Localization Algorithm For Sensor Networks Research Articles

Advanced localization algorithm for wireless sensor networks using fractional order class topper optimization

Advanced localization algorithm for wireless sensor networks using fractional order class topper optimization

Low beacon node localization algorithm in sensor networks based on multi-space key distribution algorithm

Low beacon node localization algorithm in sensor networks based on multi-space key distribution algorithm

Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO- BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.

Comparative Study of the Different Variants of the DV-Hop Based Node Localization Algorithms for Wireless Sensor Networks

Comparative Study of the Different Variants of the DV-Hop Based Node Localization Algorithms for Wireless Sensor Networks

Distributed localization using Levenberg-Marquardt algorithm

In this paper, we propose a distributed algorithm for sensor network localization based on a maximum likelihood formulation. It relies on the Levenberg-Marquardt algorithm where the computations are distributed among different computational agents using message passing, or equivalently dynamic programming. The resulting algorithm provides a good localization accuracy, and it converges to the same solution as its centralized counterpart. Moreover, it requires fewer iterations and communications between computational agents as compared to first-order methods. The performance of the algorithm is demonstrated with extensive simulations in Julia in which it is shown that our method outperforms distributed methods that are based on approximate maximum likelihood formulations.

Multi-target localization algorithm for wireless sensor network

Multi-target localization algorithm for wireless sensor network

Distributed localization algorithm for wireless sensor networks using range lookup and subregion stitching

One of the ways in which localization algorithms in wireless sensor networks (WSNs) have been categorized is whether they are range-based or range-free. Range-based algorithms use expensive hardware to measure one or more physical quantities and, in turn, use them to localize nodes with greater precision. In contrast, range-free algorithms use coarse-grained quantities like connectivity to localize nodes with limited precision. A middle way between these two approaches can be called a partial range-based approach that can utilize the existing received signal strength indicator (RSSI) readings from sensor nodes to improve the already existing coarse-grained localization methods. Another important consideration in WSNs is that a distributed localization algorithm is more computationally feasible as compared to its centralized counterpart. Keeping these two considerations in mind, a distributed localization algorithm is proposed here which falls in the aforementioned partial range-based category. The proposed algorithm called RangeLookup-MDS first creates subregions using connectivity information only. This is followed by the collection of RSSI readings from individual sensor nodes that are used to perform range lookup for inter-node distance estimates in a lookup table. After that, relative localization in every subregion is performed using multidimensional scaling, and then the relative maps are stitched together to create a consistent (but relative) coordinate system. The algorithm also has the capability to compute absolute coordinates in two-dimensional if the stitching step is executed with at least three non-collinear anchor nodes with known locations. Simulation results on uniform as well as irregular networks of various sizes show that the proposed algorithm provides improved localization accuracy and reduces localization error up to 25% in comparison to a previous partial range-based localization algorithm.

3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks

The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques.

Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network.

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Location awareness is very crucial for almost existing sensor network applications. However, using Global Positioning System (GPS) receivers to every node is very expensive. Therefore, anchor based localization techniques are proposed. The lack of anchors in some WSNs lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector- Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multihop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

An Improved PSO Localization Algorithm for UWB Sensor Networks

Traditional particle swarm optimization (PSO) localization method can achieve good localization accuracy with the advantages of fewer parameters and simpler calculations. However, its localization performance is constrained by the limited ranging information available between the anchor node and the unknown node. Thus, an improved PSO localization method is proposed to further improve the localization performance. Firstly, we use the ranging information between the unknown node and the anchor node to make an initial positioning through the traditional PSO method. Then, based on the initial positioning, all ranging information is employed to calculate a more precise location. Compared with the traditional PSO method, the proposed algorithm can reduce the impact of the ranging error and improve localization performance.

An Improved DV-maxHop Localization Algorithm for Wireless Sensor Networks

Over the past few years, location information has contributed to a significant part in various Wireless Sensor Network (WSN) applications. Designing efficient localization algorithms is still a challenging task for many researchers. Our work emphasizes on range-free algorithm because of less cost and better performance in dense and indoor areas as compared to range-based algorithms. Mostly in real world scenario the network is anisotropic due to multiple holes and obstacles. Many recent range-free algorithms for anisotropic networks are complex with additional overhead of computation and assume even distribution of anchors, but it is not possible to deploy anchor nodes uniformly. DV-maxHop is one of the variants of DV-Hop algorithm which is an optimal range free algorithm for both isotropic and anisotropic networks, but still fails to achieve the desired accuracy in presence of non-uniform dissemination of anchor nodes. In context of exploring this limitation of DV-maxHop, we propose a novel strategy known as Improved DV-maxHop algorithm for localization. In our work, we correct the distances by modifying the average one hop of each link between anchor nodes and unknown nodes using a correction method. This correction helps the sensors to localize more accurately according to the distribution of sensor nodes in the network. Simulations has been performed and results indicate significant improvement is achieved with Improved DV-maxHop algorithm, when measuring metrics such as localization error, while varying other simulation parameters such as Anchor ratio and changing topology of anchors as well as sensors. Results prove that our proposed algorithm minimizes the localization error efficiently without requiring any additional hardware or increasing communication overhead.

Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

An improved Cuckoo search localization algorithm for UWB sensor networks

Conventional Cuckoo search (CS) localization method can obtain good positioning results that are highly accurate and robust. However, its positioning performance is constrained by the limited distance information available between the unknown node and the anchor node. In order to further enhance the positioning accuracy of the CS method, an improved CS localization algorithm is proposed that can take advantage of all of the distance information available. In the positioning process, an objective function which contains the distance information among the unknown nodes is given. Firstly, we use this distance information between the anchor node and the unknown node to determine an initial position through the conventional CS method. Then, based on the initial position, all of the distance information available is used to compute a more precise position. The simulation results demonstrate that the proposed algorithm can enhance the positioning accuracy in comparison with the conventional CS algorithm.

A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization.

Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization

In this paper, we proposed an improved DV-Hop localization using runner-root algorithm to overcome the existing disadvantage of traditional DV-Hop. In proposed algorithm, we have introduced a correction factor in order to modify the hop size of the anchor nodes. The proposed algorithm lessens the communication between unknown and anchor nodes by calculating hop size of all anchors at unknown nodes. To show the applicability of proposed algorithm in anisotropic network, radio irregularity model is also considered. Simulations have been carried out on MATLAB, and comparison is made for our proposed algorithm with traditional DV-Hop, genetic algorithm-based DV-Hop and particle swarm optimization-based DV-Hop. Simulation results confirm that our proposed localization algorithm minimizes the localization error, localization error variance and computational time in comparison with existing localization techniques.

Improved DV Hop Localization Algorithm for Wireless Sensor Networks

Considering the emerging applications of Wireless Sensor Network, the localization of sensor nodes is very important in some applications. Hence in this paper, proposed an improved Distance-Vector hop technique to reduce the localization error generated in the classical method of range free DV-hop algorithm. The proposed algorithm targets to minimize the error introduced in the average hop-size value by the series of mathematical corrections like minimum mean squared error method, modifying the average hop-value value calculated between known sensor nodes with respect to error-term calculated by comparing the estimated distance with actual distance or Euclidean value of anchor nodes, by using dynamic coefficient of weight with respect to minimum number of hopes, calculating x and y co-ordinates of unknown by using 2-D-hyperbolic method. The final corrections on the x and y values are done by using some geometrical methods. Simulations and results are executed on three different evolution models of network by varying radius of communication range, anchor node percentage and number of sensor nodes.

An Enhanced AUV- Aided TDoA Localization Algorithm for Underwater Acoustic Sensor Networks

Since localization now has been an essential step in underwater work. The traditional localization algorithm, like time different of arrival (TDoA) has been proposed for its efficiency in terrestrial while it cannot perform perfect because of the unstable propagation in the harsh underwater environment. And autonomous underwater vehicle (AUV) has been used in many circumstances to meet the needs of underwater works. Hence, an enhanced AUV-aided TDoA localization algorithm (EATLA) for underwater acoustic sensor networks (UASNs) is proposed. The AUV dives into the predefined depth from the water surface after acquiring its position coordinates through the GPS, and periodically transmits data packets around the unknown node. After the unknown node receives the data packets and calculates its position, the conditions for the unique result and evaluate the reliability are quantified. Then, to save the energy consumption, a time-delay system is proposed. Compared with traditional localization algorithms, this paper evaluates the performance of EATLA with localization accuracy, coverage and time used in simulations. The obtained results indicate this algorithm achieves relatively higher accuracy with relatively smaller calculations and overcomes some traditional localization drawbacks.

Performance evaluation of range-free localization algorithms for wireless sensor networks

Wireless sensor network (WSN) consists of a number of nodes that are mostly distributed in a random way to monitor or control different phenomena, such as military operations, earthquake monitoring, environmental monitoring, factory automation and security. A sensor network is formed from a large number of tiny, low energy, limited processing capability and low-cost devices called sensor nodes (SNs) that communicate with each other in an ad-hoc fashion. SNs gather and forward data in order to achieve targeted missions. However, the manual configuration for any sensor network is difficult especially when they are distributed in such a harsh environment. The task of determining the exact position of SNs in WSNs is known as localization, which is an important factor in all WSNs applications that deal with monitoring or controlling phenomena. The localization accuracy varies from one application to another; localization techniques are deployed in different applications based on given requirements. Localization techniques are categorized into two groups: range free and range based. In range-free techniques, localization depends on the relationship between nodes and topological information of sensor nodes; however, in the range-based group, it is required to calculate the distance between sensor nodes. The scope of this paper is on the range-free localization techniques only. First, we survey different range-free localization techniques and discuss some of the localization-based applications where the location of the SNs is vital and sensitive. Then, we describe five localization algorithms: Centroid, Amorphous, approximate point in triangle, DV-Hop and DV-HopMax. After that, we simulate these algorithms using MATLAB based on different setups and topologies. Finally, we make a comparative study between aforementioned localization algorithms based on different performance metrics showing their pros and cons such as localization accuracy and energy consumption.