Concentric Anchor Beacon Localization Algorithm for 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.

Localization technology is the basis of practical applications of wireless sensor networks (WSNs). An improved localization algorithm for wireless sensor networks was proposed to reduce hop distance error and solve the premature convergence problem for the hybrid localization algorithm of DV-Hop and Bat (BADV-Hop). Based on the vector routing localization mechanism, an optimization method was designed to enhance the average hop distance accuracy. The linear weight strategy was used to correct searching velocity of bats. In addition, tendentious random disturbance was established to optimize location information. Experimental results showed that the localization accuracy of improved algorithm is 13% greater than that of DV-Hop algorithm and 3% greater than that of BADV-Hop algorithm. Moreover, the improved algorithm effectively reduced the maximum iterations and avoided premature convergence without any additional energy consumption compared to BADV-Hop algorithm.

Wireless Sensor Networks (WSNs) are being deployed in a variety of location-dependent applications, where the measurement of data is meaningless without accurate location. Many range-free algorithms were proposed in order to minimize the extra cost that appears in the ranged-based algorithms. Fuzzy logic provides a simple, fast and cost efficient way that can be used in range-free algorithms. In this paper, we propose a fuzzy-based collaborative localization algorithm for wireless sensor networks. Our proposed algorithm implements a Sugeno-type fuzzy system using a collaborative feedback communication scheme. The proposed algorithm provides more accurate localization estimation without any additional hardware implementation. The simulation results demonstrate the effectiveness of the proposed approach.

Wireless Sensor Networks (WSNs) are being deployed in a variety of location-aware applications, where the measurement of data is meaningless without accurate location. Many localization algorithms have been proposed in the literature. However, localization in threedimensional (3D) space has not been studied sufficiently. Also, artificial neural networks are not commonly used in localization. In this paper, we present a 3D localization algorithm for wireless sensor networks using artificial neural networks. In the proposed scheme, the idea of the 2D localization using neural network is extended to achieve a 3D localization with simplicity, location accuracy, mobility, and low cost. The simulation results depict the performance and the effectiveness of the proposed approach.

In order to improve the location accuracy of unknown nodes in APIT algorithm in mobile wireless sensor networks, an improved APIT algorithm is proposed. A new test method called gravity center combination has been proposed for the InToOut and OutToIn errors in APIT. The algorithm further builds an anchor box by combining one-hop and two-hop information of nodes to limit the range and finally determine the location of unknown nodes to reduce the number of nodes that cannot be located. By improving the coverage and node availability, the positioning accuracy is eventually improved. The complexity is lowered by the lack of requirement of additional hardware, thus the improved algorithm is relatively easy to implement. The simulation results of the verification demonstrate that the updated algorithm improves the coverage ratio and the node usage efficiency compared with the other algorithm, thus makes the mobile node location more accurate.

In many applications of wireless sensor networks, location is very important information. Conventional location information comes from manual setting or GPS device. However, manual location setting requires huge cost of human time, and GPS location setting requires expensive device cost and large device size. Furthermore, GPS can not operate in indoor environment. Both approaches are not applicable to localization task of wireless sensor networks. In this paper, an accurate and efficient localization algorithm based on multidimensional scaling (AIDS) is proposed in hierarchical network environment. Through localized computation of multidimensional scaling within a cluster, the computation overhead can be distributed to each cluster. On the other hand, MDS- based localization approach requires the estimation of multihop distance. Through restricting estimation of multihop distances within a cluster, the proposed localization algorithm achieves better accuracy and can operate in non-convex network environment. Experimental results reveal that the proposed HMDS localization algorithm outperforms MDSMAP algorithm in terms of accuracy.

There are many algorithms demonstrated effectively for wireless sensor network (WSN). Some approaches aim to provide precise position and others demonstrate identification purpose. Even though there is a growing awareness for Zigbee-based sensor networks applications that an integrated coordination of hardware and software is needed for current applications. Especially, the previous research efforts could not clearly establish the impact of protocols or topology management issues. This paper intends to design a more convenient and efficient localization algorithm for Zigbee wireless sensor networks for the paint-ball game. A RSSI-based algorithm (Received Signal Strength Indicator, RSSI) and fixed nodes topology is developed to illustrate the performance of a novel mixed centroid localization algorithm within the paint-ball game scenarios. The experiment data and algorithm actually help the paint-ball game simulation project. The next step for this project is getting beacons motion figure which may expand this algorithm further applications.

In a Wireless Sensor Networks, localization techniques are needed to identify the exact position of an event. In this paper, we proposed Fuzzy Inference System (FIS) based target localization algorithm in a three-dimensional wireless sensor networks. In the proposed scheme, distance between anchor and target nodes are calculated by adding correction factor with hop size of an anchor node. The concept of fuzzy logic-based edge weight calculation is introduced to improve the localization accuracy. Simulation result shows that proposed scheme achieves less localization error and better accuracy as compared with existing localization technique.

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

Localization plays an important role in wireless sensor network management. Currently, the approaches mainly fall into two categories: range-based and range-free. In this paper, the proposed work combines the advantages of the two types of methods (range-based and range-free) to introduce the near and far field characteristic of RSSI(NFC), which is 1) in the near field, there is a relatively stable relationship between RSSI and distance. 2) In the far field, a max range of the signal radiation exists. NFC provides a new method of distance measurement and node positioning. A new localization technique (NFCB) based on NFC is also proposed. In this algorithm, anchor nodes transmit beacons at different power levels, which can divide geographical regions into the rings with multiple near and far field combination. From the information received by each beacon heard, nodes can determine its position, which depends on the property of intersections of near-fields. NFCB can turn the nodes with high-precisions into anchor nodes. Without increasing the hardware, it can raise the density of anchor nodes, which greatly improve positioning accuracy. Simulation results demonstrate that the estimation error is lower than CAB APIT.

Localization is crucial to many applications in wireless sensor networks. This article presents a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo Localization algorithm. We improve the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. Namely, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.

In recent years, the application of wireless sensor networks (WSNs) becomes a growing interest. Positioning technology is increasingly important in wireless sensor networks. The weighted centroid localization algorithm for wireless sensor network positioning equipment provides a quick and easy implementation. The algorithm comes from the average coordinates of the centroid of the adjacent anchor for the determination of the computing device. After analysis of radio propagation path loss model, we select the most appropriate log-distance distribution model for analog signal transmission. Based on the centroid algorithm and weighted centroid algorithm, this paper presents an elliptical centroid localization algorithm. The algorithm uses the characteristics of the oval to estimate the unknown node coordinates. The main idea of the oval center of mass algorithm is to control the accuracy factor (PCF), which can control the positioning accuracy of the algorithm. In oval centroid algorithm, the node is extended to serve as the anchor, to strengthen the anchor density dynamic. The simulation results show that oval centroid algorithm is more effective than the centroid algorithm and weighted centroid precision arithmetics.

According to the classical DV-HOP localization algorithm in wireless sensor networks with low precision, we put forward an improved algorithm,which adopted a new way to calculate the distance between unknown nodes and anchor nodes, then by using the weighted least squares method to calculate nodes coordinates. Matlab experimental simulation results show that under the same network environment, the algorithm can calculate the distance to reduce localization errors effectively, and improve the localization accuracy. Keywords-wireless sensor networks; node localization; DV-HOP algorithm

SummaryRange‐free localization algorithms in wireless sensor networks have been an interesting field for researchers over the past few years. The combining of different requirements such as storage space, computational capacities, communication capabilities, and power efficiency is a challenging aspect of developing a localization algorithm. In this paper, a new range‐free localization algorithm, called PCAL, is proposed using soft computing techniques. The proposed method utilizes hop‐count distances as the data to train and build a neural network. Before feeding the data into the neural network for the purpose of training, the dimensionality of data is reduced by principal component analysis algorithm. The performance of the proposed algorithm is evaluated using simulation. The obtained results show that the proposed algorithm has a better performance in contrast to other algorithms based on storage space, communication overhead, and localization accuracy. Furthermore, the effect of various parameters on the PCAL algorithm is studied.

Wireless sensor networks, by providing an unprecedented way of interacting with the physical environment, have become a hot topic for research over the last few years. As with any new technology, results from real experimentations using these networks are still scarce, as real deployments are either costly, or still unfeasible in the current state of technology. There is therefore an increasing need for simulation tools allowing the testing of different architectures, communication protocols or information processing algorithms in sensor networks. In this paper, we investigate a simulation framework for the testing of data processing in wireless sensor network applications. In a first stage, data is generated using partial differential equations, allowing the modeling of a large panel of physical phenomena. In a second stage, sensing unit operating system and network constraints are simulated using an instance of a versatile simulator to account for the platform characteristics. Insights provided by the proposed simulation frame are illustrated by a set of experiments on a heat source detection task.