are significant challenges when working with GPS datasets that have a low frequency, which often occurs due to storage capacity limitations or data transmission costs. This study explores methodologies for reconstructing movement paths from low-frequency GPS datasets. The process includes several key steps: data pre-processing involving cleaning and normalizing data;aligning GPS data with existing road networks using the nearest-neighbor KD-Tree algorithm;and accurate path reconstruction. In addition, the study develops a method for time interpolation estimation to determine movement times between the aligned coordinate points. The findings of this research indicate that with the proposed approach, movement paths can be reconstructed effectively and accurately, even from data with low sampling frequencies. This approach promises a wide potential application, ranging from transportation infrastructure planning to the development of more sophisticated navigation systems. With ongoing research, the integration of additional sensors and machine learning techniques is anticipated to further enhance the accuracy and utility of this methodology. © 2023 IEEE.

关键词： Global positioning system

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An Optimized Edge Platform for 3D Object Detection in Autonomous Driving

An Optimized Edge Platform for 3D Object Detection in Autono...

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International conference on Electronics, information and Communications (ICEIC)

作者： Kwonseung Bok Yumi Kim Aeri Kim Sang-Seol Lee Kyungho Kim Intelligent Image Processing Research Center Korea Electronics Technology Institute Gyeonggi-do South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Autonomous driving technology has been developed to enhance the safety of vehicles and drivers by recognizing surrounding objects and making appropriate situational judgments using sophisticated sensors. In particular, 3D object detection networks utilizing LiDAR within deep learning models have advanced to achieve accurate object detection. However, performing 3D object detection using LiDAR and advanced sensors requires processing a massive amount of data, making it challenging to handle directly within the vehicle. While external computational resources such as cloud servers can be employed, this approach introduces difficulties in guaranteeing low data exchange latency and ensuring data stability and security. In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications. The proposed edge platform consists of a hardware architecture optimized for processing large volumes of point cloud data and a quantized, lightweight network. This enables the edge platform to handle the entire process of acquiring point cloud data via LiDAR, performing 3D object detection, and executing image post-processing tasks directly on the device.

关键词： Point cloud compression Solid modeling Three-dimensional displays Laser radar Image edge detection Computational modeling Object detection sensors Object recognition Autonomous vehicles

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Innovative Agriculture System with intelligent Integration of IoT and Machine Learning

Innovative Agriculture System with Intelligent Integration o...

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Network, Multimedia and information Technology (NMITCON) International conference on

作者： Ganeshayya I Shidaganti M R Bhavani Bindu C D Sneha C Vanitha Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Traditional farming practices often faces challenges from unpredictable soil conditions and weather patterns, resulting in suboptimal yields and wasted resources. This project aims to develop a Smart Agriculture system that combines Machine Learning (ML) algorithms and Internet of Things (IoT) devices, providing proactive decision-making tools for farmers. By analyzing extensive datasets on soil characteristics, weather patterns, and crop health, ML can identify patterns and anticipate problems. IoT devices offer real-time monitoring of environmental conditions, enabling timely interventions. This integration provides farmers with data-driven insights and automation features, transforming traditional practices. Informed decisions based on accurate information optimize resource allocation, increase productivity, and enhance agricultural sustainability. Ultimately, the synergy between ML and IoT empowers farmers to transform their approach to farm management, reducing uncertainties and improving agricultural efficiency. The main goal of this project is to equip farmers with the necessary tools to make informed decisions and achieve superior outcomes in agriculture.

关键词： Temperature sensors Schedules Accuracy Machine learning algorithms Machine learning sensor systems Real-time systems Internet of Things Servers intelligent sensors

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Analysis of Composite Human Actions Based on Millimeter-Wave Radar

Analysis of Composite Human Actions Based on Millimeter-Wave...

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Signal, information and Data processing (ICSIDP), IEEE International conference on

作者： Xiaofeng Wang Yongtao Ma Haibo Zhao Xiaolin Wang School of Microelectronics Tianjin University China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Radar-based human sensing has emerged as a promising solution for non-contact human-computer intelligent interaction. Current existing works typically preset a set of actions and use deep learning algorithms to extract features for classification. These studies usually focus only on recognizing body activities (such as walking, standing, and sitting) or gestures (such as sliding, pushing, and rotating). When recognizing activities, hand movements are often ignored, while gesture recognition typically requires the body to remain still. However, people often make gestures during body activities to convey additional information. In this paper, we investigate the feasibility of using a millimeter-wave radar sensor to simultaneously recognize human activities and gestures when composite actions involving both body movements and gestures are present in the sensor’s field of view. First, we establish the kinematic model of composite actions. Next, we analyze the Doppler-Range-Angle image features of these composite actions. Experimental results demonstrate that the sensor can simultaneously capture the motion information of both the body and the hand. Additionally, the motion characteristics of the body and hand show a certain degree of spatial distinction.

关键词： Hands Legged locomotion Deep learning Kinematics Gesture recognition Millimeter wave radar Feature extraction Radar signal processing sensors Monitoring

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A Spatial Alignment Framework Using Geolocation Cues for Roadside Multi-View Multi-sensor Fusion

A Spatial Alignment Framework Using Geolocation Cues for Roa...

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International conference on intelligent Transportation

作者： Zhiguo Zhao Yong Li Yunli Chen Xiaoting Zhang Rui Tian Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Science Technology Beijing University of Technology Beijing China

Multi-modal sensor fusion plays a vital role in achieving high-quality roadside perception for intelligent traffic monitoring. Unlike on-board sensors in autonomous driving, roadside sensors present heightened calibration complexity, posing a challenge to spatial alignment for data fusion. And existing spatial alignment methods typically focus on one-to-one alignment between cameras and radar sensors and require precise calibration. However, when applied to large-scale road-side monitoring networks, these methods can be difficult to implement and may be vulnerable to environmental influences. In this paper, we present a spatial alignment framework that utilizes geolocation cues to enable multi-view alignment across distributed multi-sensor systems. In this framework, a deep learning-based camera calibration model combined with angle and distance estimation is used for monocular geolocation estimation. A camera parameter approaching method is then used to search for pseudo camera parameters that can tolerate inevitable calibration errors in practice. Finally, the geolocation information is then used for data association between Light Detection and Ranging (LiDAR) and cameras. The framework has been conducted and tested at several intersections in Hangzhou. Experimental results show that the framework can achieve geolocation estimation errors of less than 1.1 m for vehicles traversing the monitored zone, demonstrating the framework's ability to accomplish spatial alignment with a singular execution, and apply it in extensive large-scale roadside sensor fusion scenarios.

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