DV-Hop Localization Algorithm Research Articles

An Improved DV-Hop Localization Algorithm Based on Hop Distance and Hops Correction

In order to solve the problem that the DV-Hop algorithm has large error in the network topology environment of random distribution of nodes, this paper proposes a WSN localization algorithm based on hop distance correction. The algorithm uses the method of multi communication radius to correct the hop number within 1 hops, which makes it more consistent with the real situation; Then, the weighted factor is obtained by error of the average distance per hop of anchor node; Finally, the total least squares method is used to calculate the coordinates of the unknown nodes to improve the positioning accuracy of DV-Hop positioning algorithm. The simulation results show that the improved algorithm has a better accuracy and stability than the original DV-Hop algorithm.

Novel DV-Hop localization algorithm for wireless sensor networks

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.

The Hybrid Technique for Improvement DV-Hop Localization Algorithms

The current sensor node localization algorithm consists of two types: range-based and range-free. Received Signal Strength (RSS) (range-based) and DV-Hop (range-free), are two different schemes that each have environment or hop dependency problems and have been combined and studied in several works. Although location accuracy has been improved, the degree can be more enhanced. In this paper we propose a modification hop count DV-hop calculation method using the RSSI Attenuation Model. This new method provides significant improvements in localization accuracy compared with previous algorithms. A comparative study of different improvement and hybrids DV-Hop algorithms is carried out and different results are obtained.

An Improved DV-Hop Localization Algorithm Based on Bat Algorithm

Abstract 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.

Effect of network topology on localization algorithm’s performance

Localization is an important part of wireless network design. In many internet of things (IoT) and other applications, the information produced by an individual entity or node is of limited use without knowledge of its location. Not only it is needed to report data that is geographically meaningful, but it is also required for services such as geographic and context-based routing protocols, location-aware services, location of sensed events in the physical world, object or target tracking, coverage area management and disaster event notification, like gas leakage detection system. This work is intended to perform comprehensive study on effects of topology on performance of existing range-free localization algorithms, in particular, localization in anisotropic network (irregular areas due to holes). We extended a simulation framework, written in Python, which is specially designed for wireless network simulation and add an interactive generic topology generator module. Our simulation framework also produces interactive charts, plots, and log all relevant simulation results for further analysis. In the first phase of our research we are interested in establishing a comprehensive simulation framework or guideline by implementing the original Distance Vector or DV-Hop algorithm. We first generate multiple isotropic and anisotropic topologies of different shapes and then simulate the DV-Hop localization algorithm. We then analyze the results statistically and visually. Our proposed contribution in this paper provides a framework and guideline to systematically and statistically study and compare different network algorithms.

LSDV-Hop: Least Squares Based DV-Hop Localization Algorithm for Wireless Sensor Networks

LSDV-Hop: Least Squares Based DV-Hop Localization Algorithm for Wireless Sensor Networks

An Improved DV-Hop Localization Algorithm Against Wormhole Attack in WSN

Wormhole attack is one of the most serious attacks on DV-Hop localization algorithm. Previous solutions usually require hardware support, or a higher ratio of beacon nodes and very complex mathematical operations. In this paper, we analyze the effect of the wormhole attack on DV-Hop algorithm, which mainly destroys the network topological structure and shrinks hop count between nodes and expands the localization error. We propose a modified DV-Hop method without additional hardware support against wormhole attacks. Through analyzing the characteristics of the neighboring nodes set of nodes attacked, this method detects wormhole attack, then locates nodes by using an improved DV-Hop algorithm. We show that the scheme can detect wormhole attacker adequately, and the obtained localization accuracy is even better than that of the DV-Hop method without wormhole attacks in most cases.

An Improved DV-Hop Localization Algorithm Based on the Node Deployment in Wireless Sensor Networks

An Improved DV-Hop Localization Algorithm Based on the Node Deployment in Wireless Sensor Networks

An Improved DV-Hop Algorithm Based on Shuffled Frog Leaping Algorithm

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, SF LA DV-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.

Two Novel DV-Hop Localization Algorithms for Randomly Deployed Wireless Sensor Networks

Aiming at solving the problem of low accuracy in traditional distance vector-hop (DV-hop) algorithm used for wireless sensor networks node location, two refined localization algorithms, that is, hyperbolic-DV-hop localization algorithm and improved weighted centroid DV-hop localization algorithm (IWC-DV-hop), are proposed in this paper. Instead of taking the average HopSize of BeaconNode nearest to the UnknownNode, hyperbolic-DV-hop algorithm chooses the average of average HopSizes of all BeaconNodes, as the average HopSize of the UnknownNode. The ML localization algorithm is also replaced by hyperbolic location algorithm. IWC-DV-hop algorithm improves the accuracy by selecting appropriate BeaconNodes and replacing ML localization with centroid localization. And a weighted scheme is used in IWC-DV-hop so that the influence of different anchors is taken into consideration. Simulations have been conducted to prove that the accuracy is improved by both of the two algorithms and IWC-DV-hop is the best choice.

Research on the Improved DV-HOP Localization Algorithm in WSN

To solve the inaccurate node localization in WSN, this paper first analyzes the problems in current DV-HOP algorithm, takes the received signal RSS as a reference standard through the weighted centroid algorithm, effectively reduces the localization errors, and adopts the improved two-dimensional hyperbola algorithm in the distance estimation to make the estimated distance more accurate. The simulation results show that the presented algorithm has been significantly improved compared to the algorithms in literature, and enhanced the localization accuracy to a certain extent.

Improvements of DV-Hop localization algorithm for wireless sensor networks

Based on the improvements of the original DV-Hop localization algorithm, three new localization algorithms (iDV-Hop1, iDV-Hop2, and Quad DV-Hop) are presented in this article. In iDV-Hop1 and iDV-Hop2, all steps of the original DV-Hop are kept, and several steps based on geometry improvements of the localization problem are added in order to obtain better localization accuracy. The third algorithm (Quad DV-Hop) formulated the localization problem as bounded least squares problem, to be solved by quadratic programming. Simulations are carried out on the four different types of network topology by varying nodes communication range, number of anchor nodes and number of nodes. Comparison of our algorithms with the original DV-Hop and Improved DV-Hop algorithms are given. It is shown that iDV-Hop1 algorithm can significantly reduce the localization error (up to three times) in scenarios with irregular topologies compared to DV-Hop and Improved DV-Hop. In scenarios with regular topologies, iDV-Hop2 and Quad DV-Hop showed better performance compared to DV-Hop and Improved DV-Hop (up to 11 % lower localization error).

Research on an Improved DV-HOP Localization Algorithm Based on PSODE in WSN

Research on an Improved DV-HOP Localization Algorithm Based on PSODE in WSN

DV-Hop Localization Algorithm Based on RSSI Correction

DV-Hop Localization Algorithm Based on RSSI Correction

An Improved Dv-Hop Localization Algorithm Based On Machine Learning For Wireless Sensor Network

The DV-Hoplocalization method has a series of superiorities, such as high distributiveness and expandability, which is perfectly fit for large-scale deployment. Moreover, it may lead to reasonable positioning accuracy merely in isotropous dense network. In practical environment, most scenes are anisotropic, with unevenly distributed nodes. In this paper, kernel PCR method is applied to collect and utilize the correlation between hop count and real distance, so as to build an optimal relationship model, converting hop count information between nodes into the value of real distance, so that DV-Hop method may be applicable to different environment. Compared with existing similar and typical methods, the method proposed in this paper has higher environment adaptability, as well as higher positioning accuracy and stability.

DVHop Localization Algorithm with Collinearity in Wireless Sensor Network

DVHop Localization Algorithm with Collinearity in Wireless Sensor Network

Optimization of DV-hop localization algorithm in hybrid optical wireless sensor networks

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Research on Location Feedback Based DV-Hop Localization Algorithm

In wireless sensor networks (WSNs), estimation of the location of the unknown node based on the average hop distance is an important research problem for range free localization algorithm. As one of the range free algorithm, DV-Hop chooses the average hop distance comes from the nearest beacon, can't reflect the real status of WSNs. We observe that the unknown node can achieve the precise location when one feedback channels are built between the unknown node and the beacons which embedded accurate location. Based on this observation, we propose one improved DV-Hop localization algorithm based on feedback mechanism (FDV-Hop). Using DV-Hop, the unknown node achieves the estimated location, and broadcasts its average hop distance to the beacons. The beacons also use DV-Hop to calculate their location based on the average hop distance from the unknown node. Then the beacons calculate the difference between the estimated location and the real location, and send the difference between them to unknown node with weights setting. The unknown node recalculates its location which involving the difference of location and the weights. The simulation shows that FDV-Hop can reduce the average localization error effectively and keep the localization stable.

Research on Wireless Sensor Networks with an Improved DV-Hop Localization Algorithm Based on Weighted Processing and Error Modification

Regarding the conventional DV-Hop algorithm easily caused big error in a network topology scenario, this paper proposes an improved DV-Hop localization algorithm comprehensive consideration of all anchor nodes average one-hop distance and normalized weighted, use anchor nodes as unknown nodes calculating error and use the error optimizing accuracy. Theoretical analysis and simulation results show that the proposed algorithm has better locating performance in locating precision and precision stability.

Calibration Based DV-hop Localization Algorithm for WSNs with Different Network Topologies

Aiming at the deficiencies and limitations of DV-hop localization algorithm, this paper proposes a calibration based DV-hop localization algorithm, which defines a new measure to estimate the neighborhood distances by relating the proximity of two neighbors to their connectivity difference. Furthermore, this algorithm adds the calibration step, which moves the estimated position of unknown node to the more reasonable one with the help of credible neighborhood distance. Simulation results demonstrate that our algorithm is more reliable in various topologies and achieves better localization performance in comparison with the existing relative algorithms under the same scenario.