Sensors In Network Research Articles

Identification of sensor location and link flow reconstruction using turn ratio and flow sensors in an arterial network

In this article, a quadratic programming problem is considered to identify all link flows in an arterial network when there are unmeasured link flows. A graphical method is provided to determine the minimum number of measurements and sensor locations required to obtain a fully observable model. It is shown that this method is also valid for the augmented graph with turn ratio measurements. If the minimum measurements required are met, a fully determined network can be obtained. If there is not enough measurement, a bound on the magnitude of the resulting inaccuracy in terms of vehicle kilometers traveled (VKT) can be calculated by the proposed linear programming method. The model is that of a queueing network; the parameters describe network geometry, saturation flow rates, turning ratios, timing plan and link flows. Three case studies are conducted to validate this approach. The first two cases are to calculate all missing flows by using a few numbers of measurements and minimum number of measurements required, respectively. Upper and lower bounds in terms of VKT are also calculated for these cases. Third case is to obtain a fully determined network with the minimum number of flow measurements when turn ratio sensors are included. Real measurements are collected from a network in Mugla including 55 links and 16 intersections. Vissim simulator is used to analyze the accuracy of the link flow calculations obtained from the proposed method. The results show that the proposed programming method can calculate the missing flows with a high accuracy and short computation time.

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

Inspired by the notion of swarm robotics, sensing, and minimalism, in this paper, we study and analyze how a collection of only 1D depth scans can be used as a part of the minimum feature for human body detection and its segmentation in a point cloud. In relation to the traditional approaches which require a complete point cloud model representation for skeleton model reconstruction, our proposed approach offers a lower computation and power consumption, especially in sensor and robotic networks. Our main objective is to investigate if the reduced number of training data through a collection of 1D scans of a subject is related to the rate of recognition and if it can be used to accurately detect the human body and its posture. The method takes advantage of the frequency components of the depth images (here, we refer to it as a 1D scan). To coordinate a collection of these 1D scans obtained through a sensor network, we also proposed a sensor scheduling framework. The framework is evaluated using two stationary depth sensors and a mobile depth sensor. The performance of our method was analyzed through movements and posture details of a subject having two relative orientations with respect to the sensors with two classes of postures, namely, walking and standing. The novelty of the paper can be summarized in 3 main points. Firstly, unlike deep learning methods, our approach would require a smaller dataset for training. Secondly, our case studies show that the method uses very limited training dataset and still can detect the unseen situation and reasonably estimate the orientation and detail of the posture. Finally, we propose an online scheduler to improve the energy efficiency of the network sensor and minimize the number of sensors required for surveillance monitoring by employing a mobile sensor to recover the occluded views of the stationary sensors. We showed that with the training data captured on 1 m from the camera, the algorithm can detect the detailed posture of the subject from 1, 2, 3, and 4 meters away from the sensor during the walking and standing with average accuracy of 93% and for different orientation with respect to the sensor by 71% accuracy.

Position estimation in indoor using networked GNSS sensors and a range-azimuth sensor

The global navigation satellite systems (GNSS) receivers suffer from determining an accurate position estimate in the indoor region due to non-line of sight (Non-LOS) conditions. This paper proposes a novel method of position estimation within the indoor region by using distributed GNSS sensors and a range-azimuth sensor setup. All the GNSS sensors are connected to a central node; the inaccurate position estimates evolved from the Kalman filter (KF) framework are transmitted to a central node as primary data. The deviation between the inaccurate position estimate and the GNSS sensor’s actual position is the positional deviation (PD) vector, which needs to be estimated. In the same indoor region, a range-azimuth sensor is also deployed. It estimates the GNSS sensor’s physical location in its local coordinates, and these estimates are being transmitted to a central node as secondary data. Further, by using the primary and secondary data, we formulated a PD vector compensation followed by a sequential fusion (SF) framework to derive the precise locations of both GNSS sensors and the range-azimuth sensor by recursively estimating the PD vector. Finally, the Cramer–Rao lower bound (CRLB) for the proposed framework is derived. The simulation results are quantified with the root mean square error (RMSE) and CRLB.

Intertwine Connection-Based Routing Path Selection for Data Transmission in Mobile Cellular Networks and Wireless Sensor Networks

In a network setting, a sensor node's round-trip delay time over hostile nodes compromises the node's ability to transmit data from the sender node to the destination node. Minimum distance path discovery causes the path failure, since aggressive nodes are available. Node connectivity is poor which should cause the packet loss; it does not control more energy consumption, since packet broadcasting is repeated for many times using that path. So, the proposed intertwine connection-based routing path selection (ICBRPS) technique allows only energy efficient routing path, path connectivity is important, and routing path is damaged because of the presence of aggressive nodes. It hacks the information from sensor and operates unpredictable manner. The objective of this presented ICBRPS scheme is to improve the routing path in efficient manner. If any damages occur during the transmission of data, then the alternate best node connectivity path is created by energetic route discovery method. The performance metrics of parameters are delay, network overhead, energy consumption, packet loss, packet delivery ratio, and connectivity ratio. It enhances the connectivity rate and reduces the energy consumption.

Optimized Hand Gesture Based Home Automation for Feebles

Abstract: Home automation innovation is utilized to plan and actualize inaccessible, vitality proficient and versatile keen homes with the essential highlights that guarantee inhabitant consolation and security. A savvy domestic robotization framework that mechanizes fans, lights, and other apparatuses for individuals with disabilities, the elderly, and out of commission patients utilizing hand signals. The framework comprises networks (sensors and gadget actuators and cameras that portray a controller that communicates with a hand-off that speaks to the client interface and an Arduino microcontroller). Smart homes are cost effective since they utilize video handling frameworks and sensors. Send and get commands utilizing the MQTT server. It employs a remote WIFI module named Esp8266 NodeMCU to supply smooth and hassle-free WIFI use.

SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.

Information transmission simulation of Internet of things communication nodes under collision free probability equation

Abstract Based on the background of the Internet of Things, this paper proposes a communication node information transmission protocol for wireless network sensors. This method adopts the strategy of alternating listening and sleeping. It can also reflect the overall characteristics of the monitoring range while meeting the real-time requirements. The protocol method proposed in this paper has the characteristics of low energy consumption and no synchronization. The algorithm reserves multi-hop timeslots at one time in the data forwarding direction at the frontier of data transmission. It avoids the delay caused by intermittent transmission of single-packet data on the forwarding path. The simulation results show that the energy efficiency and delay performance are much better than SMAC.

Highly efficient indoor/outdoor light harvesting luminescent solar concentrator employing aggregation-induced emissive fluorophore

The rapid development of luminescent solar concentrators (LSCs) is largely attributable to their advantageous features including colorful aesthetics, design flexibility, and their ability to harvest both outdoor (solar) and indoor (light emitting diodes (LD)) light. In this study, we fabricated and characterized a highly efficient LSC containing a newly synthesized pyrrolopyrazine-based aggregation-induced emissive fluorophore (PP1). PP1 comprises a central pyrrolopyrazine stator surrounded by three phenyl rotors proportionally end-capped with electron-donating dimethylamines and electron-accepting cyano moieties. Due to the high photoluminescence quantum yield (75%) of PP1 in polymethyl methacrylate matrix and a large Stokes shift, the fabricated LSCs showed the external photon efficiency (ηext) as high as 5.3% under 1 sun and 8.3% under LED illumination. Additionally, a theoretical model was employed to estimate the ηext of large-area (up to 100 cm length) PP1 LSCs. Photovoltaic measurements under 1 sun revealed that the champion device displayed a power conversion efficiency (ηPCE) of 1.68% with scattering background and 0.95% without scattering background, the highest recorded ηPCE among the aggregation-induced emission based LSCs. While the ηPCE of the same LSC under LED illumination was 1.05% and 0.67% with and without scattering background, respectively. Collectively, this work demonstrates a substantial step forward toward the realization of highly efficient aggregation-induced emissive fluorophores based LSCs, that can be utilized not only as power producing building windows but also as the aesthetically appealing “wireless power suppliers” for uninterrupted functioning of various indoor electronic devices such as Internet-of-Things (IoT) and network sensors.

Wireless Sensor Technology-Based Physical Education Teaching Evaluation.

A wireless sensor network (WSN) is a group of geographically scattered and specialized sensors to monitor and record variables related to environmental and storing the obtained data in a vital location. These networks have applications and can be utilized in different research domains including physical education where error prediction is assumed as one of the core issues. Thus, careful attention is required from the researcher to provide reliable and accurate prediction models. Thus, aiming the shortage of large prediction error in the physical education evaluation, which is based on the BP neural network and wireless sensor technology, a combination of AFP and questionnaire survey method is proposed in order to improve the accuracy and predictability of evaluation, according to the characteristics of different evaluation subjects. We select the evaluation index system as the input of wireless sensor technology and then use the principle of genetic algorithm to select the optimal individual and optimize the initial parameters of wireless sensor technology to establish the evaluation model of physical education quality. Through the training and testing of sample data, it is shown that the model greatly improves the accuracy of physical education quality evaluation and has a good application prospect in physical education evaluation.

Machine learning-enabled resolution-lossless tomography for composite structures with a restricted sensing capability

Construction of a precise ultrasound tomographic image is guaranteed only when the sensor network for signal acquisition is of adequate density. On the other hand, machine learning (ML), as represented by artificial neural network and convolutional neural network (CNN), has emerged as a prevalent data-driven technique to predictively model high-degree complexity and abstraction. A new tomographic imaging approach, facilitated by ML and based on algebraic reconstruction technique (ART), is developed to implement in-situ ultrasound tomography, and monitor the structural health of composites with a restricted sensing capability due to insufficient sensors of the sensor network. The blurry ART images, as the inputs to train a CNN with an encoder-decoder-type architecture, are segmented using convolution and max-pooling to extract defect-modulated image features. The max-unpooling boosts the resolution of ART images with transposed convolution. To validate, a carbon fibre-reinforced polymer laminate is prepared with an implanted piezoresistive sensor network, the sensing capability of which is purposedly restrained. Results demonstrate that the developed approach accurately images artificial anomaly and delamination in the laminate, with inadequate training data from the restricted sensor network for tomographic image construction, and in the meantime it minimizes the false alarm by eliminating image artifacts.

Energy Efficiency Improvement of Composite Energy Building Energy Supply System Based on Multiobjective Network Sensor Optimization

In order to solve the problem of improving the energy efficiency of the energy supply system, the author proposes a method for improving the energy efficiency of the energy supply system of composite energy buildings based on multiobjective grid optimization, the method solves the energy efficiency improvement of the grid-optimized composite energy building energy supply system, with the increasing material needs of people, the traditional fossil energy consumption is increasing, and the environmental pollution problem is also becoming more and more serious, and people will face serious energy crisis. Due to the high proportion of coal in China’s energy, most thermal power plants use coal as fuel to generate electricity. In the process of coal combustion power generation, a large amount of air pollutants such as CO2, SO2, and Nox are produced, which destroys the ecological environment. In order to alleviate the energy crisis and reduce the emission of polluting gases, the development of renewable energy sources such as wind energy and solar energy is extremely important. Electricity supply and heat supply account for the main part of China’s energy consumption, 40% of China’s energy consumption is used for electricity supply, and 25% is used for heat supply. In terms of electricity supply, 12% of electricity is used for residential use, about 74% is used for industrial production, and 14% is used for commercial development.

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm.

Finding the location of sensors in wireless sensor networks (WSNs) is a major test, particularly in a wide region. A salient clustering approach is laid out to achieve better performance in such a network using an evolutional algorithm. This paper developed a clustered network called neighborhood grid cluster which has a node assuming the part of a cluster center focused in every grid. Grid-based clustering is less difficult and possesses a lot of benefits compared to other clustering techniques. Besides, we proposed a localization algorithm that centers around assessing the target area by considering the least estimated distance embedded with the genetic algorithm. Performance standards incorporate the energy representation, connectivity stratagem, and distance measure as fitness functions that assess our localization problem to demonstrate its viability. Simulation results confirm that our approach further improves localization accuracy, energy utilization, node lifetime, and localization coverage.

K-Means clustering of optimized wireless network sensor using genetic algorithm

Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby.

A vibration energy harvesting system for Self-Powered applications in heavy railways

Heavy-duty trains with intelligent autonomous rail transit systems play an essential role in transporting goods and storing a large amount of energy to make it more sustainable. This paper presents a vibration energy harvesting system using a two-ball pair mechanism to collect track vibration for power sensors of a heavy railway network. The track vibration input module, motion conversion module, generator module, and energy storage module are essential subdivisions of the proposed system. The reciprocating linear vibrations of the track are transferred to the motion conversion module via the track vibration input module. The motion conversion module converts the vertical vibration into unidirectional rotation by employing a two-ball pair mechanism. The generator module utilizes the unidirectional rotary motion to generate electricity and is stored in an energy storage module. The performance of the energy harvesting system is evaluated based on simulation and experimental analyses under similar boundary conditions and found a good agreement between the results. The results indicate a maximum performance of 48.3% with an output power of 0.928 W, and this power is sufficient for self-propelled applications around heavy railways.

Smart sensors in vehicular communication networks: Principles, algorithms, systems and applications

Smart sensors in vehicular communication networks: Principles, algorithms, systems and applications

Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

SummaryHierarchical based routing is a kind of group‐based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large‐scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self‐organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid‐based k‐means clustering protocol (named GBK), which combines grid‐based routing with k‐means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k‐means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBK algorithm named GBK‐R is also proposed to extend network stability of the GBK algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBK and GBK‐R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBK clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.

Cultural Differences of Basketball between China and the United States and Its Concept in Basketball Teaching under the Background of Wireless Network Intelligence Technology

The increasingly extensive socialization trend has led to the comprehensive innovation of sports. The development of Chinese basketball should be based on the successful experience of the development of professional basketball in the United States. The development of China’s basketball has a solid cultural and mass foundation, but we should not be proud and arrogant. It is necessary to make full use of the critical and inherited attitude to learn and absorb the excellent and advanced knowledge of American basketball culture. At present, there are still some problems that need to be further explored. The purpose of this paper is to research and discuss the cultural differences of basketball between China and the United States and their concepts in basketball teaching based on the background of wireless network intelligence technology. This paper firstly analyzes and introduces the difference between the wireless network and wireless sensor network. The main design goal of traditional wireless networks is to improve service quality and efficiently use bandwidth and then consider energy saving, while the main design goal of sensor networks is to use energy efficiently, which is one of the most important differences between sensor networks and traditional networks, to analyze the differences between Chinese and American basketball cultures, to understand the impact of Chinese and American basketball cultural differences on basketball teaching concepts, and to compare and study the differences between Chinese and American basketball culture and their teaching philosophy in basketball. Using the method of this article, through the analysis of experimental data, we understand the impact of Chinese and American basketball cultural differences on basketball teaching concepts and find that Chinese and American basketball cultural differences play an important role in basketball teaching concepts, with a satisfaction rate of 66%. Through theory and experiments, based on the data, it is analyzed that the impact of the differences between Chinese and American basketball cultures on basketball teaching concepts reaches 50%. The research results show that the differences between Chinese and American basketball cultures play an important role in basketball teaching concepts and can be used to promote the development of basketball culture and skills.

BLOCKCHAIN BASED SECURE ANDENERGY EFFICIENT ROUTING PROTOCOLFORWSN

Routing in a Wireless Network Sensor (WSN) is a critical process as it is basically responsible for transmitting the data to Base Stations (BS). WSNs are used in a wide range of applications that require the data to be transmitted and received securely, such as in battlefield surveillance, flood detection, forest fire detection, traffic surveillance, etc. But the actual routing process is susceptible to attacks that can completely destroy the operation of the WSN. The intervention from malicious nodes is one of them. Malicious nodes can send erroneous information of particular lengths to other nodes in order to increase their chance of receiving the data that is being transmitted. The malicious nodes can also send corrupted data to other nodes and cause the operation of WSNs to fail. Hence, a trustworthy routing system is a must in orderto ensure that the WSN will operate smoothly and efficiently. An authentication scheme is introduced that is blockchain-based, so as to provide secure routing in the WSNs. As there are unauthenticated and malicious nodes that affect the routing process, the correct identification of the routing path becomes a challenging issue. Therefore, in our model, to prevent the involvement of these malicious nodes in the network, the registration of the nodes is done by a Certificate Authority Node. Each node that is involved in the routing gets authenticated by the BS and a mutual authentication is also performed. Furthermore, in the proposed routing protocol, a Cluster Head (CH)is used. The selection of a CH node is based on the residual energy of the node and the node which has the minimum distance from BS. This CH collects the data from the other nodes and sends it forwards to the BS. Every node can authenticate the process of transmitting or receiving data using a Proof of Authority (PoA) method in the blockchain network.

A Design for the Bandwidth Improvement for the Microstrip Patch Antenna for Wireless Network Sensor

The microstrip patch antenna is extensively used in the wireless network because the Microstrip patch Antenna can be easily manufactured. Bandwidth Improvement of the Microstrip patch antenna is shown in this paper. The Antenna is made at the 8.5GHz frequency. The material used for the substrate is FR4-epoxy. For this material, the dielectric loss tangent is 0.0018. This antenna observes the Bandwidth to 660MHz, gain 5.4028, Directivity 5.5068, return loss -37.0048, and VSWR 1.0629. In this design there is an Improvement of bandwidth to 660 MHz which is suitable for wireless network applications and the software which is being used for the simulation is the High-Frequency Structure Simulator which is also known as HFSS.

Data reconstruction applications for IoT air pollution sensor networks using graph signal processing

The analysis of sensor networks for air pollution monitoring is challenging. Recent studies have demonstrated the ability to reconstruct the network measurements with graphs derived from the acquired data, thus describing the complex relationships between the sensors that compose the network. In this work, we propose a graph-based data reconstruction framework that can be used to carry out different post-processing applications that arise in real-world low-cost sensor deployments for air pollution monitoring. This data reconstruction framework first describes the relationships between the different network sensors by means of a graph learned from the measured data, and then a signal reconstruction model is superimposed to reconstruct sensor data. This methodology allows reconstructing sensor data to carry out missing value imputation, signal reconstruction at points where there are no physical sensors (virtual sensing), or data fusion. The results, using real data taken with an air pollution monitoring network including reference stations and low-cost sensors, show how this framework performs well for these applications compared to estimates obtained from individual sensors and task-specific state-of-the-art methods that are not able to deal with this necessary range of applications. In short, the results show the potential of using graphs, whose topology is based on the measurements taken, to calibrate and reconstruct signals in heterogeneous networks of low-cost air pollution sensors for a wide variety of applications.