Research Note: The accuracy of self-assigned land seismic node positions

In emergency situations, cooperative positioning of vehicular nodes is essential for facilitating precise and stable information for achieving reliable data dissemination in Vehicular Ad hoc NETworks (VANETs). However, the existence of Non-Line-Of-Sight (NLOS) nodes degrades the accuracy in estimating ranging measurements introduced by the blockages from tall vehicles and buildings. In this paper, Raccoon Optimization Algorithm-based Accurate Positioning Scheme (ROA-APS) is proposed for improving the accuracy in the estimation of ranging measurements in order to determine the exact position of NLOS nodes. It is proposed for ensuring maximized reliability and reduced latency in the event of warning message exchange. It inherits the food rummaging style of real raccoons for speeding and strengthening the local and global search process involved in the estimation of NLOS node positions. It utilizes maximum probability of acquiring higher adaptability through active learning to attain better localization of NLOS nodes. It inherits the distance information for calculating the position accuracy associated with vehicle trajectory, distance information error and the number of vehicles. It also uses the method of weighted average to enforce more confidence to the distance information provided by neighboring nodes. The simulation experiments of the proposed ROA-APS using EstiNet simulators are conducted to determine its significance with respect to positioning accuracy, emergency message dissemination rate, positioning error, neighbor vehicles awareness rate and positioning time. The results confirm an increased mean emergency message dissemination rate, positioning accuracy and neighbor vehicles awareness rate by 16.21%, 14.38% and 15.16% when compared to the benchmarked schemes.

The objective of this research is to develop the multipoint displacement measurement system for infrastructures. This paper describes the developed prototype of this system and shows the results of the field experiments. This system consists of a GPS server and sensor nodes. A low cost Ll GPS receiver and a wireless LAN device are equipped on the sensor nodes. A GPS server is able to gather the GPS data received at sensor nodes and to estimate their accurate positions by analyzing the Ll carrier phases. Field experiments were conducted with three sensor nodes. The results show that the positions of the sensor nodes are monitored by the accuracy of a few centimeters. The integer ambiguities, that are needed to estimate the accurate position, are correctly determined in 10 minutes in the experiment.

In underwater sensor networks (UWSNs), it is important to know the position of each sensor node to identify the place of event in the target ocean field. Ling et al. proposed a localization method that estimates the positions of sensor nodes based on trilateration from distances to anchors with accurate positions and the sensor nodes whose positions have already been estimated [1]. This method lacks the accuracy of estimated positions, since the estimation error is accumulated while new anchors are generated one after another. Erol et al. proposed the localization method using special buoys that can dive and surface in water[2]. Each buoy gets the location information by GPS while it is on the water surface. It can also dive and send the information to the neighboring underwater sensor node by the acoustic beacon to measure the distance. This method achieves accurate position estimation, but takes high cost because many buoys need to be deployed.

Wireless location is one of the core technologies of Wireless Sensor Network. In many applications, the accuracy of the location is the precondition of the useful of data information the node collected. Under the premise of cost limits, improving the accuracy of wireless sensor node position has crucial significance. After analyzing reasons of the location weakness of Dv-Distance algorithm due to errors caused by different paths, we propose an estimation algorithm that is based on the distance of different paths between node and the anchor node, thereby balancing the error of different paths to locate the node. This is an improved algorithm which is based on circle focal point positioning.

Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.

DV-Hop is a typical node localization algorithm without ranging of wireless sensor network. Because of the shortcoming that this algorithm's accuracy for node positioning is not high, a sensor node localization algorithm based on improved wolves algorithm is proposed. The algorithm is based on the classic DV-Hop algorithm, converts the wireless sensor node localization problem into a multi-constrained optimization problem, and uses improved wolves algorithm to solve this problem, then obtains the optimal solution of unknown node coordinates. Matlab simulation show that, this algorithm can significantly improve the positioning accuracy of the node than other wireless sensor network node localization algorithm.

Aiming at the problem that the localization algorithm based on received signal strength indication is difficult to dynamically track the parameter change in the complex environment of coal mines. In order to improve the accuracy of the mine personnel positioning, this paper proposes a method to use the improved gravitational search algorithm to position the underground personnel in the weighted centroid positioning. Utilizing the long distance path loss model got the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm performed the unknown node positioning. Finally, the improved GSA-PSO optimized the preliminary location results and parameters. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.

Ultra-wideband (UWB)-based indoor localization systems are characterized by high accuracy, strong anti-interference capabilities, low power consumption, and excellent scalability, making them a prominent technology in indoor positioning. Determining accurate anchor node positions is a prerequisite for achieving high-precision tag localization. However, this process often requires significant manual effort and time and may not be feasible for certain scenarios involving manual measurements. To address this challenge, this paper proposes an accurate and convenient anchor self-localization method. The proposed method requires only TOF and PDOA data from UWB measurements without relying on external sensor data, and can realize single-anchor localization without the need to introduce multiple anchors to construct a coordinate system. Subsequently, the iterative reweighted least squares (IRLS) algorithm is employed to centrally process measurement data from each anchor, yielding optimized position estimates. In simulation experiments, we analyzed the impact of ranging noise and AOA estimation errors on localization accuracy. Furthermore, we conducted validation experiments, and the results demonstrated that the proposed method effectively achieves accurate self-localization of UWB anchor nodes.

Geometric routing, data collection, node deployment and node tracking in wireless ad-hoc networks often require a form of position-awareness by the nodes. There exist several location sensing algorithms that use network topology (connectivity) to infer spatial information. Most of these rely on either centralised computation method, several well known anchor nodes/points or expensive message exchange between the nodes. We propose a particle positioning system (PPS) that uses node connectivity information collected by routing protocol to map node positions and hence avoid some of the aforementioned restrictions. PPS mainly uses a combination of particle and spring physics to simulate a system that calculates increasingly accurate relative positions with increased topology knowledge. If combined with angle of arrival information (AoA) or anchor nodes, PPS can map its relative coordinates to the real world positions. In this paper we show that PPS generates positions accurate to between 6-33% of the node's transmission range, while competitive location estimate techniques manage a 20% accuracy only in best-case scenarios.

摘要: 研究了多种网络拓扑结构及稀疏网络下节点定位的鲁棒性问题. 联合考虑 1 跳邻居数目、邻居节点自身定位精度与测距误差, 引入节点相对定位误差和相对可信度概念, 提出了一种分布式基于加权多尺度分析技术的鲁棒节点定位算法. 该算法根据节点2跳局部网络连通度信息及邻居节点相对定位误差大小, 自适应选择综合性能好的邻居节点参与迭代优化, 并采用与节点相对可信度成正比的加权机制, 增加高可信度节点在定位计算中的贡献度. 实验数据显示, 该定位算法能够有效地抑制较大定位误差在网络内的扩散, 同基于高斯核加权的 dwMDS(G) 算法相比, 不仅迭代次数减半, 而且在网络连通度较低或拓扑不规则时, 可提高 5% 左右的定位精度. 关键词: 无线传感器网络 / 定位 / 分布式加权多尺度分析 / 自适应邻居选择 / 相对可信度

Accurate positions of some sensor nodes need to be calculated within wireless sensor network (WSN) in many applications, such as environmental monitoring system. This paper aims at the location method of sensor nodes in small scale WSN application scenarios; several kinds of traditional positioning methods are discussed. The main contribution of this work is to propose an algorithm based on the energy attenuation model with transmitting distance, and then design a communication frame for rapid positioning on the basis of ZigBee protocol stack correspondently. The algorithm is easily applied in small scale area networks. Its quality is verified in the actual environment, the result shows that the positioning accuracy is within 2 meters; the relative error is less than 3% in small scale area of open environment.

In this paper, a novel scheme to track moving node in indoor wireless sensor network is proposed. Firstly, the observed position is estimated by a moving node position algorithm based on Received Signal Strength Indication (RSSI). Secondly, a Kalman filter is used to estimate position in order to improve position accuracy. Simulation results show the effectiveness of presented algorithm in indoor environments.

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.