Visual Sensor Networks Research Articles

BCS-Based Encoding Schemes for Monoview and Multiview Visual Configurations in WVSN Data Gathering: A Survey

Wireless Visual Sensor Network (WVSN) has become a valuable tool in addressing the evolving needs of modern monitoring systems. Encoding in WVSNs is a multifaceted process that involves compressing visual data, optimizing energy consumption, ensuring error resilience, and adapting to various network and application requirements. The associated lightweight encoders and the demand for less storage space make block compressive sensing (BCS) techniques suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Based on the number of visual perspectives or camera angles available within a network for data capture, there are two primary congurations: monoview and multiview. This paper provides a comprehensive survey of dierent BCS-based encoding schemes used for data-gathering in both monoview and multiview scenarios within WVSNs. A comparative study of these algorithms based on compression level, computational complexity, relative gain in encoder energy, and reconstruction quality is performed. A BCS-based joint encoding scheme for multiview conguration that ensures a relatively high compression level is also proposed in this paper.

Optimized Wireless Visual Sensor Networks for Guaranteed Target Coverage With Maximum Lifetime and Least Data Delivery Latency

Optimized Wireless Visual Sensor Networks for Guaranteed Target Coverage With Maximum Lifetime and Least Data Delivery Latency

Camera management in wireless visual sensor networks based on a quality aware resource allocation scheme

Camera management in wireless visual sensor networks based on a quality aware resource allocation scheme

Query-Based Multiview Detection for Multiple Visual Sensor Networks.

In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a detailed ground plane feature. This feature is formed by projecting convolutional feature maps from multiple viewpoints and fusing them using uniform weighting. However, simply aggregating data from all cameras is not ideal due to different levels of occlusion depending on object positions and camera angles. To overcome this, we introduce QMVDet, a new query-based learning multiview detector, which incorporates an innovative camera-aware attention mechanism for aggregating multiview information. This mechanism selects the most reliable information from various camera views, thus minimizing the confusion caused by occlusions. Our method simultaneously utilizes both 2D and 3D data while maintaining 2D-3D multiview consistency to guide the multiview detection network's training. The proposed approach achieves state-of-the-art accuracy on two leading multiview detection benchmarks, highlighting its effectiveness for IoT-based multiview detection scenarios.

Underwater Image Enhancement Algorithm Based on Adversarial Training

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.

Optimisation of spatiotemporal context-constrained full-view area coverage deployment in camera sensor networks via quantum annealing

Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.

E-TPE: E fficient T humbnail- P reserving E ncryption for Privacy Protection in Visual Sensor Networks

When visual sensor networks (VSNs) enter daily life in society, they not only bring great convenience but also cause people to worry about privacy. Traditional image encryption uses the snowflake effect to protect the privacy, but this can compromise the usability of directly browsing images captured by nodes in VSNs. Recently, thumbnail-preserving encryption (TPE) has been proposed to balance image usability and privacy. However, the Markov chain’s limited connectivity and high time cost, which result in low efficiency, may preclude its application in VSNs. Motivated by this, we propose a novel TPE scheme to protect the image collected in VSNs’ privacy efficiently. We first deeply study the bijective relationship between the three-pixel group and its rank, and then propose a new number method for both, based on which we design the efficient rank mapping. Subsequently, an efficient thumbnail-preserving image encryption via three-pixel rank mapping (E-TPE) is designed, achieving NR security and enhancing the Markov chain’s connectivity. The experiments demonstrate the scheme’s efficiency; that is, it is currently the quickest ideal-TPE scheme with NR security, and a good balance is achieved between usability and privacy.

Full View Maximum Coverage of Camera Sensors: Moving Object Monitoring

The study focuses on achieving full view coverage in a camera sensor network to effectively monitor moving objects from multiple perspectives. Three key issues are addressed: camera direction selection, location selection, and moving object monitoring. There are three steps to maximize coverage of moving targets. The first step involves proposing the Maximum Group Set Coverage (MGSC) algorithm, which selects the camera sensor direction for traditional target coverage. In the second step, a composed target merged from a set of fixed directional targets represents multiple views of a moving object. Building upon the MGSC algorithm, the Maximum Group Set Coverage with Composed Targets (MGSC-CT) algorithm is presented to determine camera sensor directions that cover subsets of fixed directional targets. Additionally, a constraint on the number of cameras is imposed for camera location selection, leading to the study of the Maximum Group Set Coverage with Size Constraint (MGSC-SC) algorithm. Each of these steps formulates a problem on group set coverage and provides an algorithmic solution. Furthermore, improved versions of MGSC-CT and MGSC-SC are developed to enhance the coverage speed. Computer simulations are employed to demonstrate the significant performance of the algorithms.

Sensor Placement Optimization of Visual Sensor Networks for Target Tracking Based on Multi-Objective Constraints

With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.

Critical Density for K-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence

Critical Density for <i>K</i>-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence

Wand-Based Calibration of Unsynchronized Multiple Cameras for 3D Localization.

Three-dimensional (3D) localization plays an important role in visual sensor networks. However, the frame rate and flexibility of the existing vision-based localization systems are limited by using synchronized multiple cameras. For such a purpose, this paper focuses on developing an indoor 3D localization system based on unsynchronized multiple cameras. First of all, we propose a calibration method for unsynchronized perspective/fish-eye cameras based on timestamp matching and pixel fitting by using a wand under general motions. With the multi-camera calibration result, we then designed a localization method for the unsynchronized multi-camera system based on the extended Kalman filter (EKF). Finally, extensive experiments were conducted to demonstrate the effectiveness of the established 3D localization system. The obtained results provided valuable insights into the camera calibration and 3D localization of unsynchronized multiple cameras in visual sensor networks.

Scene-Constrained Neural Radiance Fields for High-Quality Sports Scene Rendering Based on Visual Sensor Network

Scene-Constrained Neural Radiance Fields for High-Quality Sports Scene Rendering Based on Visual Sensor Network

An Improved MPSO Coverage Enhancement Algorithm for Directional Video Sensor Networks

An Improved MPSO Coverage Enhancement Algorithm for Directional Video Sensor Networks

An improved coverage optimization method for video sensor networks based on whale optimization algorithm

An improved coverage optimization method for video sensor networks based on whale optimization algorithm

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

SummaryMany 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.

Maximal Weighted Coverage Deployment of UAV-Enabled Rechargeable Visual Sensor Networks

Due to the generation and transmission of video data, the high energy consumption rates of wireless nodes result in short lifetime of a visual sensor network, which has been commonly deployed in 3D space. In many applications, lots of targets need to be covered, but the budgets of both deployment and maintenance are limited. Therefore, how to maximize total weighted target coverage while prolonging network lifetime is of great significance. This work investigates the maximum weighted target coverage deployment of a rechargeable visual sensor network charged by an unmanned aerial vehicle (UAV) in 3D space, which should ensure network permanence and satisfy the budgets of both deployment and maintenance. In this problem, since UAV has a limited energy capacity, it is allowed to return to the depot for energy replenishment more than once in each charging period of wireless nodes so as to increase target coverage. After formulating this problem as a mixed integer nonlinear program, this work proposes a greedy heuristic and a particle swarm optimizer to approximately solve this NP-hard problem. Extensive simulation results reveal that the latter can deliver a better solution despite consuming more time than the former.

Enhanced Throughput and Power for Forth level of Heterogeneity using Sector based clustering Protocol (SBCP)

Over the course of the last two decades, a considerable body of research has been devoted to devising methodologies aimed at prolonging the operational longevity of networks and enhancing data transfer rates. Notably, LEACH, PEGASIS, HEED, TEEN, and SEP have emerged as prominent clustering algorithms, each offering potential for augmenting network stability and throughput, thus concurrently extending the lifespan of camera sensor networks. Within this academic investigation, we present the Sector Based Clustering Protocol (SBCP) tailored explicitly to Third Level Heterogeneity, posited as an innovative approach fostering amplified data throughput and heightened power efficiency. Our proposed protocol attains a noteworthy 70% increase in data transfer rates, while rigorously upholding the quintessential requisites of network stability and sustained existence. The empirical evaluation, conducted through the implementation of sector-based clustering in MATLAB, includes a comparative analysis against LEACH, SEP, and ZSEP. The results obtained from this rigorous comparison underscore the superior performance of the proposed SBCP in terms of optimal data throughput, accompanied by marked enhancements in network stability vis-à-vis the aforementioned clustering algorithms.

Multiobjective Optimized Deployment of Edge-Enabled Wireless Visual Sensor Networks for Target Coverage

In wireless visual sensor networks, the generation and transmission of huge amounts of image data consume much energy of sensor nodes, and their routing and processing take quite a long time. It is of great importance to shorten event reporting delay and prolong network lifetime, which can be achieved by the appropriate deployment of edge nodes that can not only collect but also process data. This work investigates how to jointly optimize sensor node deployment, edge node deployment, data routing, and data offloading to minimize the number of deployed sensor nodes, the number of deployed edge nodes, and event reporting delay and maximize network lifetime. We formulate this problem as a mixed integer nonlinear program and propose a multiobjective differential evolution algorithm to solve it. A large number of simulation results demonstrate that it can deliver a more accurate Pareto set than the nondominated sorting genetic algorithm III.

Online Data Transmission Reduction Scheme for Energy Conservation in Wireless Video Sensor Networks

Online Data Transmission Reduction Scheme for Energy Conservation in Wireless Video Sensor Networks

Enhancement of Performance Metrics of Heterogeneous Wireless Camera Sensor Network with Functionalization of Extensive Zonal Stable Election Protocol using Threshold Amplification and Residual Energy

We report a new improved energy competent and optimized data packet flow protocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.The existing Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method with finite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remained on the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better than existing EZ-SEP, Z-SEP, SEP, LEACH, Mod-Leach protocols during entire stability timeframe. The notable achievement is also reported in throughput as the same is enhanced more than by ~ 39%, 43%, 49% ,56%, 53% while total packets communicated with base station has been increased more than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.