Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks

With the high-speed development of wireless radio technology, numerous sensor nodes are integrated into wireless sensor networks, which has promoted plentiful location-based applications that are successfully applied in various fields, such as monitoring natural disasters and post-disaster rescue. Location information is an integral part of wireless sensor networks, without location information, all received data will lose meaning. However, the current localization scheme is based on equipped GPS on every node, which is not cost-efficient and not suitable for large-scale wireless sensor networks and outdoor environments. To address this problem, research scholars have proposed a rangefree localization scheme which only depends on network connectivity. Nevertheless, as the representative range-free localization scheme, Distance Vector-Hop (DV-Hop) localization algorithm demonstrates extremely poor localization accuracy under anisotropic wireless sensor networks. The previous works assumed that the network environment is evenly and uniformly distributed, ignored anisotropic factors in a real setting. Besides, most research academics improved the localization accuracy to a certain degree, but at expense of high communication overhead and computational complexity, which cannot meet the requirements of high-precision applications for anisotropic wireless sensor networks. Hence, finding a fast, accurate, and strong solution to solve the range-free localization problem is still a big challenge. Accordingly, this study aspires to bridge the research gap by exploring a new DV-Hop algorithm to build a fast, costefficient, strong range-free localization scheme. This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. To address the poor localization accuracy problem in irregular C-shaped network topology, it adopts an efficient Grew Wolf Optimizer instead of the least-squares method. The dynamic communication range is introduced to refine hop between anchor nodes, and new parameters are recommended to optimize network protocol to balance energy cost in the initial step. Besides, the weighted coefficient and centroid algorithm is employed to reduce cumulative error by hop count and cut down computational complexity. The developed localization framework is separately validated and evaluated each optimized step under various evaluation criteria, in terms of accuracy, stability, and cost, etc. The results of EGWO-DV-Hop demonstrated superior localization accuracy under both topologies, the average localization error dropped up to 87.79% comparing with basic DV-Hop under C-shaped topology. The developed enhanced DWGWO-DVHop localization algorithm illustrated a favorable result with high accuracy and strong stability. The overall localization error is around 1.5m under C-shaped topology, while the traditional DV-Hop algorithm is large than 20m. Generally, the average localization error went down up to 93.35%, compared with DV-Hop. The localization accuracy and robustness of comparison indicated that the developed DWGWO-DV-Hop algorithm super outperforms the other classical range-free methods. It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks

Non-Litigation Settlement Mechanism of Labor Disputes in China---- Examine and Review

Labor disputes settlement in China follows a non-litigation mode which contains consultation, mediation and arbitration. This article gives explanations on basic theory of labor disputes settlement mechanism and analyzes problems existed in the current non-litigation settlement mechanism of labor disputes in order to help with the perfecting of the mechanism.Key words: Labor disputes settlement; Non-litigation settlement mechanism of labor disputes; Problem

A review of Convolutional Neural Networks in Remote Sensing Image

Effectively analysis of remote-sensing images is very important in many practical applications, such as urban planning, geospatial object detection, military monitoring, vegetation mapping and precision agriculture. Recently, convolutional neural network based deep learning algorithm has achieved a series of breakthrough research results in the fields of objective detection, image semantic segmentation and image classification, etc. Their powerful feature learning capabilities have attracted more attention and have important research value. In this article, firstly we have summarized the basic structure and several classical convolutional neural network architectures. Secondly, the recent research problems on convolutional neural network are discussed. Later, we summarized the latest research results in convolutional neural network based remote sensing fields. Finally, the conclusion has made on the basis of current issue on convolutional neural networks and the future development direction

Using convolution neural networks for improving customer requirements classification performance of autonomous vehicle

Customer requirements are vital information prior to the early stage of autonomous vehicle (AV) development processes. In the development process of AV many decisions have been made concerning customer requirements at the first stage. The development of AV that meets customer requirements will increase the global consumer and remain competitive. Safety and regulation are one of crucial aspect for customers that requires to be concerned and evaluated at the early stage of AV development. If safety and regulation related requirements did not well identified, AV developer could not develop the safest vehicles due to the huge compensation of accidents. To efficiently classify customer requirements, this study proposed an approach based on natural language processing method. For classification purpose, the customer requirements are divided into six categories that the concept are come from the quality management system (QMS) standard. These categories will be as input for the next process development in making the best decision. Most of conventional algorithms, such as, Naive Bayes, MAXENT, and support vector machine (SVM), only use limited human engineered features and their accuracy for customized corpus in sentences classification are proven low which is less than 50 percent. However, in literature, convolution neural networks (CNN) have been described efficiently to overcome the customized corpus of sentence classification problems. Therefore, this study implements CNN architecture in customized corpus classification operations. As the results, the accuracy of CNN classification has improved at least 6 percent compared to the conventional algorithms

catena-Poly[1-butyl-3-methyl­imidazolium [[dichlorido(methanol-κO)(propan-2-ol-κO)lanthanate(III)]-di-μ-chlorido]]

The title compound, (C8H15N2)[LaCl4(CH3OH)(C3H7OH)], consists of one 1-butyl-3-methyl­imidazolium (BMI+) cation and one hexa­hedral tetra­chlorido(methanol)(propan-2-ol)lanthanate anion. The LaIII ion is eight-coordinate, with the LaIII ion bridged by a pair of Cl atoms, so forming chains propagating along the a-axis direction. Each LaIII ion is further coordinated by two isolated Cl atoms, one methanol and one propan-2-ol mol­ecule. The coordinated methanol and propan-2-ol mol­ecules of the anion form O—H⋯Cl hydrogen bonds with the Cl atoms of inversion-related anions. The BMI+ cation froms C—H⋯Cl hydrogen bonds with the Cl atoms of the anion. The anions are located in the C faces of the triclinic unit cell, with an inversion center in the middle of the La2Cl2 ring of the polymeric chain

Optimized range-free localization scheme using autonomous groups particles swarm optimization for anisotropic wireless sensor networks

Location information is a required concern for localization-based service application in the field of wireless sensor networks (WSNs). Distance Vector-Hop (DV-Hop) algorithm as the most typical range-free localization scheme is more suitable for large-scaled WSNs. Its localization performance is good in even distributed networks. However, it demonstrated extremely poor accuracy under anisotropic networks, which is an urgent problem that need to be addressed. Accordingly, an optimized DV-Hop localization algorithm is put forward in this study with considering several anisotropic factors. Accumulated hop size error and collinearity are two main reasons that led to low accuracy and poor stability. Hence, hop size error of anchors is reduced by introducing distance gap based on anchors. Besides, weighted least square method is adopted to replace the least square method to against anisotropic factors caused by irregular radio patterns. Moreover, an Autonomous Groups Particles Swarm Optimization (AGPSO) is employed to further optimize the obtained coordinate in the first round. It developed a novel method to determine localization coverage. The localization coverage is also added to be one evaluation metric in our study, which makes up for the lack of this evaluation indicator in most of the studies. Simulation results display good localization accuracy and strong stability under anisotropic networks. In addition, it also concluded that metaheuristic optimization algorithm and weighted least square method are more suitable to conquer anisotropic factor. It briefly points out a new direction for the future research work in the localization area under anisotropic networks

Flotation separation of poly (ethylene terephthalate and vinyl chloride) mixtures based on clean corona modification: Optimization using response surface methodology

Postconsumer polyethylene terephthalate (PET) has potential applications in many areas of manufacturing, but contamination by hazardous polyvinyl chloride (PVC) in common waste streams can reduce its recyclable value. Separating collected PET-PVC mixtures before recycling remains very challenging because of the similar physicochemical properties of PET and PVC. Herein, we describe a novel flotation process with corona modification pretreatment to facilitate the separation of PET-PVC mixtures. Through water contact angle, surface free energy, X-ray photoelectron and FT-IR characterization, we found that polar hydroxyl groups can be more easily introduced on the PVC surface than on the PET surface induced by corona modification. This selective wetting can suppress the floatability of PVC, leading to the separation of PET as floating product. A reliable mechanism including two different hydrogen-abstraction pathways was established. Response surface methodology consisting of Plackett-Burman and Box-Behnken designs was adopted for optimization of the combined process, and control parameters were solved based on high-quality prediction models, with fitting from significant variables and interactions. For physical or chemical circulation strategies with PET purity prioritization, the validated purity of the product reached 96.05% at a 626 W corona power, 5.42 m/min passing speed, 24.78 mg/L frother concentration and 286 L/h air flow rate. For the energy recuperation strategy with PET recovery prioritization, the factual recovery reached 98.08% under a 601 W corona power, 6.04 m/min passing speed, 27.55 mg/L frother concentration and 184 L/h air flow rate. The current work provides technological insights into the cleaner disposal of waste plastics

Improving efficiency of customer requirements classification on autonomous vehicle by natural language processing

Safety is critical for autonomous vehicle, therefore quality management system method is crucial for the risks that may impact human beings. Quality management system help identify customer requirements and finally meet them. Customer requirements also include other aspects that customers or stakeholders are most concerned. Although many researches on customer perception had been done, they do not include all aspect of the requirements toward autonomous vehicle. Furthermore, they are most in text format or will be transfer to text format that convenient to store and read. In front of the large amount text data, classifying them become time and costs consuming. The customer requirements on autonomous vehicle are summarized and allocated in different categories. The natural language processing method is applied in this paper. This method shows its efficiency on dealing with customer requirements. The results provide valuable reference for autonomous vehicle developer and top manageme

A Survey on Deployment and Coverage Strategies in Three-Dimensional Wireless Sensor Networks

The deployment and coverage strategies are key issues in researches and the applications of WSNs, since it greatly influences the node energy, communication bandwidth and Quality of Service (QoS) for WSNs. The current literatures on sensor coverage control approaches mainly focused on the two-dimensional (2D) plane. However, many applications including underwater monitoring, indoor surveillance and others scenarios that are deployed on the three-dimensional (3D) space. This paper presents an extensive overview of various coverage and deployment problems and algorithms in three-dimensional wireless sensor networks. It focuses on different coverage strategies, vital characteristics, design schemes, advanced methods and practical constraints dealing with coverage and deployment in 3D WSNs

A hybrid range-free algorithm using dynamic communication range for wireless sensor networks

Location plays a backbone role in networks, since it will great influence basic wireless sensor networks (WSNs) architecture. Distance-Vector Hop (DV-Hop) is a representative range-free localization algorithm, which is widely utilized to locate node position in location-based application. However, with poor localization accuracy, it cannot satisfy precise location-based application requirement. Consequently, we proposed a hybrid range-free algorithm depends on dynamic communication range to address low localization accuracy problem, named as DCDV-Hop. Firstly, we applied statistical methods to analyze the relationship between location error and hop count under different communication ranges. Thereafter, we employed centroid algorithm to calculate target node coordinate based on hop threshold. Finally, a weighted least square is applied to locate remaining target nodes. We conducted considerable experiments, the results demonstrated that our proposed algorithm DCDV-Hop can effectively reduce accumulate localization error and improve localization accuracy of target nodes, with stable performance. Moreover, maximum localization accuracy reached up to 91.35% and localization error reduced more than 50%, compared with DV-Hop algorithm