Virtual Sensor Network Research Articles

Visiting experience of digital museum interior design based on virtual reality and sensor networks

China's computer technology and image sensor technology are both rapidly developing, and the related advantages of digital holography technology are gradually expanding. These technologies have been applied in many fields. For example, three-dimensional measurement, deformation measurement, and anti-counterfeiting fields. This article combines holographic technology and virtual reality technology, and applies them to the interior design of digital museums and the optimization of auxiliary visits. A corresponding mathematical calculation model is established for digital holographic images, and in-depth research is conducted on this mathematical calculation model. Relevant personnel have also conducted relevant research on commonly used digital holography technology, and provided a detailed introduction to the principles of virtual reality technology. They have also conducted in-depth research on the specific working principles of VR technology and the application technology of virtual reality systems. Finally, an analysis and research were conducted on the development of Chinese museums, and a new perspective was proposed on the indoor space of museums under the new digital concept, which has certain reference significance. Currently, Chinese museums are thriving, so deepening the interior space of museums is very important. This article hopes to provide some guidance and practical value for the development of Chinese museums.

Application of voice enhancement based on virtual sensor network in enterprise financial management

Based on virtual sensor network, this paper explores the application of voice enhancement technology in enterprise financial management. Virtual sensor network is a low cost, easy to expand and flexible network system based on simulation and simulation technology. In this network system, the virtual voice sensor node can collect and process the voice information inside the enterprise. Speech enhancement technology uses signal processing and machine learning algorithms to reduce noise, enhance and clarify speech signals, thereby improving the accuracy and reliability of speech data. By constructing virtual voice sensor nodes in virtual sensor networks and applying voice enhancement technology, the research results show that enterprises can collect and process voice data more accurately in the process of financial management. Voice enhancement technology can reduce the impact of environmental noise on financial data and improve the clarity of voice signals, so as to effectively improve the quality of data. Through the voice enhancement technology based on virtual sensor network, high-quality voice data acquisition and processing can be realized in the financial management of enterprises, improve the accuracy and effect of financial decision-making, and reduce the cost and complexity of enterprises.

A virtual sensor network for pressure distribution inside a multi-zone building based on spatial adjacency relationships and multivariate adaptive regression spline

Airflows within multi-zone buildings cause variations in environmental parameters both vertically and horizontally. Although it is difficult to monitor the airflows through existing sensors, pressure monitoring allows the identification of airflows, since airflows across zones are induced by pressure differences. This study proposes a data-driven method for establishing a virtual pressure sensor network within a multi-zone building. Three scenarios with various sample sizes are applied to evaluate their impacts on the analyzed results, and scenario 3 is identified as the most suitable database for creating a virtual sensor network. The single-sided or multi-sided spatial adjacency relationships are determined for each zone, and the MARS model is then applied to develop the relationships among the variables, with key physical sensors designated by the frequency analysis of independent variables. The final sensor network comprises four key physical sensors, thirteen direct virtual sensors, and five indirect virtual sensors. All virtual sensors can achieve excellent predicted results with high R2 values and low MAE and RMSE, with mean values of 0.997 Pa and 1.269 Pa, respectively. Notably, the difference in accuracy between direct and indirect virtual sensors is influenced by spatial adjacency relationships. This study demonstrates a reliable virtual sensor network for predicting pressure variations and provides a reference for the identification of airflows and the efficient adjustment of HVAC systems.

Learning a simulation-based visual policy for real-world peg in unseen holes.

This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation and adapting to arbitrary unseen shapes in the real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy from the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve a generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN + CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configuration of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2-3s, using only hundreds of auto-labeled samples for the SN transfer.

Survivability Mapping Strategy for Virtual Wireless Sensor Networks for Link Failures in the Internet of Things

In the case of virtual wireless sensor networks, a link-failure-oriented survivable virtual sensor network mapping algorithm (F-SVNE) is proposed to address the issue of link failure in the underlying wireless sensor network. First, the algorithm utilizes the fast routing strategy and creates a backup route set based on a multi-path selection algorithm to reduce the delay in fault recovery caused by path selection. Second, the survivable virtual sensor network mapping algorithm is adopted based on the routing set. Finally, in the mapping stage, the efficiency and reliability of the algorithm are comprehensively considered, and the path with the largest survival probability is selected from the backup route set of the faulty link to remap the virtual link affected by the link failure. Empirical results demonstrate that the F-SVNE algorithm can efficaciously lessen the failure recovery delay, and improve the long-term average revenue–cost ratio and average failure recovery rate.

A New Method for Spatial Estimation of Water Quality Using an Optimal Virtual Sensor Network and In Situ Observations: A Case Study of Chemical Oxygen Demand.

Accurate water quality estimation is important for water environment monitoring and water resource management and has emerged as a pivotal aspect of ecological rehabilitation and sustainable development. However, due to the strong spatial heterogeneity of water quality parameters, it is still challenging to obtain highly accurate spatial patterns of them. Taking chemical oxygen demand as an example, this study proposes a novel estimation method for generating highly accurate chemical oxygen demand fields in Poyang Lake. Specifically, based on the different water levels and monitoring sites in Poyang Lake, an optimal virtual sensor network was first established. A Taylor expansion-based method with integration of spatial correlation and spatial heterogeneity was developed by considering environmental factors, the optimal virtual sensor network, and existing monitoring stations. The proposed approach was evaluated and compared with other approaches using a leave-one cross-validation process. Results show that the proposed method exhibits good performance in estimating chemical oxygen demand fields in Poyang Lake, with mean absolute error improved by 8% and 33%, respectively, on average, when compared with classical interpolators and remote sensing methods. In addition, the applications of virtual sensors improve the performance of the proposed method, with mean absolute error and root mean squared error values reduced by 20% to 60% over 12 months. The proposed method provides an effective tool for estimating highly accurate spatial fields of chemical oxygen demand concentrations and could be applied to other water quality parameters.

English

Virtualization of the Sensor network empowers the chance of distribution of actual normal assets in various partner uses. The current thesis centers around tending to the unique transformation of currently doled out virtual sensor network assets in answering timespan changing uses requests. We have suggested n advancement structure which powerfully designates implementations in Sensor's hubs while representing qualities with the remote Sensor's climate restrictions. This likewise considers the extra energy utilization connected with initiating new hubs and moving currently dynamic applications. Different goal capacities connected with the accessible energies with hubs were broken down. The suggested structure was assessed with recreation assuming reasonable boundaries out of genuine Sensor's hubs with sending uses in surveying proficiency of proposition.

Blockchain-enabled Internet of Things and Virtual Sensor Networks, Mobile Cloud and Edge Computing Systems, and Predictive Modeling and Cognitive Data Fusion Techniques in Big Data-driven Digital Twin Urban Geopolitics and Immersive Hyper-Connected Virtual Spaces

Blockchain-enabled Internet of Things and Virtual Sensor Networks, Mobile Cloud and Edge Computing Systems, and Predictive Modeling and Cognitive Data Fusion Techniques in Big Data-driven Digital Twin Urban Geopolitics and Immersive Hyper-Connected Virtual Spaces

A fuzzy logic technique for virtual sensor networks

A virtual sensor network is designed to infer an approximation of real data when the physical sensor network cannot provide it, due to technical or economic reasons. In this paper, we propose an approach to model virtual sensor networks based on fuzzy logic. It relies on the hypothesis that the readings of the physical sensors are correlated. In particular, the virtual sensor network is able to learn the relationships between the physical quantities detected by the sensors and model them through fuzzy rules, which provide an approximation of the real values. In the proposed model, a set of fuzzy rules is derived for each physical sensor. It will provide an accurate approximation of the sensor readings when needed (e.g. sensor malfunction). The proposed approach has been designed as an extension of the well known Tree Routing protocol, used in sensor networks for transmitting sensed data to the base station. It has been implemented in Snlog, a language derived from Datalog that supports the development of distributed algorithms for Wireless Sensor Networks in a declarative way. The experimental evaluation reported in the paper shows the effectiveness and efficacy of our solutions.

Effective and Secure vWSN Applications in a Virtualized Cloud Computing Environment

Security is one of the key concerns in cloud computing. We are proposing Virtual Wireless Sensor Network (vWSN) which is one of the key features of cloud computing in the area of agriculture. Our main focuses are effective and secure vWSN in virtualized cloud computing environment with enhanced security to detect environmental monitoring, humidity monitoring, soil moisture, air quality (pollution) monitoring, insect monitoring, pest and disease control, which is based on virtualization and cloud computing technology, centrally managed device having flexible and configurable parameter with less maintenance and operational cost. To the best of our knowledge, vWSN is the first approach to agriculture based on extensional WSN information. WSN contains both control plane (signaling) and data plane (forwarding) coupled together.WSN is a specific purpose computer containing hardware whose main components are memory, CPU, IO, registers, Data and address bus, timer, sensor, computing logic and decision making logic. WSN is a physical device which needs to be placed in the field in certain topology (Ring, mesh, tree, star) which may need to be protected for damage and erosion due to various parameter including but not limited to physical theft, damage due to weather, animal and various other reason. In this paper our approach is to propose virtualized model to replace physical wireless sensor network (WSN) to virtual machine (VM) based vWSN which can be deployed on the cloud. Since security is one of the major concern for VM in cloud computing therefore we also proposed the enhanced 3-tier security model for vWSN.

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection.

This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

Energy-efficient sink relocation using whale optimisation technique in virtual grid-based wireless sensor networks

Energy-efficient sink relocation using whale optimisation technique in virtual grid-based wireless sensor networks

Energy-efficient sink relocation using whale optimisation technique in virtual grid-based wireless sensor networks

Wireless Sensor Network (WSN) is an efficient network for monitoring and recording the physical environment and transfers the monitored data into the central location using widely distributed sensor nodes. One of the main problems in WSN is the issue of developing an energy-efficient routing protocol that achieves less energy consumption and enhances the lifetime of the network. During the past decades, researchers have used the mobile sink to reduce the energy problem and hotspot problems. In this work, Virtual Grid Based Energy Efficient Sink Relocation (VGESR) is proposed to solve these issues. The grid clustering is achieved by employing K-means clustering. After clustering, the Leader Node (LN) selection is done by calculating the Acceptability Factor (AF). Acceptability factor is calculated based on the nodes residual energy, Available bandwidth and Received Signal Strength (RSS). Whale Optimisation Algorithm (WOA) technique is employed for the optimal sink relocation based on the fitness value of the nodes. The results obtained from the simulation prove that the proposed VGESR performs well in terms of life span and energy utilisation. The proposed VGESR simulation is performed using the OMNet++ tool.

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity.

Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

Retraction Note: Coastal biometric recognition based on virtual sensor network and accounting cost accounting of logistics companies

Retraction Note: Coastal biometric recognition based on virtual sensor network and accounting cost accounting of logistics companies

RETRACTED ARTICLE: Coastal biometric recognition based on virtual sensor network and accounting cost accounting of logistics companies

RETRACTED ARTICLE: Coastal biometric recognition based on virtual sensor network and accounting cost accounting of logistics companies

Integration of Machine Learning Techniques in Virtual Wireless Sensor Network for insect monitoring

Agriculture industries are comparatively slow in adopting emerging technologies than any other industries despite lot of exciting research. The use of Wireless sensor network (WSN) is very important role in agriculture for more productive and sustainable growth. The structure of WSN is tightly application dependent. Every WSN have sensors, processing unit, low frequency radio wave transmitter and power supply using battery. With increasing number of interconnected WSN devices, there is substantial increase in data generation. It contains both control messages as well as application dependent data, collected by the sensors. The collected data are frequently sent to the nearest centralized controller for further processing and decision making. For continuous functioning of WSN, uninterrupted power supply is needed. Many researches are carried out to overcome these challenges. In this manuscript we are proposing a simple and effective machine learning techniques combined with pause and play method to increase battery life of WSN. This could be achieved in three stages play-pause-play (PPP) model. First by gathering data for some time (play mode), Second by putting WSN to sleep (pause mode), in the backend, apply machine learning algorithm that helps to build model from training data to predict the future data. At the final stage, put WSN back to play mode. Compare the result with actual data and fine tune the model to reduce the error. Using this method WSN will get enough sleep time to increase overall life by simulating the normal behaviour of sensor node. The sleep time will be calculated dynamically.

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks.

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

Reconfigurable Array Beampattern Synthesis via Conceptual Sensor Network Modeling and Computation

Reconfigurable array radiates multiple patterns with only a single array via designing multiple excitation vectors with common magnitudes for the same element-index excitation elements, so as to guarantee phase-only control. To tackle this problem, this article proposes a new reconfigurable array beampattern synthesis method. First, we borrow the concept of sensor network consensus computation and model the synthesis problem as a sensor network computation problem via imitating a virtual (conceptual) multinode sensor network, where each “node” corresponds to an individual beampattern synthesis task. More specifically, these “nodes” with the same number as those of radiated patterns share a set of common excitation magnitudes, which results in a special magnitude-consensus computation problem in the imitated sensor network. Then, via locally computing the excitation in each “node” and exchanging current computation results with neighboring “nodes,” all “nodes” obtain the magnitude-consensus excitations after the “network” is stable. Numerical examples are provided to demonstrate the validity and faster convergence speed of the proposed method.