Intelligent Sensor Network Research Articles

Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks

In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.

Enhanced energy efficient sleep awake aware intelligent sensor network routing protocol for wireless sensor network

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

Study of rail damage diagnosis and localization method based on intelligent wireless acoustic sensor network

Many major derailments are closely related to rail damage, so it is especially important to conduct regular damage detection on in-service rails. In this paper, with the main objective of nondestructive detection and localization of rail crack damage, a wireless acoustic sensor network (WASN) based rail flaw detection and localization diagnosis system is designed and implemented: firstly, the WASN nodes rapidly sample the acoustic emission signal on the rail surface, then calculate and wirelessly transmit its characteristic parameters to the gateway, and then use the proposed information wasserstein generative adversarial network-gradient penalty (InfoWGAN-GP) algorithm for data augmentation of the collected AE features, followed by feature optimization of the augmented hybrid data set based on the proposed categorical boosting-borutashap (Catboost-BorutaShap) algorithm, furthermore, the proposed improved aquila optimizer-deep extreme learning machine (IAO-DELM) and binary dwarf mongoose simulated annealing optimization-newton iteration-triangulation algorithm (BDMSAO-NI-TA) strategies are then proposed to establish accurate identification and localization of rail crack damage derivation models. The experimental comparison results show that the IAO-DELM model constructed based on the preferred feature subset has the best effect on the performance enhancement of rail crack damage diagnosis, with the accuracy, precision, F1-score, weighted average and AUC of 99.86%, 99.58%, 98.75%, 0.9962 and 0.9923, respectively, which are higher than those of the optimization algorithm such as honey badger combined with DELM model. The evaluation indexes indicate that this method has excellent monitoring effectiveness.

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

AbstractThe growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

Retraction

Retraction: Cai Z, Li D, Deng L, Yao X. Smart city framework based on intelligent sensor network and visual surveillance. J Concurrency Computat Pract Exper. 2021; 33(12).The above article, published online on 06 Jun 2019 Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5301) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies were found making the motivation and conclusion of this article untrustworthy. These inconsistencies include unattributed image reproduction, irrelevant citations, and conceptual flaws. The authors were not able to provide comprehensive experimental data upon request. Accordingly, the conclusions of this manuscript must be considered insufficiently supported.

Proposed Mitigation Framework for the Internet of Insecure Things

Intrusion detection systems IDS are increasingly utilizing machine learning methods. IDSs are important tools for ensuring the security of network data and resources. The Internet of Things (IoT) is an expanding network of intelligent machines and sensors. However, they are vulnerable to attackers because of the ubiquitous and extensive IoT networks. Datasets from intrusion detection systems (IDS) have been analyzed deep learning methods such as Bidirectional long-short term memory (BiLSTM). This research presents an BiLSTM intrusion detection framework with Principal Component Analysis PCA (PCA-LSTM-IDS). The PCA-LSTM-IDS is comprised of two layers: extracting layer which using PCA, and the anomaly BiLSTM IDS layer for classification. Through a comparison study, the effectiveness of the proposed approach for the task of detecting attacks is evaluated using the NSL-KDD dataset, which solves many of the inherent problems with the KDD'99 dataset. Experimental results indicate that the suggested framework, with an average accuracy of 99%, outperforms alternative approaches specially when using PCA with explained variance of 90%.

Drift Detection of Intelligent Sensor Networks Deployment Based on Graph Stream

The optimal deployment of sensor nodes can effectively improve the monitoring performance of the sensor network. The existing optimization deployment strategies focus on how to achieve the optimal coverage or the maximal monitoring efficiency of the sensor network deployment, and lack research on the issue of when a deployed sensor network need to update its deployment. Considering that the parameters of the monitored targets by sensor network will vary over time, making the deployment a certain degree of time drift characteristic. In this paper, we propose a deployment drift detection algorithm based on graph stream for intelligent sensor networks (ISNs) to investigate the optimization deployment update time of sensor nodes for more efficient targets monitoring. We utilize graph stream to represent the topology of sensor network at different timestamps, and propose a projection fusion clustering algorithm based on Structural Clustering Algorithm for Networks (SCAN) to construct the tensor graph summarization within a sliding time window. According to the contrast between the node-level and the graph-level queries of the summary graph in adjacent windows, we refer Hoeffding boundary and graph contrastive loss function as the criterion to judge the optimal update time of the sensor network deployment. Experiments on Intel_Lab_Data and CIMIS datasets demonstrate that our approach can effectively detect the update moment of the optimal deployment of sensor network.

The Development of Smart Dairy Farm System and Its Application in Nutritional Grouping and Mastitis Prediction

In order to study the smart management of dairy farms, this study combined Internet of Things (IoT) technology and dairy farm daily management to form an intelligent dairy farm sensor network and set up a smart dairy farm system (SDFS), which could provide timely guidance for dairy production. To illustrate the concept and benefits of the SDFS, two application scenarios were sampled: (1) Nutritional grouping (NG): grouping cows according to the nutritional requirements by considering parities, days in lactation, dry matter intake (DMI), metabolic protein (MP), net energy of lactation (NEL), etc. By supplying feed corresponding to nutritional needs, milk production, methane and carbon dioxide emissions were compared with those of the original farm grouping (OG), which was grouped according to lactation stage. (2) Mastitis risk prediction: using the dairy herd improvement (DHI) data of the previous 4 lactation months of the dairy cows, logistic regression analysis was applied to predict dairy cows at risk of mastitis in successive months in order to make suitable measurements in advance. The results showed that compared with OG, NG significantly increased milk production and reduced methane and carbon dioxide emissions of dairy cows (p < 0.05). The predictive value of the mastitis risk assessment model was 0.773, with an accuracy of 89.91%, a specificity of 70.2%, and a sensitivity of 76.3%. By applying the intelligent dairy farm sensor network and establishing an SDFS, through intelligent analysis, full use of dairy farm data would be made to achieve higher milk production of dairy cows, lower greenhouse gas emissions, and predict in advance the occurrence of mastitis of dairy cows.

An optimized CNN-based intrusion detection system for reducing risks in smart farming

Smart farming is a well-known and superior method of managing a farm, becoming more prevalent in today's contemporary agricultural practices. Crops are monitored for their overall health and productivity using various information and agricultural technology. This involves monitoring the state of the field crops and looking at various other indicators. The ultimate objective of smart farming is to decrease the amount of money spent on agricultural inputs while maintaining the same level of quality in the final result. The Internet of Things has enabled smart farming via different data collecting and storage methods. For example, intelligent sensor networks gather field data for optimum plant irrigation in contemporary irrigation systems. As the dependency of smart farming on the IoT environment increases, they become more vulnerable to cyber-attacks. The reason is that IoT networks include so many nodes with few resources available, making securing them a challenging problem. Therefore, the need for an intrusion detection system (IDS) that can adapt to such difficulties is of the utmost importance. This paper proposes a new system that detects intrusions in IoT networks used in agriculture. The NSL KDD data set is used to evaluate the proposed method, which starts by performing several pre-processing steps on the original feature set. Important features are selected using recursive feature elimination, then converted into square color images. Now input images are suitable to be learned by different CNN architectures such as VGG16, Inception, and Xception models. Performance comparisons of CNN models with classical machine learning algorithms are evaluated using accuracy, F1 Score, Recall, and precision metrics.

Synthetic Behavior Sequence Generation Using Generative Adversarial Networks

Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN , that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.

Application of Intelligent Voice Technology and Sensor Network in Production and Operation Management VR Intelligent Teaching System

In this paper, first, we discuss the status quo of research and development of intelligent speech technology and analyze the problems existing in today’s education. The second is to explain the related concepts of intelligent speech technology, and, at the same time, to conduct in-depth research on the intelligent speech technology tools used in the experimental research and the related functions of the educational applications cited in this research, and briefly describe the correlation between information education and language learning basic knowledge. Then we summarize the structure, attributes, scope and existing solutions to solve the energy efficiency of the sensor network, focus on the classification and analysis of the existing routing protocols of the wireless sensor network, and summarize the characteristics of the protocol. In addition, this paper conducts extensive research on the relationship between LEACH protocol cluster division and energy consumption. This paper focuses on the shortcomings of the mainstream virtual reality (VR) system creation method, and proposes a method of interactive design, which significantly reduces the threshold for creating VR content and improves the efficiency of VR creation. Wisdom teaching technology not only mobilizes the enthusiasm of students in learning, but also helps to improve the teaching effect. Finally, it summarizes the specific work and importance of the research, and shows the application and development prospects of intelligent teaching technology in future education. Through the research of intelligent voice technology and sensor network, this paper applies it to the VR intelligent teaching system, and promotes the development of the VR intelligent teaching system in future education.

Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Research and optimization of the coal‐blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal‐blending system has some defects, such as fuzzy ash content of coal‐blending warehouse products and excessive error of the coal‐blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal‐blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal‐blending research, then establishes an optimized dynamic coal‐blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal‐blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal‐blending method, the average error is 3.33%, and the accuracy is improved by 4.82%.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks, then we decompose the sub-blocks by Schur decomposition. After that, the feature matrix is constructed according to the relationship between the image block energy and the whole image energy. At the same time, we encrypt watermarking with the logistic chaotic position scrambling. Finally, the zero-watermarking is obtained by XOR operation with the encrypted watermarking. Three indexes of peak signal-to-noise ratio, normalization coefficient (NC) and the bit error rate (BER) are used to evaluate the robustness of the algorithm. According to the experimental results, most of the NC values are around 0.9 under various attacks, while the BER values are very close to 0. These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods, which indicates it is more suitable for medical image privacy and security protection.

Design of Urban Sculpture Artwork Pattern on Account of Smart Sensor Network from the Artistic Perspective

&lt;p&gt;As a combination of the three elements of modern information technology, smart sensor network is a brand-new information collection and management technology. The smart sensor network is composed of a large number of smart sensor nodes with low energy consumption. In order to improve the performance of pattern design, this paper proposes an urban sculpture artwork pattern design framework based on intelligent sensor network. It uses intelligent sensor network technology to build design models and design patterns for urban sculptures. Based on the introduction of intelligent sensor network technology, various computer software and pattern texture calculation methods are used to process the urban sculpture pattern in the design process, and the construction of the urban sculpture pattern model is completed through the construction process of the urban sculpture pattern intelligent sensor network mode. Experimental results show that the average design time of urban sculpture patterns based on smart sensor network technology is 0.42 s, the average resolution is 7200 pixels, and the average dot pitch is 0.27mm. Compared with traditional 3D technology pattern design methods, the design performance of urban sculpture patterns based on intelligent sensor network technology is better, and it meets the design requirements of urban sculpture patterns.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Retracted: A Study of English Reading Modality Recognition and Practice Based on Intelligent Sensor Networks

Retracted: A Study of English Reading Modality Recognition and Practice Based on Intelligent Sensor Networks

Retracted] Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real‐time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network‐Geographic Information System (IWSN‐GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN‐GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN‐GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research.

Retracted: Design and Application of Collaborative Experiment Management Platform for Innovation and Entrepreneurship Education Based on an Intelligent Sensor Network

Retracted: Design and Application of Collaborative Experiment Management Platform for Innovation and Entrepreneurship Education Based on an Intelligent Sensor Network

Application of 5G Communication Technology Based on Intelligent Sensor Network in Coal Mining

Intelligent mining provides an indispensable, strong technical support for the construction of intelligent mines. Intelligent tunneling technology is an inevitable requirement for safe and efficient production of coal and a fundamental way to solve the imbalance of mining in order to improve the deficiency of data transmission in light of intelligent coal tunneling technology. Based on intelligent sensor and 5G technology, this paper puts forward a kind of intelligent tunneling support transportation integrated technology. According to the geological conditions of the surrounding rock in different coal seams, the anchor road header and 5G continuous intelligent tunneling mode are adopted. At the same time, the data communication network coverage suitable for underground unmanned driving is expounded, and the performance indexes that need to be met for accessing WLAN network are given based on the actual engineering experience. The breakthrough of underground unmanned driving application that 5G new communication network will bring is analyzed. And the construction mode of 5G platform of underground coal mine sensor network is given.