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visual sensor network Task Scheduling Algorithm at Automated Container Terminal

IEEE sensorS JOURNAL 2022年第6期22卷 6042-6051页

作者： Mi, Chao Chen, Jian Zhang, Zhiwei Huang, Shifeng Postolache, Octavian Shanghai Maritime Univ Minist Educ Container Supply Chain Technol Engn Res Ctr Shanghai 201306 Peoples R China Shanghai Maritime Univ Logist Engn Sch Shanghai 201306 Peoples R China Shanghai SMUVis Smart Technol Ltd Shanghai 201306 Peoples R China Shanghai Maritime Univ Logist Sci & Engn Res Inst Sch Shanghai 201306 Peoples R China Inst Telecomunicacoes P-1049001 Lisbon Portugal Univ Lisbon ISCTE Inst P-1649026 Lisbon Portugal

The visual sensor network (VSN) is an important part of the automated container terminal. VSN present also a of problems such as the redundant number of visual sensors and limited computing resources. Different vision sensors are reported in literature when about computing resources (CR) reduced number of publications provides information about required CR as part of visual sensor network. In this context the paper propose solution that transforms the visual sensor network terminal task scheduling process into Markov Decision Process. Thus, a visual sensor network terminal task scheduling algorithm based on Deep-Q Learning is considered. In this algorithm, an innovative return value function is proposed to achieve better algorithm convergence. To verify the effectiveness of the model, several experiments were carried out under different conditions. The result shows that the recognition rate is improved by using the proposed algorithm. Based on considered method the number of visual sensors can be reduced, that conducts to a rational use of limited computing resources. At the same time cost reduction is also provided that is an important requirement of port operation optimization.

关键词： Task analysis sensors Containers visualization Vision sensors Wireless sensor networks Scheduling algorithms Automated container terminal computing resources deep-Q learning visual sensor network

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visual sensor network lifetime maximization by prioritized scheduling of nodes

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JOURNAL OF network AND COMPUTER APPLICATIONS 2013年第1期36卷 409-419页

作者： Hooshmand, M. Soroushmehr, S. M. R. Khadivi, P. Samavi, S. Shirani, S. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable. The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： visual sensor network Directional sensors visual coverage Priority based algorithms Genetic algorithm PSO algorithm

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Improving response time in time critical visual sensor network applications

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AD HOC networkS 2014年第Dec.期23卷 65-79页

作者： Felemban, Emad Sheikh, Adil A. Manzoor, Muhammad Asif Umm Al Qura Univ SimplicityLabs Mecca Saudi Arabia Umm Al Qura Univ Sci & Technol Unit Mecca Saudi Arabia Umm Al Qura Univ Dept Comp Engn Mecca Saudi Arabia

Wireless sensor network (WSN) consisting of nodes equipped with cameras or advanced low-cost image sensors is known as a visual sensor network (VSN). The main function of VSN is to capture images and send them to sink nodes for processing. Common applications of a VSN are surveillance, tracking, crowd management, scientific research, etc. Such applications require large amounts of data to be exchanged between camera nodes and sink. Image data is considerably larger than common sensor data such as temperature, humidity, and pressure. For data delivery in VSNs, the communication is constrained by many stringent QoS requirements like delay, jitter and data reliability. Moreover, due to the inherent constraints of WSN such as limited energy, low CPU power, and insufficient memory;the architect of VSN must choose appropriate topology, image compression algorithms, and communication protocols depending on application. In literature, different techniques have been proposed to resolve the bandwidth requirements for the VSN. The majority of these techniques are based on the compression of visual data. This paper focuses on one of these aspects, namely the communication protocol for VSN. In this paper, we present Priority Routing Framework for Image Transmission (PRoFIT), a new routing framework for VSN to deliver critical imagery information with system's time constraint. We demonstrate that PRoFIT improves response time in various VSN applications as compared to priority-less routing techniques. We have implemented PRoFIT along with an image processing application using Contiki and simulated it on Cooja simulator to support our claim. (C) 2014 Elsevier B.V. All rights reserved.

关键词： visual sensor network Priority based routing Contiki operating system Real-time VSN applications Bit-plane encoding Progressive image transmission

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Spread Spectrum visual sensor network Resource Management Using an End-to-End Cross-Layer Design

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IEEE TRANSACTIONS ON MULTIMEDIA 2011年第1期13卷 125-131页

作者： Bentley, Elizabeth S. Kondi, Lisimachos P. Matyjas, John D. Medley, Michael J. Suter, Bruce W. USAF Res Lab Rome NY 13441 USA Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

In this paper, we propose an approach to manage network resources for a direct sequence code division multiple access (DS-CDMA) visual sensor network where nodes monitor scenes with varying levels of motion. It uses cross-layer optimization across the physical layer, the link layer, and the application layer. Our technique simultaneously assigns a source coding rate, a channel coding rate, and a power level to all nodes in the network based on one of two criteria that maximize the quality of video of the entire network as a whole, subject to a constraint on the total chip rate. One criterion results in the minimal average end-to-end distortion amongst all nodes, while the other criterion minimizes the maximum distortion of the network. Our experimental results demonstrate the effectiveness of the cross-layer optimization.

关键词： Code division multiple access (CDMA) convolutional codes cross-layer H.264 joint source-channel coding multimedia communications power control resource allocation spread spectrum visual sensor network

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A study on visual sensor network cross-layer resource allocation using quality-based criteria and metaheuristic optimization algorithms

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APPLIED SOFT COMPUTING 2015年 26卷 149-165页

作者： Pandremmenou, K. Kondi, L. P. Parsopoulos, K. E. Univ Ioannina Dept Comp Sci & Engn GR-45110 Ioannina Greece

visual sensor networks (VSNs) consist of spatially distributed video cameras that are capable of compressing and transmitting the video sequences they acquire. We consider a direct-sequence code division multiple access (DS-CDMA) VSN, where each node has its individual requirements in compression bit rate and energy consumption, depending on the corresponding application and the characteristics of the monitored scene. We study two optimization criteria for the optimal allocation of the source and channel coding rates, which assume discrete values, as well as for the power levels of all nodes, which are continuous, under transmission bit rate constraints. The first criterion minimizes the average distortion of the video received by all nodes, while the second one minimizes the maximum video distortion among all nodes. The resulting mixed integer optimization problems are tackled with a modern optimization algorithm, namely particle swarm optimization (PSO), as well as a hybrid scheme that combines PSO with the deterministic Active-Set optimization method. Extensive experimentation on interference-limited as well as noisy environments offers significant intuition regarding the effectiveness of the considered optimization schemes, indicating the impact of the video sequence characteristics on the joint determination of the transmission parameters of the VSN. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Hybrid algorithms Metaheuristic optimization Particle swarm optimization Power control Resource allocation visual sensor network

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Collaborative Occupancy Reasoning in visual sensor network for Scalable Smart Video Surveillance

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2010年第3期56卷 1997-2003页

作者： Cho, Yongil Lim, Sang Ok Yang, Hyun Seung Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea

Video surveillance is a popular consumer application that is used for various purposes such as public safety, facilities surveillance, and traffic monitoring. In a general video surveillance system, video streams from cameras are sent to a control center and operators monitor the videos. But human operator monitoring of the views every moment of every day is almost impossible;so, smart surveillance systems are required, systems that are capable of automated scene analysis. There are a number of studies to enable smart video surveillance in a multi-camera network. Most of the studies, however, treat central processing approaches in which a scene analysis is processed inside a central server domain once all available information has been collected in the server. Such approaches require tremendous efforts in building the system and, moreover, limit the scalability. To accomplish scalable smart video surveillance, an inference framework in visual sensor networks is necessary, one in which autonomous scene analysis is performed via distributed and collaborative processing among camera nodes without necessity for a high performance server. In this paper, we propose a collaborative inference framework for visual sensor networks and an efficient occupancy reasoning algorithm that is essential in smart video surveillance based on the framework. We estimate the existence probabilities for every camera and combine them using the work-tree architecture in a distributed and collaborative manner. We aim for practical smart video surveillance systems(1).

关键词： Smart Video Surveillance Distributed Inference visual sensor network Occupancy Reasoning

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Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第18期75卷 11469-11491页

作者： Lin, Kuicheng Wang, Xue Tan, Yuqi Tsinghua Univ Dept Precis Instrument State Key Lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China

3D face reconstruction is an efficient method for pedestrian recognition in noncooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.

关键词： visual sensor network Optimal face acquisition 3D face reconstruction Self-adaptive morphable model Particle swarm optimization

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Genetics Based Compact Fuzzy System for visual sensor network

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Computer Systems Science & Engineering 2023年第4期45卷 409-426页

作者： Usama Abdur Rahman C.Jayakumar Deepak Dahiya C.R.Rene Robin Department of Computer Science and Engineering Jerusalem College of EngineeringChennaiTamil Nadu600100India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringChennaiTamil Nadu602117India College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044India

As a component of Wireless sensor network(WSN),visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-information out of *** applications range from health care monitoring to military *** a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O *** this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as *** innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the ***,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into *** propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual *** architecture is designed based on Mamdani’s fuzzy *** parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH *** inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of *** proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive *** algorithmbased machine learning optimizes the interpretability aspect of fuzzy *** results are obtained using *** result shows that the classification acc

关键词： visual sensor network fuzzy system genetic based machine learning mobile sink efficient energy life of network

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Human Mobility Monitoring in Very Low Resolution visual sensor network

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sensorS 2014年第11期14卷 20800-20824页

作者： Bo, Nyan Bo Deboeverie, Francis Eldib, Mohamed Guan, Junzhi Xie, Xingzhe Nirio, Jorge Van Haerenborgh, Dirk Slembrouck, Maarten Van de Velde, Samuel Steendam, Heidi Veelaert, Peter Kleihorst, Richard Aghajan, Hamid Philips, Wilfried Gent Univ IMinds B-9000 Ghent Belgium Ambient Intelligence Res Lab Stanford CA 94305 USA

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 x 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

关键词： visual sensor network low resolution imagery distributed processing tracking mobility analysis

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Heterogeneous Feature Fusion-based Optimal Face Image Acquisition in visual sensor network

Heterogeneous Feature Fusion-based Optimal Face Image Acquis...

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32nd Annual IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Lin, Kuicheng Wang, Xue Cui, Sujin Tan, Yuqi Tsinghua Univ Dept Precis Instrument State Key Lab Precis Measurement Technol & Instru Beijing Peoples R China

ISBN: (纸本)9781479961139

High quality face image acquisition from huge video data obtained in visual sensor network is of great significance in applications related to face processing, such as face recognition and reconstruction. This paper proposes an optimal face image acquisition method in visual sensor network, which is based on collaborative face frames acquisition and heterogeneous feature fusion-based face quality assessment. Gaussian-probability-distribution-based multi-view data fusion and kalman filter are used for collaborative target localization and tracking. To achieve primary screening of face frames, a lightweight face frames quality evaluation method is presented. Importantly, new face quality assessment criterion calculation methods are proposed to make fine screening of face images more applicable in visual sensor network. The new face quality assessment criterion calculation methods are based on heterogeneous feature fusion of pedestrian tracking and static face image features analysis. Fuzzy inference engine is used to combine these criteria to generate a face quality assessment score. Experimental results show that the proposed method can acquire optimal face images accurately and robustly.

关键词： optimal face image acquisition fuzzy inference engine visual sensor network face quality assessment

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