Wireless sensor networks (WSNs) are widely used for detecting objects, collecting, analyzing, and communicating information. Optimizing the estimation of sensor node positions is crucial for precise and reliable opera...
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Wireless sensor networks (WSNs) are widely used for detecting objects, collecting, analyzing, and communicating information. Optimizing the estimation of sensor node positions is crucial for precise and reliable operations. The standard Distance Vector Hop (DV-Hop) localization method, while simple and practical, lacks sufficient accuracy. This article introduces an Improved Distance Vector Hop (IDV-Hop) algorithm that enhances positioning accuracy using a Variable Velocity Strategy Human Conception Optimization (VVS-HCO). This method refines the velocity update technique to improve convergence and precision without additional hardware costs. The proposed algorithm allows adjustable hop sizes for anchor nodes. Simulation results demonstrate that the IDV-Hop algorithm significantly reduces localization error and variance, improving accuracy under various conditions, outperforming other advanced algorithms.
Wireless sensor network (WSN) is widely used in a variety of practical applications. WSN may be used to sense objects, gather information, analyze it, and then transmit it again. The significance of optimization techn...
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Wireless sensor network (WSN) is widely used in a variety of practical applications. WSN may be used to sense objects, gather information, analyze it, and then transmit it again. The significance of optimization techniques is crucial for the accurate and reliable estimation of the sensor nodes' location. The positioning accuracy of traditional distance vector hop (DV-Hop) localization algorithm is not entirely satisfactory instead of it is quite simple, stabilized, feasible, and requires less hardware. Thus to enhance the positioning accuracy without increasing the hardware cost of a sensor node, this article provides an improved distance vector hop (IDV-Hop) localization algorithm using human conception optimization. The proposed method adds a parameter to alter the anchor nodes' hop size. Furthermore, it is analyzed with traditional DV-Hop, IDV-Hop algorithm, DV-Hop based particle swarm optimization, and DV-Hop based class topper optimization. The simulation results support the conclusion that, the proposed algorithm performs better than the competing algorithms by minimizing the localization error, localization error variance, and the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range.
With the increasing demand for indoor services based on location information, the importance of achieving accurate indoor positioning has become increasingly prominent. However, wireless sensor networks (WSNs) are imp...
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With the increasing demand for indoor services based on location information, the importance of achieving accurate indoor positioning has become increasingly prominent. However, wireless sensor networks (WSNs) are impacted by non-line-of-sight (NLOS) transmissions when transmitting signals, resulting in decreased positioning accuracy. In contrast, The Inertial Navigation System (INS) operates independently without relying on external data, its positioning results are not affected by NLOS transmission, but it has the problem of error accumulation caused by integration. A joint positioning method combining INS and Ultra-wide band (UWB) is advanced to decrease the influence of NLOS error. When locating target nodes in UWB, an improved fuzzy clustering algorithm is employed to minimize the influence of NLOS transmission. Finally, the Multi-Filter Fusion method is used to fuse the positioning results of INS and UWB to obtain high-precision and robust position information. Simulation and experimental results show that the proposed algorithm owns better performance compared with existing algorithms.
Aiming at the localization deviation of DV-Hop algorithm in 3D wireless sensor networks, this paper proposes an improved DV-HOP localization algorithm based on approximate path distance optimization. The algorithm sel...
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ISBN:
(纸本)9798350350227;9798350350210
Aiming at the localization deviation of DV-Hop algorithm in 3D wireless sensor networks, this paper proposes an improved DV-HOP localization algorithm based on approximate path distance optimization. The algorithm selects the message with the least number of nodes and the greatest number of anchors from all the messages which broadcast from the source anchor node to the target anchor node. The approximate path distance of the message is used to replace the linear distance from the source anchor node to the target anchor node when calculating the average jump distance of the target anchor node. On this basis, the weighted average of the average jump distance of the three anchor nodes near the unknown node is used to calculate the average jump distance of the unknown node. The simulation results show that compared with the other three localization algorithms, the proposed localization algorithm can effectively reduce the localization deviation of unknown node under different node proportions.
A new localization algorithm based on large scale unmanned aerial vehicle swarm (UAVs) is proposed in the paper. The localization algorithm is based on a spring particle model (LASPM). It simulates the dynamic process...
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A new localization algorithm based on large scale unmanned aerial vehicle swarm (UAVs) is proposed in the paper. The localization algorithm is based on a spring particle model (LASPM). It simulates the dynamic process of physical spring particle system. The UAVs form a special mobile wireless sensor network. Each UAV works as a highly-dynamic mobile sensor node. Only a few mobile sensor nodes are equipped with GPS localization devices, which are anchor nodes, and the other nodes are blind nodes. The mobile sensor nodes are set as particles with masses and connected with neighbor nodes by virtual springs. The virtual springs will force the particles to move to the original positions. The blind nodes' position can be inferred with the LASPM algorithm. The computational and communication complexity doesn't increase with the network scale size. The proposed algorithm can not only reduce the computational complexity, but also maintain the localization accuracy. The simulation results show the algorithm is effective.
This paper firstly analyzes and points out the problems of the traditional DV-Hop algorithm, then proposes a node localization algorithm based on the hop distance optimization, which uses the actual path distance betw...
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ISBN:
(纸本)9798350313048
This paper firstly analyzes and points out the problems of the traditional DV-Hop algorithm, then proposes a node localization algorithm based on the hop distance optimization, which uses the actual path distance between two anchor nodes instead of the traditional linear distance to calculate the average hop distance of anchor nodes, and further calculates the average hop distance of unknown nodes with the help of multiple anchor nodes. The simulation results show that the proposed localization algorithm can effectively improve the localization accuracy of nodes, especially in the 3D outdoor environment without ranging.
A novel localization algorithm for a four-wheel alignment is present. As our algorithm is based on monocular vision, the problem that a traditional 3D four-wheel alignment needs to unify the global coordinate system a...
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A novel localization algorithm for a four-wheel alignment is present. As our algorithm is based on monocular vision, the problem that a traditional 3D four-wheel alignment needs to unify the global coordinate system and easy to cause error accumulation is not existing. In accordance with the principle of monocular reconstruction in computer vision, the representation of equivalent reference plane for each wheel in its corresponding camera coordinate system is calculated out. Based on these new definitions, related angle parameters of the vehicle wheel are finally determined. Simulations and analysis show the feasibility and robustness of our algorithm described in this paper. Experiment results show that the proposed algorithm can obtain precise results and the root mean square error is about 0.005 degrees. Compared with the traditional localization algorithm of a 3D four-wheel alignment, the measurement precision of our proposed algorithm is similar, but does not need global coordinate system calibration, so the operation is simple and efficient. Furthermore, as the wheel measurement is relatively independent, it is not easy to cause error accumulation.
For the complex non-line-of-sight propagation environment after the disaster,propose an improved Chan-Taylor position solution method based on multiple base stations, integrate the improved residual weighting algorith...
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ISBN:
(纸本)9781665469890
For the complex non-line-of-sight propagation environment after the disaster,propose an improved Chan-Taylor position solution method based on multiple base stations, integrate the improved residual weighting algorithm with the multivariate Taylor series expansion algorithm, and perform quadratic residual weighting. Moreover, in view of the actual situation of the lack of resources after the disaster, carry out the research of localization algorithm based on single base station, add additional observations such as base station motion state information, to realize secondary weighted positioning in non-line-of-sight environment. Simulation experiments show that, improved multiple base stations and single base station location algorithms can better suppress non-line-of-sight errors, and it is of great significance to carry out location search and rescue in time after the disaster.
Conventional Cuckoo search (CS) localization method can obtain good positioning results that are highly accurate and robust. However, its positioning performance is constrained by the limited distance information avai...
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Conventional Cuckoo search (CS) localization method can obtain good positioning results that are highly accurate and robust. However, its positioning performance is constrained by the limited distance information available between the unknown node and the anchor node. In order to further enhance the positioning accuracy of the CS method, an improved CS localization algorithm is proposed that can take advantage of all of the distance information available. In the positioning process, an objective function which contains the distance information among the unknown nodes is given. Firstly, we use this distance information between the anchor node and the unknown node to determine an initial position through the conventional CS method. Then, based on the initial position, all of the distance information available is used to compute a more precise position. The simulation results demonstrate that the proposed algorithm can enhance the positioning accuracy in comparison with the conventional CS algorithm.
Traditional particle swarm optimization (PSO) localization method can achieve good localization accuracy with the advantages of fewer parameters and simpler calculations. However, its localization performance is const...
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Traditional particle swarm optimization (PSO) localization method can achieve good localization accuracy with the advantages of fewer parameters and simpler calculations. However, its localization performance is constrained by the limited ranging information available between the anchor node and the unknown node. Thus, an improved PSO localization method is proposed to further improve the localization performance. Firstly, we use the ranging information between the unknown node and the anchor node to make an initial positioning through the traditional PSO method. Then, based on the initial positioning, all ranging information is employed to calculate a more precise location. Compared with the traditional PSO method, the proposed algorithm can reduce the impact of the ranging error and improve localization performance.
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