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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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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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Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第3期37卷 e5655-e5655页

作者： Rajkumar, K. Paramasivan, B. Natl Engn Coll Dept CSE Kovilpatti 628503 Tamil Nadu India Natl Engn Coll Dept Informat Technol Kovilpatti Tamil Nadu India

Many monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target-based monitoring in order to achieve availability. Designing a Siamese network to identify redundant nodes to replace faulty nodes to improve the availability in visual sensor network. Identifying redundant nodes based on Siamese network to replace faulty nodes in the network. If average energy, entropy, transmission delay and network coverage are lesser than threshold value means, then the node is recognized

关键词： availability energy entropy fake node image similarity redundant node Siamese network visual sensor network

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Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for visual sensor network

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IEEE ACCESS 2020年 8卷 165720-165739页

作者： Dhara, Sobhan Kanti Sen, Debashis Swamy, M. N. S. IIT Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine transform (DCT) basis. The relation is that the eigenvalues of the Laplacian with proper boundary condition form the diagonal elements of the diagonal matrix generated by the DCT operation on the Laplacian. Exploiting this relation, we propose a focus measure which works on the DCT coefficients reflecting the spatial-domain Laplacian operation. Certain simplifications allow our focus measure computation through hardware-friendly integer multiplication and summation, where matrix multiplication involves just N scalar multiplications for an N x N 2D signal. Finally, we propose an approach which suitably fuses multi-focus images or video frames in DCT based image or video coding framework through detection of properly focused area and neighborhood consistency analysis. We show that our proposed approach is hardware-friendly, computationally simple, and is fast enough for VSN. Through experimental results, we show that our approach outperforms the relevant state-of-the-art in multi-focus image fusion for VSN both quantitatively and subjectively. We also show that our approach is effective in comparison to the state-of-the-art and a few latest generic multi-focus image fusion techniques in terms of quantitative and subjective evaluations.

关键词： Anti-diagonal matrix discrete cosine transform multi-focus image fusion visual sensor network

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A new image size reduction model for an efficient visual sensor network

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JOURNAL OF visual COMMUNICATION AND IMAGE REPRESENTATION 2019年 63卷 102573-000页

作者： Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Blumenstein, Michael Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England UTS Sydney NSW Australia

Image size reduction for energy-efficient transmission without losing quality is critical in visual sensor networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime. (C) 2019 Elsevier Inc. All rights reserved.

关键词： visual sensor network Image size reduction Inter-redundancy Intra-redundancy Energy consumption Quality of the image

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Critical Location Spatial-Temporal Coverage Optimization in visual sensor network

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sensorS 2019年第19期19卷 4106-4106页

作者： Xiong, Yonghua Li, Jing Lu, Manjie China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase Spatial-temporal Coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches.

关键词： visual sensor network spatial-temporal coverage sensor node scheduling two-phase coverage-enhancing method

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A Practical High Efficiency Video Coding Solution for visual sensor network using Raspberry Pi Platform 12

A Practical High Efficiency Video Coding Solution for Visual...

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IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)

作者： Thao Nguyen Thi Huong Huy Phi Cong Tien Vu Huu Xiem HoangVan PTIT Hanoi Vietnam VNU Univ Engn & Technol Hanoi Vietnam

ISBN: (纸本)9781538666890

visual sensor network (VSN) has recently emerged as a promising solution for tremendous range of new vision-sensor based applications, from video surveillance, environmental monitoring to remote sensing. However, the practical VSN currently faces to the visual processing and transmitting problems due to the limitation of power at sensor nodes and the restriction of transmission bandwidth. In this context, the selection of a suitable video compression algorithm is utmost important task for achieving a practical VSN. To address this problem, this paper introduces a practical Raspberry Pi based High Efficiency Video Coding (HEVC) solution for visual sensor networks. The selected video coding solution is one of the most up-to-date compression engines but still achieving the low complexity capability. Experimental results show that the proposed video coding architecture has good compression performance with acceptable complexity performance.

关键词： visual sensor network Raspberry Pi HEVC

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On 2-Angular-Coverage in Wireless visual sensor network Deployment for 3D Indoor Monitoring 17

On 2-Angular-Coverage in Wireless Visual Sensor Network Depl...

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ACM Southeast Regional Conference

作者： Wang, Z. Brown, T. Shan, T. Wang, F. Xue, J. Univ Mississippi Sch Engn University MS 38677 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781450350242

Extensive efforts have been devoted to achieve k-coverage for wireless sensor networks (WSNs) deployed in the sensing area by considering omni-directional sensing ranges as seen in scalar temperature sensors, where the larger k is, fault tolerance and monitoring quality is better. However, in wireless visual sensor networks (WVSNs), the sensing range of a visual sensor is often directional and modeled as a convex triangular shape in 2D and a pyramid-shape in 3D, which renders the k-coverage solutions proposed for WSNs incapable of solving the problem in WVSNs. Moreover, a number of WVSN applications consider 3D indoor space monitoring, which makes previous solutions proposed for 2D WVSN coverage less effective. In this paper, we take an initial step to tackle this challenging problem by considering 2-coverage for WVSNs in 3D indoor space monitoring. To maximize the information that can be captured by 2 visual sensors covering a 3D location, we propose to deploy the visual sensors from different directional angles and further extend 2-coverage to "2-angular-coverage" to denote such unique requirements in WVSNs. We demonstrate our simulated WVSN testing platform which emulates a 3D space and highlights our objective to provide efficient 2-angular-coverage in the indoor environment by minimizing the number of visual sensors required to cover the full regions through precision angle view monitoring. We formulate the general problem in a continuous space and then discretize our model via a lattice based approach. In addition, we also create a strategy to determine the degree of coverage for the WVSN, where a given location needs to be covered by at least 2 visual sensors that fulfill the angular-coverage requirement. We design an efficient greedy heuristic and an enhanced-Depth First Search algorithm to solve the discrete version problem, where the latter, if given enough time can return an optimal solution. By extensive simulations, we demonstrate that both our solution

关键词： visual sensor network k-coverage depth first search heuristic

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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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Joint Prior Learning for visual sensor network Noisy Image Super-Resolution

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sensorS 2016年第3期16卷 288-288页

作者： Yue, Bo Wang, Shuang Liang, Xuefeng Jiao, Licheng Xu, Caijin Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist EducInt Res Ctr Intelligent Percept & Com Xian 710071 Peoples R China Kyoto Univ Grad Sch Informat Dept Intelligence Sci & Technol Kyoto 6068501 Japan

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception.

关键词： visual sensor network image super-resolution prior learning EM algorithm image denoising

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