DV-Hop Localization Algorithm Research Articles

Multi-Source Information Fusion-Based Localization in Wireless Sensor Networks

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[Formula: see text]m to 14.39[Formula: see text]m, thereby improving localization accuracy by 56.46%.

Improved DV-Hop Localization Algorithm Based on Variable Communication Radius for Wireless Sensor Networks

Improved DV-Hop Localization Algorithm Based on Variable Communication Radius for Wireless Sensor Networks

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

An IMOA DV-Hop localization algorithm in WSN based on hop count and hop distance correction

An IMOA DV-Hop localization algorithm in WSN based on hop count and hop distance correction

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

UBETI: A DV-Hop Localization Algorithm for IoT With Distance Upper Bound Estimation by Triangle Inequality

UBETI: A DV-Hop Localization Algorithm for IoT With Distance Upper Bound Estimation by Triangle Inequality

Retracted: Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Retracted: Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

An Improvement of DV-Hop Localization Algorithm Based on Improved Adaptive Genetic Algorithm for Wireless Sensor Networks

An Improvement of DV-Hop Localization Algorithm Based on Improved Adaptive Genetic Algorithm for Wireless Sensor Networks

Ethernet Information Security Protocols Based on Industrial Control Wireless Sensor Networks

This paper provides an in‐depth study and analysis of information security protocols for industrial Ethernet using wireless sensor networks. The optimal number of cluster heads for nonuniform subclustering is derived based on the sensor energy consumption model, and then, the EEUC contention radius formula is optimized to select candidate cluster heads with random values and energy as weights. A multihop approach based on the shortest offset is also proposed for intercluster information transmission. Experimental results show that the EEUC‐based improved cluster routing protocol proposed in this paper balances the node energy consumption and extends the network lifetime. In response to the problem that the coarsened hop value and average hop distance of the DV‐Hop localization algorithm cannot reflect the network topology, the improved DV‐Hop algorithm based on multicommunication radius and hop distance correction is proposed. Simulation experiments show that the improved algorithm based on multiple communication radius and hop count correction can significantly reduce the localization error and improve the accuracy of the algorithm. Aiming at the shortcomings of MRP with excessive risk concentration and transmission medium limitation, this paper proposes a fast self‐healing mechanism of industrial Ethernet with a multiexpert strategy. The PCP‐AP common platform architecture for openSAFETY sites is designed on the base sleeve of the implementation of the industrial Ethernet protocol Ethernet POWERLINK; the main communication part of POWERLINK is implemented through an FPGA hardware solution, and the openSAFETY site is implemented using AM335X high‐performance processor to implement openSAFETY security application functions. Finally, the article conducts field tests on the wireless signal information transmission, WSN data transmission, network connection, and power supply system in the system and compares and analyzes the data collected by the system with the monitoring data of the national control site. The data obtained by the system has real reliability. The communication module used is inexpensive, lightweight, and easy to operate. It can realize the collection of multiple pollution sources, and compared with traditional monitoring equipment, it avoids the difficulties of complicated wiring, difficult positioning of pollution sources, and restricted monitoring areas and largely reduces the investment in human and material resources.

Improvement on the DV-hop localization algorithm based on average hop distance optimization

Wireless sensor networks, which have numerous uses in numerous industries, have attracted more attention over the past few years. Node localization is one of the gordian technology to locate in it. This research sought to address the low precision of the classic DV-hop algorithm in the context of wireless sensor networks. This novel DV-hop localization algorithm is given some practical solutions in this paper. The results of the simulations demonstrate that the new method can improve accuracy and increase communication range. First of all, the least squares method is applied to correct the average length of each hop between beacon nodes. In addition, an error distance matrix is defined as an intermediate variable to reckon the average length between the unknown node and the beacon node for each jump and select the minor error distance matrix. Finally, this improved algorithm chooses the minimum error distance matrix from the previous steps and modifies it. Simulation consequences reveal that this modified algorithm can better enhance the accuracy and extend the communication range.

DV-Hop-based localization algorithm using optimum anchor nodes subsets for wireless sensor network

Node localization has become a crucial technology in wireless sensor network (WSN) research field. Although the widely used DV-Hop localization algorithm is simple and easy to implement, its positioning accuracy still needs to be further enhanced. To improve the DV-Hop algorithm accuracy, a DV-Hop-based scheme using optimum anchor nodes subsets (OANS DV-Hop) is put forward in this article. For OANS DV-Hop algorithm, first of all, each anchor node localizes itself by using other anchor nodes, and employs binary particle swarm optimization (BPSO) algorithm to generate an optimum subset composed of other anchor nodes except itself. Then the anchor node applies OANS to recalculate its average hop size and broadcasts the new hop size and OANS to the nearest unknown nodes. The unknown node localizes itself by using OANS instead of all anchor nodes. Finally, a fitness function is designed based on OANS, and the continuous particle swarm optimization (PSO) algorithm is applied to further increase the localization accuracy. Simulation results demonstrate that OANS DV-Hop algorithm owns higher localization accuracy compared with the primal DV-Hop and other improved DV-Hop algorithms in various network environments.

Three-Dimensional Node Localization Algorithm Based on Communication Radius Refinement and Improved Particle Swarm Optimization

Background: With the development of the Internet of things, WSN node positioning is particularly important as its core technology. One of the most widely used algorithms, the DV-hop algorithm, has many advantages, such as convenient operation, use of no additional equipment, etc. At the same time, it also has some disadvantages, like large location error and insufficient robustness. Particle swarm optimization algorithm is advantageous in dealing with nonlinear optimization problems. Therefore, the improved particle swarm optimization algorithm is introduced to solve the problem of inaccurate positioning. Objective: Aiming at the problem of large positioning error in the three-dimensional node localization algorithm, the paper proposes an intelligent node localization algorithm based on hop distance adjustment. The algorithm is used to optimize the hop number of nodes and make the distance calculation more accurate. At the same time, particle swarm optimization is used to intelligently solve the problem of choosing the most valuable node position. Methods: Firstly, this paper analyzed the errors caused by the 3D DV-hop localization algorithms. Then, a new method of distance estimation and coordinate calculation is provided. At the same time, mutation factors and learning factor based on the particle swarm optimization algorithm are introduced. Then, a three-dimensional node localization algorithm based on ranging error correction and particle swarm optimization algorithm is proposed. Finally, the improved algorithm is simulated and compared with similar algorithms. The simulation results show that the proposed algorithm has good convergence and improves the positioning accuracy without additional hardware conditions, and effectively solves the problem of inaccurate node positioning. : The simulation results show that the proposed algorithm has good convergence and improves the positioning accuracy without additional hardware conditions, and effectively solves the problem of inaccurate node positioning. Results: The proposed algorithm creatively combines the hop number correction and particle swarm optimization algorithm to improve the accuracy of node positioning and robustness. However，the amount of computation is increased. Conclusion: Overall, it is within acceptable limits. It is worthwhile to improve the performance with a little increase in the amount of computation. The algorithm is worth popularizing.

Node Hop Distance Error Optimization of Improved DV-Hop Localization Algorithm in Wireless Sensor Network

Based on the lower accuracy of classic DV-Hop localization algorithm, this thesis puts forward an improved DV-Hop localization algorithm. In the second phase of the improved algorithm, through selectively modifying the shortest path table, the hop distance error of a hop anchor node is reduced by using the neighbour node overlapping estimation, and the average hop distance is calculated with the two poles of whole network and local network. Meanwhile, the hop count distribution from nodes to corresponding anchor nodes within the communication range of nodes is used to modify the hop distance of N hop nodes, and the simulation experiment is conducted. The experimental results show that, in the experimental scene, the accuracy of the improved DV-Hop localization algorithm is about 10% higher on average than that of the classical DV-Hop localization algorithm.

Improved DV-Hop Localization Algorithm Based on Weighted Least Squares Cycle Optimization in Anisotropic Networks

Improved DV-Hop Localization Algorithm Based on Weighted Least Squares Cycle Optimization in Anisotropic Networks

Improved DV-Hop localization algorithm based on social learning class topper optimization for wireless sensor network

Improved DV-Hop localization algorithm based on social learning class topper optimization for wireless sensor network

A novel secure DV-Hop localization algorithm against wormhole attacks

A novel secure DV-Hop localization algorithm against wormhole attacks

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the k -means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved.

Location Prediction for Improved Human Safety at Complex Environments

In underground operation, primary consideration is safety. In recent decades, for minimizing accident and for preventing major economic losses and casualties, wireless sensors are used by various large mineral countries through early warning. The Improved DV-Hop Localization Algorithm (IDVHLA) is used in existing works for doing this. However, accurate anchor node detection is impossible in existing works with the malicious nodes presence, where there won't be any accurate sharing of anchor node's location information. In case of emergency situation, faster communication is a highly complex one. A technique called Modified Distance Vector Hop based Multipath Routing Protocol (MDVHMRP) is introduced in this proposed research work for resolving this. In this work, to detect anchor node position, a Modified Distance Vector Hop technique is introduced. This research work considers time taken and session time for computing neighbour node's presence in addition to hop count values. Malicious nodes presence can be avoided by considering session time in neighbour node presence detection. The alert message are send by people in emergency crisis to sever in initial condition. Then Dynamic Source Routing (DSR) routing protocol is used for doing immediate route path selection. In case of route path failure, for ensuring uninterrupted communication and faster communication, this work introduces a multi path routing. The modified distance vector hop technique is used for predicting anchor node location information and predicted information will be transmitted. In NS2 simulation environment, overall evaluation of this research work is carried out. When compared with available techniques, localization accuracy is enhanced by proposed technique as proven in experimental results.

Optimization of the DV-hop Localization Algorithm in Wireless Sensor Networks

Wireless sensor networks (WSNS) have the characteristics of low cost and large-scale distribution. However, due to the variable monitoring area and random node deployment, there are errors in node positioning. The technology of the node location is the key to the application of WSNS, because the location information of nodes is very necessary. A corresponding optimization method for the error caused by the minimum Hop in DV-hop positioning algorithm is introduced. By introducing RSSI value to weight the minimum hops, the setting of the minimum hops is more reasonable, so as to reduce the error. The simulation experiment proves the effectiveness of the improved algorithm.