Position Information Of Nodes Research Articles

A geometric solution to microphone array position calibration and its confidence interval analysis

Wireless acoustic sensor networks (WASNs) comprised of spatially distributed acoustic nodes have been increasingly popular in many areas such as speech enhancement, blind source separation, and audio surveillance, where node position information is generally required to implement relevant processing algorithms. In this article, we focus on the node position calibration only using direction-of-arrival (DOA) information with each node consisting of a microphone array. State-of-the-art techniques translate the position calibration into optimization of DOA-based cost functions, and have shown notable success. However, a compromise between position calibration accuracy and computational load still exists. Furthermore, none of the existing studies can offer a quantitative evaluation of calibration performance before the optimization procedure is finished. To address these problems, a geometric solution to array node position calibration is proposed using DOA measurements. For a given array node, DOAs of three sources are first utilized to construct cylinder, plane, and cone equations. Next, the two-dimensional projection of intersections among these equations is calculated using Sylvesterresultantmatrix. Finally, a closed-form solution of node position in three-dimensional space is established. Moreover, explicit mathematical derivation of the confidence interval analysis on the calibration results under given node angle estimation error condition is presented, indicating that the proposed method can provide predictable performance with corresponding probabilities. The Cramér-Rao bound (CRB) is further investigated. Simulation results reveal that as compared with existing methods our approach achieves comparable calibration accuracy with much less computational burden. Real-world experiments also verify its effectiveness.

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.

Broad-Learning-Based Localization for Underwater Sensor Networks With Stratification Compensation

Localization is an indispensable service for underwater sensor networks (USNs). Generally, the convex optimization method is adopted to solve the localization problem. However, the acoustic ray in water medium does not propagate along a straight line, which makes it difficult or impossible to transform the non-convex optimization problem into a convex optimization problem. This paper develops a broad learning (BL) based localization solution for USNs with isogradient sound speed profile. We first employ the ray tracing model to compensate the range bias caused by straight-line propagation. On the basis of collected range information from anchor nodes, the localization optimization problem is transformed into supervised, unsupervised and semisupervised learning frameworks. Correspondingly, three BL-based location estimators are developed to seek the position information of sensor nodes, where the incremental learning schemes are conducted for fast parameter tuning and remodeling. In addition, the Cramer-Rao Lower Bound (CRLB) of positioning error and the convergence to global optimality are both analysed. Finally, simulation and experiment results are presented to show the effectiveness of our approach. It is demonstrated that the proposed solution in this paper has the following nice features: 1) relax the dependence of convex relaxation over convex optimization-based location estimators; 2) reduce the training time and improve the localization efficiency over deep learning-based location estimators.

Application of multi-target positioning algorithm in basketball teaching system and injury prevention

In today's era, the rapid spread of information and the rise of major network platforms provide good conditions for online learning and at the same time provide a new development idea for basketball teaching. The establishment of modern multimedia teaching theory provides theoretical support for the research and development of this subject. Construct and apply the online learning system of basketball basic technology, provide high-quality education, and can accommodate a large number of basketball fans to study and exchange together, and improve the physical fitness of young people. This thesis combines the multi-target localization algorithm in wireless sensor networks, first introduces the related concepts and basic theories of UWSNs, and then summarizes the related theories of node localization technology. In the course of basketball teaching, the advantages and disadvantages of the wireless sensor network multi-target positioning algorithm and the research direction are introduced. Aiming at the problem of low positioning accuracy and high complexity of the basketball motion prediction positioning algorithm, a multi-target positioning algorithm improved by backtracking search is proposed. The MBSA algorithm improves the positioning accuracy of anchor points, and uses cooperation mechanisms and basketball mobility to locate nodes for unknown actions. An improved BSA algorithm, namely the KLBSA-LoT algorithm, is proposed. The KLBSA-LoT algorithm accurately locates the anchor point and uses a cooperative mechanism to predict the position information of unknown nodes in basketball. Applying this algorithm to basketball teaching can greatly improve the accuracy of its positioning training. Sports teaching provides new methods.

Security Analysis of Eemccp Protocol for Underwater Acoustic Sensor Networks

Underwater Acoustic Communication (UWAC) is a recent addition to the ad hoc networking arena, and it has a variety of applications using autonomous underwater vehicles (AUVs). However, UWAC faces several challenges such as the Doppler Effect, channel availability problems, and limited bandwidth. Efficient management of the network is crucial to overcome these challenges, and clustering is an effective approach to achieve this. In this paper, we propose a novel protocol called EEMCCP (Efficient Energy Management and Cluster-based Communication Protocol) that uses clusters to route packets efficiently with the lowest cost. The Chaotic Algae Algorithm (CAA) is used to efficiently achieve clustering without requiring node position information. One node from each cluster acts as the Cluster Head (CH) to ensure the proper combination of network components. The proposed protocol does not consider nodes based on their location or position. Instead, the Received Signal Strength Indicator (RSSI) value of the Hello packets is used to select some CHs as gateway nodes, which are responsible for supplying AUVs with data. When gateway CH nodes exhaust their energy, back-up nodes take over, making the protocol flexible. Communication links transmit the data from AUV nodes to terrestrial destinations. We compare the performance of EEMCCP with existing underwater communication protocols, and our analysis shows that EEMCCP achieves a higher throughput and a higher Packet Delivery Ratio (PDR) than the existing protocols. EEMCCP is a cost-effective solution for transmitting data underwater, and it opens up several avenues for research in the field of UWAC. EEMCCP achieves a higher throughput and a higher Packet Delivery Ratio (PDR) than existing underwater communication protocols such as AEERP and EEDBR, making it a cost-effective solution to transmit data underwater.

SP-GNN: Learning structure and position information from graphs

Graph neural network (GNN) is a powerful model for learning from graph data. However, existing GNNs may have limited expressive power, especially in terms of capturing adequate structural and positional information of input graphs. Structure properties and node position information are unique to graph-structured data, but few GNNs are capable of capturing them. This paper proposes Structure- and Position-aware Graph Neural Networks (SP-GNN), a new class of GNNs offering generic and expressive power of graph data. SP-GNN enhances the expressive power of GNN architectures by incorporating a near-isometric proximity-aware position encoder and a scalable structure encoder. Further, given a GNN learning task, SP-GNN can be used to analyze positional and structural awareness of GNN tasks using the corresponding embeddings computed by the encoders. The awareness scores can guide fusion strategies of the extracted positional and structural information with raw features for better performance of GNNs on downstream tasks. We conduct extensive experiments using SP-GNN on various graph datasets and observe significant improvement in classification over existing GNN models.

RPConvformer: A novel Transformer-based deep neural networks for traffic flow prediction

Traffic prediction problem is one of the essential tasks of intelligent transportation system (ITS), alleviating traffic congestion effectively and promoting the intelligent development of urban traffic. To accommodate long-range dependencies, Transformer-based methods have been used in traffic prediction tasks due to the parallelizable processing of sequences and explanation of attention matrices compared with recurrent neural units (RNNs). However, the Transformer-based model has two limitations, on the one hand, it ignores the local correlation in the traffic state in its parallel processing of the sequence, on the other hand, the absolute positional embedding is adopted to represent the positional relationship of time nodes is destroyed when it comes to calculate attention score. To address two embarrassing shortcomings, a novel framework called RPConvformer is proposed, where the improved parts are 1D causal convolutional sequence embedding and relative position encoding. In sequence embedding, we develop a sequence embedding layer composed of convolutional units, which consist of origin 1D convolutional and 1D causal convolutional. The size of the receptive field of the convolution can focus on the local region correlation of the sequence. In relative position encoding, we introduce a bias vector to automatically learn the relative position information of time nodes when linearly mapping the feature tensor. We respect the encoding and decoding framework of the Transformer, the encoder is responsible for extracting historical traffic state information, and the decoder autoregressively predicts the future traffic state. The multi-head attention mechanism is adopted by both encoder and decoder aims to focus on rich temporal feature patterns. Moreover, key mask technique is used after computing attention matrix to mask the traffic state at missing moments improving the resilience of the model. Extensive experiments on two real-world traffic flow datasets. The results show that RPConvformer achieves the best performance compared to state-of-the-art time series models. Ablation experiments show that considering the local correlation of time series has a higher gain on prediction performance. Random mask experiments show that the model is robust when the historical data is less than 10% missing. In addition, multi-head attention matrix provides further explanation for the dependence between time nodes. RPConvformer as an improved Transformer-based model can provide new ideas for molding temporal dimension in traffic prediction tasks. Our code has been open-sourced at (https://github.com/YanJieWen/RPConvformer).

Opportunity Based Energy Efficient Routing Algorithm for Underwater Wireless Sensor Network for Submarine Detection

The submarine detection is the most significant research area of Under Water Acoustic (UWA) environment with extensive application in commercial and navy domains. The environmental complexity and variable nature of the UWA makes Underwater Wireless Sensor Network (UWSN) to exhibit fluidity, sparse deployment, time unpredictability, frequency selectivity, limited accessible bandwidth and energy constraints pose problems in the underwater positioning technology. Thus, an adaptable, scalable, and highly efficient UWA is required for the submarine routing systems. The depth-based routing has received lots of interest as it is capable of delivering effective operation without requiring full-dimensional position information of nodes. However, it has issues of vacant regions and detouring forwards. To delineate the aforementioned problems, this paper proposes an Opportunity-based Distance Vector Routing (ODVR) technique. The distance vectors, which have lowest hop counts in the direction of sink for underwater sensor nodes are determined by ODVR through a query method. Depending on the distance vectors, a dynamic routing is created to manage the packet forwarding. In the opportunistic forwarding, the ODVR has a minimal signaling cost and minimum energy consumption with the potential of eliminating the long detours issues. The outcomes of simulations demonstrate that the ODVR outperforms the conventional routing algorithms.

GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks

The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological features of networks. In this work, we propose a novel gravity model based on position and neighborhood (GPN), in which the mass of focal and neighbor nodes is redefined by the extended outspreading capability and modified k-shell iteration index, respectively. This new model comprehensively considers the position, local and path information of nodes to identify influential nodes. To test the effectiveness of GPN, a number of simulation experiments on nine real networks have been conducted with the aid of the susceptible–infected–recovered (SIR) model. The results indicate that GPN has better performance than seven popular methods. Furthermore, the proposed method has near linear time cost and thus it is suitable for large-scale networks.

REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

Privacy-Preserving Localization for Underwater Sensor Networks via Deep Reinforcement Learning

Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works.

Fundamental Performance Limits of mm-Wave Cooperative Localization in Linear Topologies

In applications such as seismic acquisition, the position information of sensor nodes, that are deployed in a linear topology, is desired with sub-meter accuracy in the presence of a limited number of anchor nodes. This can be achieved with antenna arrays via mm-wave cooperative localization, whose performance limits are derived in this letter. The number of anchor nodes is seen to have a stronger impact than the number of antenna elements in the anchor nodes. Succinct closed-form expressions for the position error bound are also obtained for 1-hop and 2-hop cooperative localization, where sub-meter accuracy is perceived over several hundred nodes.

Review on Positioning Technology of Wireless Sensor Networks

With the large-scale application of wireless sensor network, the position information of sensor nodes is more and more important. The position information of the unknown nodes are mainly depended on the beacon node the in wireless sensor network. First, the concept and characteristics of wireless sensor networks of the positioning technologies are briefly described. Then, the calculation methods of existing node positioning technologies are introduced. Next, the wireless sensors are described in detail from two aspects: range-based and range-free. Finally, summarizes the possible defects of positioning technology and looks forward to the future development of the node positioning technology.

Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs.

Node position information is critical in wireless sensor networks (WSN). However, existing positioning algorithms commonly have the issue of low positioning accuracy due to noise interferences in communication. Hence, proposed in this paper is an iterative positioning algorithm based on distance correction to improve the positioning accuracy of target nodes in WSNs, with contributions including (1) a log-distance distribution model of received signal strength indication (RSSI) ranging which is built and from which is derived a noise impact factor based on the model, (2) the initial position coordinates of the target node obtained using a triangle centroid localization algorithm, via which the distance deviation coefficient under the influence of noise is calculated, and (3) the ratio of the distance measured by the log-distance distribution model to the median distance deviation coefficient which is taken as the new distance between the target node and the anchor node. Based on the new distance, the triangular centroid positioning algorithm is applied to calculate the coordinates of the target node, after which the iterative positioning model is constructed and the distance deviation coefficient updated repeatedly to update the positioning result until the criteria of iterations are reached. Experiment results show that the proposed iterative positioning algorithm is promising and effectively improves positioning accuracy.

RMLNet-A Reliable Wireless Network for a Multiarea TDOA-Based Localization System.

Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes.

Velocity adaptation based PSO for localization in wireless sensor networks

Wireless sensor networks are a network of sensors interconnected through a wireless medium. Wireless sensor networks are utilized for many array of applications where determining precise location of the sensors are treated to be the crucial task. The prime job of localization is to determine the exact location of sensors placed at particular area as it makes the reference of anchor nodes to determine the location of remaining nodes in the network. Position information of sensor node in an area is useful for routing techniques and some application specific tasks. The localization accuracy is affected due to the estimations in anchor node placements. Localization information is not always easy as it varies with respect to the environment in which the sensors are deployed. Ranging errors occur in hostile environments and accuracy effects as there are signal attenuations in sensors when deployed underwater, underground etc. Efficiency can be enhanced by reducing the error using localization algorithms. Particle swarm optimization is one approach to overcome the localization problem. Results are considered for localization algorithms like Particle swarm optimization, Social group optimization and Velocity adaptation based Particle swarm optimization. The goal of this work is to implement a velocity adaptation based particle swarm optimization for localization method to achieve minimum error. The results reveal that the proposed approach works better for obtaining improved location accuracy.

Gossip Based Congestion Aware Routing Algorithm for Vehicular Adhoc Networks

Gossiping is a simple method for emergency information dissemination and used in many application domains like Mobile Ad-hoc Networks (MANETs), Vehicular Ad-hoc Networks (VANETs). In the existing research work has been conducted a techniques on probabilistic dissemination methods because of their efficiency compared with simple flooding and their simple applications. Most work done on static gossiping, i.e., the gossiping probability cannot be adapted according to topology changes. Thus, topology properties known in advance. In proposed method by using position information of nodes (vehicles) to develop the neighborhood relationship. Based on the neighborhood relationship, the dissemination hierarchy is constructed and the protocol is able to adjust the dissemination probability in dynamic and distributed manner. The protocol is applied in the highway scenario, where the network properties vary from sparse networks with high mobile nodes to a dense network with very high node density and low node velocities.

Localization Technique Considering Position Uncertainty of Reference Nodes in Wireless Sensor Networks

This paper proposes a distributed localization algorithm in wireless sensor networks. The algorithm considers the position uncertainty of reference nodes, which have been ignored in the past research, in which imperfect position information of reference nodes intensifies the error accumulation phenomenon. The algorithm calculates position uncertainty by the dilution of precision and presents a balance point between range errors and position errors by applying the modified spring mass method. We verify the feasibility of the proposed algorithm by changing multiple parameters under various circumstances. The result proves that the overall performance of localization is enhanced by considering the position uncertainty.

Fuzzy logic vector–based forwarding routing protocol for underwater acoustic sensor networks

AbstractThe sensor nodes working in the underwater environment are generally powered by batteries, which are undesirable or infeasible to replace and recharge. Thus, limited energy is one of the major constraints in underwater acoustic sensor networks. It is of great significance to design an energy‐efficient routing algorithm to prolong network lifetime. In this paper, we propose a fuzzy logic vector–based forwarding routing protocol wherein we consider both the position information and energy information of sensor nodes in the calculation of desirableness. In addition, parameters such as valid distance to forwarder nodes, projection, and the battery level of nodes are also considered in fuzzy logic system. The fuzzy logic–based energy‐efficient routing algorithm can achieve multiparameter and fuzzy routing decision. The performance analyses show that the routing protocol we proposed has better performance in energy consumption and average end‐to‐end delay compared with the similar algorithms; meanwhile, reliability can be guaranteed.

Enabling accurate range free localization for mo-bile sensor networks

The Existing localization algorithm was Sequential Monte Carlo (SMC) method for mobile sensor networks. In this paper, we propose an energy efficient algorithm called novel localization, which can achieve high localization in a better way. In existing algorithm, high Localization is achieved by getting more number of beacon nodes as input which improves the computational cost and decreases the sampling efficiency to a higher rate. But in the Proposed algorithm, we achieve high Localization accuracy with less number of beacon nodes itself thus the computational cost will be less and the sampling efficiency will be high. Our algorithm uses the approximate calculated position information of sensor nodes to increase the localization accuracy. The existing algorithms doesn’t have high localization accuracy when nodes move very fast, so we have propose a new algorithm, novel localization which is executed based on spped of nodes. The novel algorithm improves the Localization accuracy also when the node move fast.