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In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78 m to 14.39 m, thereby improving localization accuracy by 56.46%. [ABSTRACT FROM AUTHOR]

Accurate node localization is essential in wireless sensor networks (WSNs) for effective data analysis and the successful operation of applications like environmental monitoring and disaster management. Range-free methods like the distance vector-hop (DV-hop) algorithm are often used due to hardware and cost constraints, but they face challenges in accuracy and stability. The NP-hard nature of the localization problem has led to the integration of metaheuristic algorithms in previous studies to enhance performance. This paper presents EMGODV-Hop, a novel approach for node localization in multi-hop networks that combines the DV-Hop algorithm with the mountain gazelle optimization (MGO) algorithm to enhance localization precision. The EMGODV-Hop method operates in two phases: First, it uses an improved variant of the DV-Hop algorithm to more accurately estimate distances between unknown and anchor nodes by incorporating a correction factor. Next, it employs an enhanced version of MGO algorithm, referred to as EMGO, to determine the positions of WSN nodes. The improved DV-Hop version enhances accuracy by incorporating a correction factor for better estimation of hop distances, while the EMGO algorithm addresses the limitations of the original MGO algorithm and improves its search capabilities. Extensive simulations assessed the effectiveness of the proposed method across various factors, including anchor node ratios, total node count, and communication ranges. The results demonstrate significant accuracy improvements with the proposed algorithm, showing enhancements of 48.69%, 26.22%, 19.33%, 28.21%, and 40.47% compared to DV-Hop, MGODV-Hop, PSODV-Hop, WSODV-Hop, and SSADV-Hop, respectively. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm’s localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland.

To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance. [ABSTRACT FROM AUTHOR]

Node location is an important problem in wireless sensor network.Although the location algorithm based on distance measurement has small positioning error, it has many limitations when applied to outdoor environments.Therefore, based on the original distance vector-hop (DV-Hop) algorithm, received signal strength indication (RSSI) technology and the minimum mean square error (MMSE) criterion to modify the algorithm’s ranging process were introduced, and the improved gray wolf optimizer was used to optimize the process of determining the coordinates of unknown nodes.Simulation results show that, compared with the original DV-Hop algorithm and IPDV-Hop algorithm, the average location error rate of the IGDV-Hop algorithm under the initial parameters was reduced by 28% and 17% respectively, and the location effect was significantly improved.

Abstract Wireless sensor network (WSN) possesses very broad application prospect in many fields, where the node location technology is one of the key technologies of WSN. Distance vector hop (DV-Hop) localization algorithm is a widely used algorithm in this technology, and it uses routing exchange protocol to make unknown nodes obtain beacon node information which will be used for coordinate calculation, therefore there exists certain error for the algorithm itself. Aiming at the disadvantage of large error existing in the traditional wireless sensor network location algorithm based on DV-Hop, an improved DV-Hop algorithm based on hop thinning and distance correction is proposed. The minimum hop is corrected by introducing received signal strength indication (RSSI) ranging technology, and the average hop distance is corrected by weighted average value of hop distance error and estimated distance error. Subsequently, the overall improvement on the location performance of the Hop-DV location algorithm is realized, and the location error is reduced. Under the Matlab simulation environment, the simulation experiment on the improved algorithm is carried out. The experimental results show that the improved algorithm reduces the location error and has higher location accuracy.

Abstract Location technology is the key support technology of wireless sensor network (WSN). The hop number and hop distance information obtained by traditional distance vector hop (DV-Hop) location algorithm can only be acquired by solving the nonlinear equations, and the solution of the equation determines the accuracy of node location. Although the least squares method has better estimation performance, the solution results are sensitive to the average hop distance, which will lead to the large error in the solution of the equation. In order to solve the problem of location error caused by initial value sensitivity of least squares method in the coordinate calculation stage of unknown nodes and beacon nodes, a range-free location algorithm based on particle swarm optimization (PSO) is proposed in this paper. The proposed approach solves the problem of location error caused by initial value sensitivity of least squares method, obtains relatively accurate solution, and improves the accuracy of location algorithm. The experimental results show that the PSO algorithm has faster convergence speed and higher location accuracy than the non-optimization algorithm.

Location information is prerequisite for wireless sensor networks (WSNs) monitoring and control applications, since there is no meaning without position information for collected data. Distance vector hop (DV-Hop) localization algorithm as the typical range-free algorithm that has been widely applied in various applications. Nowadays, the research on range-free localization for WSNs is mostly based on two-dimensional (2D) space. Hence, there are few surveys concentrated on range-free localization in three-dimensional (3D) WSNs. This motivated us to present an extensive overview of enhanced DVHop localization algorithms in 3D WSNs. This paper focused on critical challenge between 2D and 3D in localization model, representative range-free 3D localization technique surveys. Moreover, a comprehensive taxonomy of most essential enhanced methods applied in 3D DV-Hop is illustrated. A considerable comparison in term of localization error, computational complexity and node type is given. Future research directions dealing with localization under 3D DV-Hop is also discussed. [ABSTRACT FROM AUTHOR]

In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-Pigeon-Inspired Optimization Algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary Algorithm for the Pigeon-Inspired Optimization Algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the Pigeon-Inspired Optimization algorithm and makes the Pigeon-Inspired Optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-Pigeon-Inspired Optimization algorithm into the Distance Vector–Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location. [ABSTRACT FROM AUTHOR]

This paper proposes a planar projection DV-hop location algorithm (PTCDV-hop) based on the LED semi-angle at half power (SAHP, which accounts for LED SAHP characteristics in visible light communication (VLC)) and uses the DV-hop algorithm for range-free localization. Distances between source nodes and nodes positioned in three-dimensional indoor space are projected onto a two-dimensional plane to reduce complexity. Circles are structured by assigning source nodes (projected onto the horizontal plane of the assigned nodes) to be centers and the projection distances as radii. The proposed PTCDV-hop algorithm then determines the position of node location coordinates using the trilateral-weighted- centroid algorithm. Simulation results show localization errors of the proposed algorithm are on the order of magnitude of a millimeter when three sources are used. The PTCDV-hop algorithm has higher positioning accuracy and stronger dominance than the traditional DV-hop algorithm. [ABSTRACT FROM AUTHOR]

In the DV-Hop algorithm, the average distance per hop is one of the factors that affect the accuracy of the positioning. In this paper, an improved DV-Hop localization algorithm based on bat algorithm (BAD-Hop) is proposed to solve the error which is brought by the average distance per hop. In BAD-Hop algorithm, bat algorithm which is a kind of intelligent optimization algorithm with good performance is introduced into DV-Hop localization algorithm to calculate average distance per hop of anchor nodes. Firstly, the average distance per hop of anchor node is calculated by using bat algorithm, which makes it closer to the actual value. Then the average distance per hop of the unknown node is weighted by using the average distance per hop of anchor nodes which hop-count is less than or equal to 3 to reduce errors caused by average distance per hop. Simulation results show that the improved algorithm can effectively reduce the positioning error without additional hardware.

Abstract In the practical application of node positioning in the Internet of Things (IOT), the distribution of beacon nodes is generally non-uniform, so there is a certain error in positioning accuracy. In order to make the positioning more accurate, The MMSDV-Hop localization algorithm which improved DV-Hop localization algorithm is proposed in this paper. At first, this algorithm needs to determine the valid beacon nodes in a local range by selecting threshold value, and after using minimization criterion of mean-square error and correcting to obtain the final average hop distance. The dynamic weight is added with the hop number and the number of valid nodes as weights to determine the distance between the unknown nodes and the beacon nodes. A Weighted centroid localization algorithm and a weighted least square method are used to obtain an estimated position separately and the final position is determined by the arithmetic mean value of the two estimated positions. The Matlab simulation experiment shows that compared with the DV-Hop localization algorithm, the improved MMSDV-Hop localization algorithm has increased significantly in the positioning accuracy. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a distributed range-free node localization algorithm for three-dimensional (3D) wireless sensor networks (WSNs), named as 3D-GAIDV Hop (3D genetic algorithm based Improved Distance Vector Hop). In the proposed algorithm, the average hop size of anchor nodes is modified by updating a correction factor and the modified hop size is further optimized by line search algorithm. The concept of coplanarity is introduced to reduce location errors caused by the anchor nodes which are coplanar. The localization accuracy is further improved by applying genetic algorithm (GA). To improve the positioning coverage of the network, those target nodes which have been localized successfully in the first round of the localization process, are upgraded to assistant anchor node. All the processes and calculations are carried out at target node level, which makes the proposed algorithm energy efficient. Simulation results show that our proposed algorithm outperforms the similar kind of existing algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of node localization in wireless sensor networks, a location algorithm for optimizing distance vector hopping (DV-hop) by constructing a quantum neural network model based on particle swarm optimization (PSO) is proposed. According to the average distance obtained by the traditional DV-HOP and the distance from the measured nodes, the quantum neural network model is constructed, and the average distance is trained by the particle swarm optimization algorithm which would shorten the training time of the traditional artificial neural network and accelerate the convergence speed. By using the proposed model, the optimal mean value is obtained, and the optimization of the DV-HOP algorithm is realized. The simulation results show that compared with the traditional DV-HOP algorithm, the proposed algorithm can reduce the positioning error by about 20%, and the positioning accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

This paper aims to improve the positioning accuracy of the traditional DV-hop algorithm in networks of uneven node distribution. For this purpose, an improved algorithm was proposed that reduces the errors in terms of the hop count, the mean single-hop distance of anchor nodes and the mean single-hop distance of unknown nodes. Specifically, the hop count was modified based on the RSSI value of the node and the critical RSSI ratio; the mean single-hop distance of anchor nodes was corrected based on the ratio of the singlehop RSSI path length between two nodes to the mean single-hop RSSI path length (i.e. the correction factor); the mean single-hop distance of unknown nodes was divided into two sections to achieve better estimation of the distance between anchor nodes and unknown nodes. The simulation results indicate that the improved algorithm boasts better positioning accuracy and stability than the contrastive algorithms, with the addition of a few computing and communication overhead. The research findings shed new light on the accurate positioning of nodes in wireless sensor networks (WSNs). [ABSTRACT FROM AUTHOR]

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.

Wireless Sensor Networks (WSN) are getting more and more attention for various applications. In addition, localization plays an important role in WSN. This article focus on the large errors of DV-Hop (Distance Vector- Hop) localization algorithm in net topology of randomly distributed notes. A CLDV-Hop (Correct Levenberg Marquardt DV-Hop) algorithm in DV-Hop based on modifying average hopping distances is proposed. Firstly, hop count threshold is to optimize the anchor node during a data exchange. The weighted average hop distance of unknown nodes nearest the three anchor nodes are selected as its average hop distance according to the minimum mean square criteria. Finally, L-M (Levenberg Marquardt) algorithm is used to optimize the coordinate of unknown node estimated by least squares. The simulation results show that, under the conditions of existing overhead and for a given simulation environment, CLDV-Hop algorithm has higher positioning accuracy than existing improved algorithms. The accuracy is improved 33%-41% compared with DV-Hop algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN), as a new method of information collection and processing, has a wide range of applications. Since the acquired data must be bound with the location information of sensor nodes, the sensor localization is one of the supporting technologies of wireless sensor networks. However, the common localization algorithms, such as APIT algorithm and DV-Hop algorithm, have the following problems: 1) the low localization accuracy of beacon nodes; 2) the low coverage rate in sparse environment. In this paper, we combine APIT algorithm and DV-Hop algorithm to design an enhanced hybrid 3D localization algorithm, which can improve the localization accuracy with a high coverage rate. The proposed hybrid algorithm can reduce the triangles in the triangle interior point test (PIT) and select good triangles to improve the localization accuracy of the beacon nodes in dense environments in dense environments. In addition, the algorithm can combine the advantages of APIT algorithm and DV-Hop algorithm localization algorithm to calculate the unknown node coordinates, and the location coverage of the beacon nodes in sparse environment can also be improved. Simulation results show that the proposed hybrid algorithm can effectively improve the localization accuracy of beacon nodes in the dense environment and the location coverage of beacon nodes in sparse environment. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is an important component of a cyber–physical system. Locating node information is a crucial problem for WSN. Currently, distance vector-hop method (DV-Hop), one of popular range-free algorithms, is widely deployed to estimate the location. However, the estimation precision is challenging. In this paper, a new evolutionary algorithm named oriented cuckoo search algorithm (OCS) is designed. In OCS, the global search capability is dominated by the combination of two different random distributions. To provide a deep investigation, ten different random distributions are employed and compared with CEC2013 test suits. Numerical results show the hybrid distribution combined with Lévy distribution and Cauchy distribution achieves the best performance. Furthermore, OCS with this hybrid distribution is also incorporated into the methodology of DV-Hop algorithm to improve the precision performance. Simulation results demonstrate that our modification achieves better precision performance when compared with three other DV-Hop algorithms. [ABSTRACT FROM AUTHOR]

Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors. [ABSTRACT FROM AUTHOR]

According to that node localization accuracy is not high in the DV Hop localization algorithm, shuffled frog leaping algorithm with many advantages such as the convergence speed is fast, easy to realize and excellent performance of global optimization and so on is introduced into the design of DV-Hop algorithm. A new DV-Hop algorithm based on shuffled frog leaping algorithm (Shuffled Frog Leaping DV-Hop Algorithm, SFLADV-Hop) is proposed in this paper. Based on traditional DV-Hop algorithm, the new algorithm used distance of nodes and position information of anchor nodes to establish objective optimization function and realize the estimation of unknown node coordinate in the final stage of DV-Hop algorithm. The simulation results showed that compared with the traditional DV-Hop algorithm, based on not increasing the sensor node hardware overhead, the improved algorithm can effectively reduce the positioning error. [ABSTRACT FROM AUTHOR]

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm (BA) was improved from two aspects: firstly, the random vector ! was adjusted adaptively according to bats fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DVHop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm (BADV-Hop). Positioning precision of the proposed algorithm increases about 16.5% and 5. 18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation. [ABSTRACT FROM AUTHOR]

Based on the traditional DV-Hop algorithm and in order to overcome its existing drawbacks, an improved DV-Hop algorithm is brought forward in this paper by using the PSO algorithm. Through simulation experiments, it is found that both the average localization error and the localization coverage rate of PSO are better than that of DV-Hop Moreover, with the increase of the number of nodes, the average localization error of PSO shows a downward trend and is less than that of DV-Hop. [ABSTRACT FROM AUTHOR]

Abstract: Wireless Sensor Networks, which can real-time monitor and acquire information in the distribution area of network, have a widely application in the next generation network. Localization of nodes is a key technology for application of wireless sensor networks, and it has attracted significant interest in recent years. According to the localization principle of the DV-Hop algorithm, we propose an improved localization algorithm that has three improvements including the estimation of average single hop distance, the calculation of distance between unknown nodes and anchor nodes and the estimation of node positions. These improvements can be used to replace the corresponding steps in the DV-Hop algorithm, and localization performances are analyzed in theory and in simulations. The results show that the proposed improvements can greatly enhance the localization accuracy of the unknown nodes. In addition, the proposed schemes do not change the localization process of the DV-Hop algorithm, and hence they need no further communication resource and additional hardware requirement. [Copyright &y& Elsevier]

The article focuses on the study which proposes an improved DV-Hop localization algorithm called NDV-Hop_Bon in wireless sensor network (WSN). The proposed localization algorithm has adjusted DV-Hop to have an average distance of per hop and uses least square method to equate coordinates of unknown nodes. It says that a simulation using Matlab to assess the performance of the proposed algorithm shows that the proposed algorithm has lower positioning error than DV-Hop and has accuracy of 22.3%.

Location technology is the key support technology of wireless sensor network (WSN). The hop number and hop distance information obtained by traditional distance vector hop (DV-Hop) location algorithm can only be acquired by solving the nonlinear equations, and the solution of the equation determines the accuracy of node location. Although the least squares method has better estimation performance, the solution results are sensitive to the average hop distance, which will lead to the large error in the solution of the equation. In order to solve the problem of location error caused by initial value sensitivity of least squares method in the coordinate calculation stage of unknown nodes and beacon nodes, a range-free location algorithm based on particle swarm optimization (PSO) is proposed in this paper. The proposed approach solves the problem of location error caused by initial value sensitivity of least squares method, obtains relatively accurate solution, and improves the accuracy of location algorithm. The experimental results show that the PSO algorithm has faster convergence speed and higher location accuracy than the non-optimization algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) possesses very broad application prospect in many fields, where the node location technology is one of the key technologies of WSN. Distance vector hop (DV-Hop) localization algorithm is a widely used algorithm in this technology, and it uses routing exchange protocol to make unknown nodes obtain beacon node information which will be used for coordinate calculation, therefore there exists certain error for the algorithm itself. Aiming at the disadvantage of large error existing in the traditional wireless sensor network location algorithm based on DV-Hop, an improved DV-Hop algorithm based on hop thinning and distance correction is proposed. The minimum hop is corrected by introducing received signal strength indication (RSSI) ranging technology, and the average hop distance is corrected by weighted average value of hop distance error and estimated distance error. Subsequently, the overall improvement on the location performance of the Hop-DV location algorithm is realized, and the location error is reduced. Under the Matlab simulation environment, the simulation experiment on the improved algorithm is carried out. The experimental results show that the improved algorithm reduces the location error and has higher location accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms. [ABSTRACT FROM AUTHOR]