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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]

In recent days, underwater exploration has emerged as one of the most predominant technologies for enhancing surveillance and early warning systems. Finding the location of the nodes placed in underwater is a difficult task owing to its harsh underwater environment. In large underwater wireless sensor networks (UWSNs), pinpointing the exact coordinates of sensor nodes may not be feasible or be incredibly expensive. In most of the applications, the coarse coordinate of the node is adequate. The primary technique used in UWSNs to determine the location of sensor nodes, based on the average distance between hops, is referred to as distance vector‐hop (DV‐Hop) localization. Nevertheless, the positioning accuracy in the classic DV‐Hop technique is influenced by the average hop distance. To reduce the localization error, it is possible to create a distinct and optimized DV‐Hop approach. To improve the effectiveness of the localization process, the average distance between hops is primarily used as an objective function. The optimization of this objective function is achieved by employing the Pelican Optimization Algorithm (POA). There is a noticeable decrease in the localization discrepancy if the optimized average hop distance is used to precisely determine the unidentified node to the anchor node distance among them. The factors used to evaluate the ability of the proposed methodology are the ratio of anchor, transmission range, and the density of the node. Compared to other localization procedures, the obtained outcomes demonstrate that the optimized approach that has been suggested achieves a low localization error of 0.3. [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]

A DV-Hop localization algorithm based on multi-communication radius and improved Archimedes optimization algorithm was proposed to address the problem of significant deviation of unknown nodes between the estimated position and the actual position in traditional DV-Hop algorithm. Firstly, the method of multi-communication radius for anchor nodes was adopted to reduce the error of unknown nodes in selecting the minimum number of hops. Then, improvements were made to the three steps of the Archimedes algorithm to improve its performance. Finally, the unknown nodes’ coordinates were obtained using improved Archimedes algorithm. The experimental results show that the proposed algorithm has better localization performance in the same environment. [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]

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range. [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.

This paper introduces an improved DV-hop-based localization technique to solve the link failure problem without using extra hardware during the continuous localization process because of the uncertainty of the underwater channel, limited energy resources, and technical glitches. On the other hand, low accuracy and poor stability are the main problems of the traditional DV-hop technique. This paper also introduces a hybrid TDoA-DV hop algorithm to overcome these problems and evaluate the position of the target nodes. In this algorithm, the average hop distance is replaced with improved hop distance to suppress the hop distance error, and the Wild Horse Optimization (WHO) algorithm is used for position estimation to find out the best and fast optimum location of the target node and achieve better accuracy and stability. The experimental result of the proposed work is compared to existing works, including the traditional DV-hop, improved DV-hop, and improved shortest path +PSO. The simulation result shows that the proposed method improves the average accuracy by 67.9%, 56.24%, and 3.6%, and stability by 61.9%, 45.9%, and 5.15% approximately. The recommended technique is useful for localization in any static ad-hoc wireless or underwater sensor network due to its better accuracy and stability. [ABSTRACT FROM AUTHOR]

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]

The purpose of this paper is to combine machine learning to locate the 3D sensor network space. Real life is mostly a three-dimensional environment. Whether it is a factory in manufacturing or a vegetation base in agriculture, it needs to be monitored and positioned. In this paper, the localization algorithm is discussed to a certain extent. This paper firstly introduces the relevant background and organizes related work. It also wrote related algorithms, such as ranging-based positioning algorithms in the free space of wireless sensors. It shows the positioning link by introducing the wireless sensor network structure system and node structure. And this paper summarizes the Bounding-box Method positioning principle, TDOA algorithm principle, and TDOA positioning principle. It then describes the gradient boosting tree classification algorithm based on machine learning, and focuses on the admiral boosting tree classification algorithm related to the experiment. This paper also describes the ranging technology combining RSSI algorithm and DV-Hop algorithm in three-dimensional space, and mentions two algorithms of RSSI and DV-Hop. In the fourth part, the machine learning coordinate prediction accuracy improvement experiment and the three-dimensional space positioning algorithm optimization experiment and result analysis are carried out. It is proved by experiments that the model evaluation effect of the gradient boosting tree classification algorithm in machine learning is the best. It can be applied to the calculation of relative position coordinates of label nodes. It then carried out the three-dimensional positioning effect test experiment of IDV-Hop algorithm. This shows that when the network density in the experimental environment reaches more than 12, the localization coverage of IDV-Hop algorithm and DV-Hop algorithm are both higher than 91%. Finally, the hybrid algorithm of RSSI and DV-Hop algorithm is used to compare the positioning accuracy, positioning coverage and bad node rate with these two algorithms. It draws the stability of the hybrid algorithm and its effects, and finally discusses and summarizes the experiments. [ABSTRACT FROM AUTHOR]

With the gradual reduction of conventional energy sources, the problem of environmental pollution is becoming more and more serious. Environmentally friendly, efficient and flexible power generation models are widely favored, and microgrids emerge as the times require. Microgrid is a medium and low voltage network including power distribution, storage devices, loads and other equipment. It can operate in grid-connected mode or as a stand-alone system in an island environment, continuing to supply power to users even when the large grid is disrupted. Compared with traditional power grids, microgrids have small capacity and changeable operation modes, making it difficult to locate distribution areas and short-circuit currents. At the same time, it is difficult to trace the faults during operation. So far, microgrids have had a lot of technical issues in operation that need to be resolved quickly. In recent years, IoT technology has developed rapidly. The Internet of Things is a comprehensive network that integrates location services, monitoring services, information identification and monitoring services through information exchange and communication technologies, such as video recognition technology, positioning technology, and wireless sensor technology. In order to monitor the operation mode of the microgrid in real time, find the source of the fault and locate the operation target, based on the Internet of Things technology, this paper installs wireless sensors in the line segment to monitor the line and other applications. This paper uses wireless sensor networks to record positioning variables, processes and data analysis to achieve the accuracy of microgrid operation target positioning. In addition, in order to improve the positioning accuracy of wireless sensor monitoring network and solve the problem of energy limitation, DV-Hop algorithm EB-IDMF algorithm and R-A algorithm are also introduced. Finally, the feasibility of the system is verified through experiments and tests. With the blessing of the positioning algorithm, the positioning accuracy is improved by 4.9%, which has great practical value for the target positioning of microgrid operation. [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.

The process of locating nodes is really a challenging problem in the field of wireless sensor networks. Wireless sensor network localization is commonly followed by the distance vector algorithm. All beacon nodes are currently using DV-Hop algorithms to locate the dumb node. On the other hand, the approximate distance from the dumb node to certain beacon nodes contains a significant error, resulting in a large finished dumb node localization problem. To improve localization error an efficient DV-Hop method on social learning class topper optimization for wireless sensor networks is implemented in this paper. The proposed algorithm reduces communication between unknown or dumb and beacon nodes by measuring the dimensions of all the beacons at dumb nodes. The network imbalance model is frequently used to show the applicability of the proposed approach in anisotropic networks. Simulations are performed on LabVIEW 2015 platform. The results show that our proposed method outperforms some existing algorithms in terms of computing time (2%), localization error (6.6%), and localization error variance (8.3%). [ABSTRACT FROM AUTHOR]

Aiming at the problem that the DV-HOP and MDS-MAP localization algorithms have large positioning errors when applied in the concave region, this paper proposes a fusion algorithm DV-MDS-SA localization algorithm. Firstly, the estimated distance from non-ranging unknown node to anchor node is obtained by DV hop algorithm. Then, the DV-Hop localization algorithm is used to multiply the shortest number of nodes by the single hop correction value to obtain the shortest distance between nodes. The shortest distance is applied to the MDS-MAP algorithm to find the estimated position of a group of non-ranging unknown nodes. Finally, in order to obtain more accurate positioning results in the concave area, the simulated annealing algorithm is used to optimize the estimated position of the unknown node obtained in the previous step, so as to further reduce the positioning error. The simulation results show that the DV-MDS-SA positioning algorithm proposed in this paper can obtain more accurate positioning results under the same network environment and has high application value. [ABSTRACT FROM AUTHOR]

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.

Wireless Senser Network (WSN) is broadly employed in many real life applications. WSN can be applied for sensing, collecting, processing and transmission of information. The importance of optimization methods plays a key role in the consistent and realistic estimation of the position of the sensor node. This article presents for improved DV-Hop localization using Class Topper Optimization (CTO) to address the current drawback of conventional DV-Hop. In the proposed algorithm, a factor to modify the hop size of the beacon nodes is presented. It is concluded from experimental assessment that the proposed optimized algorithm plays better than alternative algorithms with similar characteristics under the influence of variable number of nodes, beacon nodes and range. The proposed algorithm is compared with conventional DV-Hop, IDV-Hop algorithm and DV-Hop-based particle swarm optimization. Simulation results show that the localization error, error variance and accuracy are minimized by the proposed localization algorithm relative to current localization techniques. [ABSTRACT FROM AUTHOR]

The ability to obtain the accurate location of nodes in wireless sensor networks is crucial for practical applications. The sensed data is meaningless if it is not accompanied by its location. Range-free localization techniques are favored to overcome the hardware limitations of sensor nodes and to avoid the costly range-based techniques. DV-Hop is a range-free localization algorithm that is well-known for its simplicity. However, it suffers from low accuracy and poor stability. In this paper, an enhanced variant of the DV-Hop algorithm is used to estimate the distance between the unknown nodes and anchor nodes, then the position estimation phase is formulated as a minimization problem solved by means of the recently developed squirrel search algorithm (SSA). The SSA is utilized to find the locations of the unknown sensor nodes. Our proposed algorithm is thus called SSIDV-Hop algorithm. The performance of our proposed algorithm is compared to that of existing localization algorithms including the DV-Hop, PSODV-Hop, GADV-Hop, and DEIDV-Hop algorithms. Extensive simulations showed that our proposed algorithm is superior to other existing algorithms as it achieved higher localization accuracy, better stability and faster convergence rate. [ABSTRACT FROM AUTHOR]

DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hops path is not a straight line, which leads to a faulty location estimation. The proposed model optimized distance range free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact, ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step, DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services related to IoT. In this paper, we propose a novel distributed range-free algorithm for node localization in wireless sensor networks. The proposed three-dimensional hop localization algorithm is based on the distance error correction factor. In this algorithm, the error decreases with the localization process. The distance correction factor is used at various stages of the localization process, which ultimately mitigates the error. We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm. The simulation results are compared with some of the well-known existing algorithms in the literature. The results show that the proposed three-dimensional error-correctionbased algorithm performs better than existing algorithms. [ABSTRACT FROM AUTHOR]

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 order to improve the localization accuracy of unknown nodes in DV-Hop algorithm, an improved DV-Hop algorithm combined with adaptive hop count and optimized hop distance was proposed. Our proposed algorithm firstly introduced the concept of the adaptive value for the number of communication radii. It was used to adaptively select the number of communication radii for beacon nodes and could realize the subdivision of the minimum hop count. Secondly, sensor nodes adaptively set the hop threshold to reduce the calculation. Finally, the calculation method of the average hop distance of beacon nodes was optimized and the error was corrected. The average hop distance of unknown nodes was obtained by the weighted normalization used for the average hop distance of beacon nodes. Simulation results in both random deployment environment of sensor nodes and nonrandom deployment environment of beacon nodes demonstrate that the localization accuracy of our proposed algorithm is significantly better than the DV-Hop algorithm and furthermore has better robustness and adaptability. [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]

In sensor network topology environment with uneven node distribution, running the DV-Hop localization algorithm often caused large errors of hop value. Considering the problem above, this paper proposed an improved localization algorithm named ILDV-Hop. Firstly, for the whole network, this algorithm calculated an effective hop size based on difference of actual and estimated distance between reference nodes. Then, with adding a correction value to the hops between unknown nodes and reference nodes, it also used the received signal strength indicator (RSSI) value to correct the distance of a single hop. Finally, the Levenberg-Marquardt algorithm was applied to estimate an optimize position for each sensor. Simulation results indicate that, compare with the DV-Hop algorithm and the DV-Hop algorithm based on Quasi-Newton, the ILDV-Hop algorithm has reduced the errors by about 23% and 10% respectively, and the localization performance has increased visibly. [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]

For digital mine applications, it has the characteristics of diverse terrain, complex weather conditions, large monitoring area, large number of sensor nodes, multisource information for each node, and long monitoring period.With the goal of good environmental adaptability, low power consumption, low cost and standardization, the key technology of wireless sensor network for digital mine is proposed, including network structure, networking mode, node location method, data fusion method, fast selfsufficiency and energy saving strategy. For the application of digital mine, the improved DV-Hop algorithm is proposed to locate the nodes, and then by analyzing the IoT-WSNs network model and the node energy consumption model, a new clustering routing algorithm is proposed. The experimental results show that the wireless sensor network technology designed in this paper satisfies the application requirement of digital mine well. Both hardware and software are convenient for system integration, and it is suitable for standardization and large-scale popularization. The proposed algorithm is effective and reliable. [ABSTRACT FROM AUTHOR]

In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed randomly. The result from sensing becomes meaningless if it is not known from where the originating node is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The localization scheme consists of distance estimation and position computing. Thus, this research used connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral algorithm for computing the position. Besides that, distance estimation using the connectivity between nodes is not needed for the additional hardware ranging as required by a range-based localization scheme. In this research compared the localization algorithm based on range free localization, which are DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous alg orithm have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and energy consumption. On node density variations, dv-hop algorithm gained a localization error that is 26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained 14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm gained a localization error that is50.282% lower than amorphous. However, for energy consumption parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm. [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.

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching-Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms. [ABSTRACT FROM AUTHOR]

The DV-Hop localization algorithm, which uses the product of the hop count and the average hop distance to estimate distance and uses the maximum likelihood estimation method for positioning, has larger error. Aiming at the problem above, this paper proposed a developed localization algorithm OPDV-Hop, which based on the optimal hop distance and the improved particle swarm. Firstly, this algorithm adopted communication radius to revise hops between nodes. Then it optimized the current hop distance according to the average hop distance in the adjacent area of unknown nodes. Lastly, it applied the improved particle swarm algorithm to optimize the unknown node coordinates. The simulation results show that, compared with the DV-Hop algorithm, the DV-Hop algorithm based on particle swarm and the algorithm based on improved particle swarm, OPDV-Hop algorithm reduces the errors by about 18% , 13% and 7% respectively. Thus, it can effectively lower down the estimation errors and improve the positioning accuracy. [ABSTRACT FROM AUTHOR]

Efficient localization algorithm plays an important role in the application of wireless sensor network ( WSN ). During the process of estimating unknown node position , the accuracy of DV-Hop algorithm is not that perfect. In terms of the improvement of accuracy, this paper put forward a SACSDV-Hop algorithm. Firstly, it introduced the cuckoo search ( CS) algorithm. Secondly, it dynamically adjusted the discovery probability pa and the parameter (β impacted step size to improve the convergence speed and local search ability of CS algorithm. SACSDV-Hop algorithm used SACS algorithm instead of DV-Hop algorithm to estimate the coordinate position of the unknown node stage, which used the least square method. It transformed the node localization problem into intelligent optimization. Simulation results show that the SACSDV-Hop algorithm is better than the CSDV-Hop algorithm and DV-Hop algorithm, which has a higher positioning accuracy and does not need to increase the hardware cost. [ABSTRACT FROM AUTHOR]

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]

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Many improved DV-Hop localization algorithm have been proposed to enhance the localization accuracy of DV-Hop algorithm for wireless sensor networks. These proposed improvements of DV-Hop also have some drawbacks in terms of time and energy consumption. In this paper, we propose Novel DV-Hop localization algorithm that provides efficient localization with lesser communication cost without requiring additional hardware. The proposed algorithm completely eliminates communication from one of the steps by calculating hop-size at unknown nodes. It significantly reduces time and energy consumption, which is an important improvement over DV-Hop-based algorithms. The algorithm also uses improvement term to refine the hop-size of anchor nodes. Furthermore, unconstrained optimization is used to achieve better localization accuracy by minimizing the error terms (ranging error) in the estimated distance between anchor node and unknown node. Log-normal shadowing path loss model is used to simulate the algorithms in a more realistic environment. Simulation results show that the performance of our proposed algorithm is better when compared with DV-Hop algorithm and improved DV-Hop-based algorithms in all considered scenarios. [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]