Novel enhancements for node localization in 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.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Localization with low cost and high accuracy is of utmost importance for most applications in wireless sensor networks (WSNs). Most existing range-free schemes have limitations in one or more areas, such as low accuracy, low scalability and threat from the malicious nodes. Secure sensor localization in WSNs has received considerable attention with the rise of Internet of things. In this paper, we propose an efficient and secure range-free localization scheme based on the outlier elimination and the vector refinement process. An outlier elimination method is introduced to the localization process by filtering inaccurate beacon nodes in the presence of the malicious beacon nodes, which can prevent the location attacks and improve the localization accuracy. A vector refinement feedback process is introduced to further improve the localization accuracy. Parameters that affect the localization accuracy are discussed. Simulation and experiment are carried out to evaluate the performance of the proposed outlier elimination vector refinement DV-hop with the presence of the malicious beacon nodes. All the results show that the localization accuracy is improved significantly compared to the DV-hop algorithm and the vector refinement DV-hop algorithm, for both of the cases of with and without the malicious beacon nodes.

Wireless Sensor Network (WSN) localisation is an essential requirement in the increasing prevalence of WSN applications. It is an important part of the Internet of Things (IOT) and has become a hot research area. Distance Vector-Hop algorithm (DV-Hop), a range-free algorithm, is widely deployed to solve the localisation problem in WSN. However, the results of the estimation precision are usually not satisfactory. In order to improve the WSN positioning accuracy, in this paper, we propose a new coupling algorithm based on Bacterial Foraging Algorithm (BFA) and Glow-worm Swarm Optimisation (GSO) (BFO-GSO). The algorithm has good convergence speed, local search ability of BFO and global convergence of GSO. The optimisation performance is verified by CEC2013 benchmarks in those designs against the original algorithm. Furthermore, Wilcoxon's rank-sum non-parametric statistical test and Friedman test are carried out to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The numerical results prove that it is able to significantly outperform others on majority of the benchmark functions. Finally, the proposed algorithm is also combined into the DV-Hop algorithm to improve the WSN positioning accuracy. Experimental results show that our improved algorithm achieves better performance when compared with other DV-Hop algorithms.

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 algorithm 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.

Improved DV-HOP localization algorithm based on grey wolf optimization

Wireless sensor networks (WSNs) are indispensable for various applications, including environmental monitoring and factory automation. Nevertheless, the precision with which sensor nodes can be localized is crucial to the accuracy and dependability of WSNs. Using trilateration techniques presents a novel method for enhancing localization accuracy in WSNs. The proposed method utilizes mathematical models and algorithms to predict the precise positions of sensor nodes, compensating for the limitations of conventional localization techniques. Through extensive testing and analysis, this demonstrates how enhancements to localization accuracy, positioning precision, and network performance can make WSNs more reliable and effective across various use cases. This study investigates the technology underlying the trilateration-based method and outlines its potential applications and benefits. The importance of precise localization in WSNs is emphasized, and research directions for wireless sensor network optimization are provided.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

In this paper, we propose an improved DV-Hop algorithm for wireless sensor network (WSN) node localization. Our algorithm applies a novel virtual node method to calculate the average hop size and a novel rule to correct unreasonable distance. Then it introduces a kind of weighted least squares method to calculate the locations of unknown nodes. Our algorithm doesn't require beacon nodes to broadcast additional information. We simulated our algorithm contrasting with the original DV-Hop algorithm under different conditions. The experiment results showed that our algorithm improved the localization accuracy obviously.

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average.

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.

In wireless sensor networks (WSNs), localization is one of the most important topics because the location information is typically useful for many applications.The primary data used in a localization process include the locations of anchor nodes and the distances between neighboring nodes.However, these data may contain outliers that deviate from their true values.The existence of the outliers might make the estimated positions not accurate.Thus, it is important to detect and handle outliers in order to achieve high localization accuracy.In this paper, we survey the existing outlier detection techniques for localization in wireless sensor networks.We provide taxonomy for classifying outlier detection techniques in WSNs localization based on different features.In addition, we present comparisons of these techniques.Finally, we discuss the future research directions in this area.