Range-free Localization Algorithm Research Articles

Distance correction range-free localization algorithm for WSNs

Accurate location information is essential for the effective deployment of Wireless Sensor Networks (WSNs) applications. Recently, researchers have developed numerous localization algorithms, with multi-hop methods being particularly attractive due to their cost-effectiveness and robustness, but their accuracy is still unsatisfactory. For better localization accuracy, this paper presented a distance correction range-free localization algorithm (DCRA). Firstly, the proposed algorithm estimates the distance between neighboring nodes based on neighbor connectivity to improve the accuracy of single-hop distance estimation. Secondly, during the multi-hop distance estimation phase, path similarity metrics are employed to ascertain the optimal path correction coefficients, thereby minimizing multi-hop distance estimation errors. Finally, based on the estimated distances, the node localization is converted into an optimization problem, which is then solved using an enhanced particle swarm algorithm to obtain the node coordinates. Simulation results show that the distance estimation accuracy of the DCRA algorithm in different node density scenarios is improved by more than 31 % and 12 % compared to the DV-Hop and DV-RND algorithms, respectively; and in terms of localization accuracy the DCRA algorithm is improved by more than 58 % and 35 % compared to the DV-Hop and DV-RND algorithms, respectively.

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things.

Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

A Hybrid Localization Algorithm for an Adaptive Strategy-Based Distance Vector-Hop and Improved Sparrow Search for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are applied in many fields, among which node localization is one of the most important parts. The Distance Vector-Hop (DV-Hop) algorithm is the most widely used range-free localization algorithm, but its localization accuracy is not high enough. In this paper, to solve this problem, a hybrid localization algorithm for an adaptive strategy-based distance vector-hop and improved sparrow search is proposed (HADSS). First, an adaptive hop count strategy is designed to refine the hop count between all sensor nodes, using a hop count correction factor for secondary correction. Compared with the simple method of using multiple communication radii, this mechanism can refine the hop counts between nodes and reduce the error, as well as the communication overhead. Second, the average hop distance of the anchor nodes is calculated using the mean square error criterion. Then, the average hop distance obtained from the unknown nodes is corrected according to a combination of the anchor node trust degree and the weighting method. Compared with the single weighting method, both the global information about the network and the local information about each anchor node are taken into account, which reduces the average hop distance errors. Simulation experiments are conducted to verify the localization performance of the proposed HADSS algorithm by considering the normalized localization error. The simulation results show that the accuracy of the proposed HADSS algorithm is much higher than that of five existing methods.

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN.

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

Effect of fitness function on localization performance in range-free localization algorithm

Effect of fitness function on localization performance in range-free localization algorithm

Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

Range-Free Localization Using Extreme Learning Machine and Ring-Shaped Salp Swarm Algorithm in Anisotropic Networks

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors such as the existence of holes, non-uniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine and ring-shaped salp swarm algorithm is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of extreme learning machine, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the salp swarm algorithm based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.

A Distance Estimation Model for DV-Hop Localization in WSNs

The localization of sensor nodes in wireless sensor networks (WSNs) has been a thorny research problem. Existing DV-Hop based range-free localization algorithms estimate the distance from an unknown node to an anchor node by the multi-hop forwarding principle and the location information of anchor nodes. However, they lack an accurate distance estimation model of anchor nodes under different hops, resulting in inaccurate localization. To address this issue, this paper proposes a distance estimation model (DEM) for DV-Hop localization in WSNs. Specifically, DEM mathematically derives the farthest distance detected by an anchor node under different hops. Then, the average distance is calculated by the upper and lower boundaries according to the derived farthest distance. Finally, the average distance is embedded into a multi-objective optimization to solve the DV-Hop localization problem. The simulation results show that the proposed model is significantly superior to the state of the art in terms of sensor localization accuracy and cost effectiveness.

Improved Wireless Sensor Network Localization Algorithm Based on Selective Opposition Class Topper Optimization (SOCTO)

One of the most important challenges in wireless sensor networks is the issue of localization. Furthermore, it is critical to monitor and evaluate the data gathered. With the aid of a collection of constructed nodes known as beacons, the localization technique can measure node location. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the distance vector-hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. Based on a proposed selective opposition class topper optimization (SOCTO), an enhanced DV-Hop localization algorithm is developed. It also focuses on an optimised formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error within the estimated distance between the beacon and the dumb node, due to improved localization accuracy. The range-free localization algorithm in uniform multi-hop wireless sensor networks is the subject of a recent study. The results show that our proposed method outperforms the DV-Hop technique and related techniques in terms of average localization error (\(0.05\%\)) versus random beacon node deployment, average localization error (\(0.04\%\)) versus circular beacon node deployment, and average localization error (\(0.08\%\)) versus spiral beacon node deployment.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

AbstractImproving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

Stacked Autoencoders-Based Localization Without Ranging Over Internet of Things

Location information plays an important role in many applications of the Internet of Things (IoT). The low cost and ease of scalability of range-free localization algorithms have attracted the attention of many researchers, but the performance of many localization algorithms available in the literature varies greatly in different networks. Specifically, algorithms designed for anisotropic networks may not perform well in isotropic networks, and vice versa. To improve localization accuracy in both isotropic and anisotropic networks, a novel range-free localization algorithm named LSAE is proposed in this article, oriented to the network positioning without ranging over the IoT. The proposed algorithm utilizes the known information in the network, namely, the hop counts and distances between anchor nodes, to train the stacked autoencoders (SAE) model. In this way, it achieves accurate prediction of the distances between unknown nodes and anchor nodes. To further improve the localization accuracy, the disadvantage of the least square method is analyzed, and a novel coordinate estimation method based on the statistical results of distance estimation errors is proposed. We conducted a huge number of numerical simulations with and without the impact of multiple anisotropic factors in three different types of networks. The results indicate that the proposed algorithm outperforms other state-of-the-art algorithms treating the impact of multiple anisotropic factors, and demonstrates the high accuracy and robustness.

A Node Localization Algorithm for Wireless Sensor Networks Based on Virtual Partition and Distance Correction

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

EARP: Extended Area Multihop Node Localization Algorithm for Anisotropic Wireless Sensor Networks

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in wireless sensor networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, this paper proposes an extended area multihop node localization method. The proposed method classifies and discusses the boundaries of the concave area within the communication range of the node and then uses the maximum split communication distance of the node to identify and mark the boundary of the concave area. When the shortest communication path between the nodes is affected by the concave area, the boundary of the concave area is expanded to obtain the new shortest communication path, and the node distance is obtained by comparing the changes in the communication path. After a large number of simulation tests, it is shown that the calculation accuracy of this scheme is better than that of similar modern mainstream localization algorithms.

Low Power Fast and Accurate Localization Algorithm in Wireless Sensor Networks

Finding precise node localization is one of the critical issues in wireless sensor networks (WSNs), as the nodes are generally deployed randomly in the area of interest and without location stamp of the event, there is no use of that particular information. In this paper, a range-free localization algorithm called Horizontal-Vertical (H-V) scan method is presented which works mainly on collinearity concept. This method firstly estimates the collinearity of anchor nodes and rules out those anchor nodes which are collinear for location estimation of target nodes. By ruling out collinear anchor nodes, location is calculated by other than these ruled out anchor nodes through trilateration method which improves the localization accuracy. A new trajectory is also proposed for this method which improves coverage, accuracy and time for calculating localization. Simulation results show that the proposed method performs better in terms of localization accuracy, than the existing similar range-free localization methods. Index Terms: Localization, Collinearity concept, H-V scan method, WSN, Localization accuracy.

Improved DV-Hop based on Squirrel search algorithm for localization in wireless sensor networks

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.

A comprehensive investigation on range-free localization algorithms with mobile anchors at different altitudes

In this work, the problem of localizing ground devices (GDs) is studied comparing the performance of four range-free (RF) localization algorithms that use a mobile anchor (MA). All the investigated algorithms are based on the so-called heard/not-heard (HnH) method, which allows the GDs to detect the MA at the border of their antenna communication radius. Despite the simplicity of this method, its efficacy in terms of accuracy is poor because it relies on the antenna radius that continuously varies under different conditions. Usually, the antenna radius declared by the manufacturer does not fully characterize the actual antenna radiation pattern. In this paper, the radiation pattern of the commercial DecaWave DWM1001 Ultra-Wide-Band (UWB) antennas is observed in a real test-bed at different altitudes for collecting more information and insights on the antenna radius. The compared algorithms are then tested using both the observed and the manufacturer radii. The experimental accuracy is close to the expected theoretical one only when the antenna pattern is actually omnidirectional. However, typical antennas have strong pattern irregularities that decrease the accuracy. For improving the performance, we propose range-based (RB) variants of the compared algorithms in which, instead of using the observed or the manufacturer radii, the actual measured distances between the MA and the GD are used. The localization accuracy tremendously improves confirming that the knowledge of the exact antenna pattern is essential for any RF algorithm.

V2X-Based Mobile Localization in 3D Wireless Sensor Network

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.

Improved 3-dimensional DV-hop localization algorithm based on information of nearby nodes

DV-Hop, a range-free localization algorithm, has been one of the most popular localization algorithm. It is easy and inexpensive to implement. Therefore, in the literature, many improved variants of this algorithm exist. However, poor location accuracy and higher power consumption by DV-Hop algorithm always open new avenues for research on this algorithm and makes it a favorite among the researchers. In this paper, we have proposed an Improved 3-Dimensional DV-Hop algorithm based on the information of nearby nodes (I3D-DVLAIN). In the algorithm, by calculating hopsize at the unknown nodes, we eliminate one communication among the nodes, which reduces power consumption in the network. The hopsize calculation and location estimation is done by using only the nearby anchor nodes, which minimizes the network usage and decreases the computational effort. For the selection of nearby anchor nodes, we introduce a new method. Further, for localization, a novel method is used for solving the system of distance equations that restricts propagation of inherent error in the distance and increases localization accuracy. Furthermore, by mathematically analyzing the propagation of error in solving the system of equations, we prove the superiority of I3D-DVLAIN over other compared algorithms. The results obtained through simulation and complexity analysis of the computation and communication further strengthens our observations about the superiority of the proposed algorithm.

Novel Distance Estimation based Localization Scheme for Wireless Sensor Networks using Modified Swarm Intelligence Algorithm

Wireless sensor networks (WSN) consists of a huge number of nodes that are positioned randomly to obtain information regarding the environment and communicate with each other. On detection of an event, obtaining information regarding the geographical location of the sensor is beneficial in most applications. Range-free and range-based localization schemes are the major categories of localization algorithms available. Range-free localization algorithms utilize the connectivity information to provide a cost efficient localization solution. On the other hand, range-based localization schemes use radio signal strength and distance from anchor nodes for estimating the unknown node location. Several swarm intelligence algorithms are used for reducing the noise while optimizing localization and distance estimation while using these schemes. In this paper, we propose an enhanced swarm intelligence scheme that provides better performance when compared to the existing algorithms in terms of noise level, signal strength, number of anchors, number of nodes, radio signal strength and localization error. Surrogate based optimization (SBO), firefly algorithm (FA), butterfly optimization algorithm (BOA), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are compared with the proposed scheme.

An efficient range-free multi-hop localisation algorithm for irregular wireless sensor networks

In the range-free multi-hop localisation algorithm, the physical distance between nodes is estimated based on hops, and complex ranging equipment is not required during this process. This could help realise simpler system configuration, while lowering the node cost and consumption of energy in wireless sensor networks. During the actual deployment of nodes, the deviation of the final estimated location from the actual location is generally caused by factors such as shielding or barriers during the hop distance conversion. In this paper, an improved method for range-free multi-hop localisation is proposed which could improve the precision without increasing the cost and reducing the performance of the algorithm. First, we obtain the optimal weighting function for location estimation according to the error between the estimated locations of non-beacon nodes and their actual locations. Then, we correct the inaccurate estimated locations of non-beacon nodes through geometric constraints. Lastly, we demonstrate via simulation that our proposed localisation method outperforms the basic range-free multi-hop algorithms as well as their improved methods in irregular wireless sensor networks.