Near and far field 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.

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.

Due to the un-consideration of random distribution of DV-Hop algorithm in wireless sensor network, a big error caused by using of hop count between unknown and anchor nodes and the nearest anchor nodes’ average hop distance instead of the average hop distance of all the anchor nodes. An improved DV-HOP localization algorithm is proposed based on the traditional DVHOP localization algorithm aimed to reduce the localization error in this paper. The proposed algorithm introduce RSSI technique to modify the Hop count of DV-Hop and use the original DV-HOP algorithm to estimate the positions of anchor nodes which are already known, the error between estimation and real distance of anchor nodes so obtained are then to modify the Hop size. The simulation result shows that the proposed algorithm has a better performance in localization precision.

Wireless sensor network (WSN) is usually deployed in harsh environments for collecting and delivering the data to the remotely located base station. Due to the recent achievements in WSN, tiny and expensive sensors are used with capability of sensing large information and to propagate over longer distances. In range-based localization algorithms, anchor node whose location is known plays an important role. In order to resolve the challenges of traditional localization algorithms, an anchor-based node localization algorithm is proposed for WSN. The distance between anchor or known node and unknown node is measured by optimizing anchors and creating database of optimized anchors. Using trilateration method unknown nodes are located and are used as new anchors which will reduce localization algorithm’s dependency on anchors. The objective of the proposed work is to maximize the life time of the network by minimizing the energy consumption. From the simulation results, we show that the proposed algorithm increases lifetime of wireless sensor network.

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

Abstract—Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been recently proposed, most of them require nodes to be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor beacon (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes can determine in which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. We also propose two heuristics, namely CAB with Equal Area and CAB with Equal Width, to determine the transmitting power levels of the beacons. Simulation results show that the estimation error is reduced by half when anchors transmit beacons at two different power levels instead of at a single power level. CAB also gives a lower estimation error than some other range-free localization schemes (e.g., Centroid and Approximated Point-In-Triangulation) when the anchor-to-node range ratio is less than 4. Index Terms—Localization, position estimation, wireless sensor networks. I.

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.

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-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.

The TDOA localization algorithm for wireless sensor networks can help us measure the time differences of an unknown node to more anchor nodes, which establish nonlinear equation of the differential distance between unknown node and anchor node. Newton iteration is the crucial method for nonlinear equation numerical solutions, so using the Newton iterative method can better achieve nonlinear optimization in TDOA location. The simulation results indicate that the algorithm can meet the requirement of localization and possesses the preferable localization precision.

This paper presents a three-dimension localization algorithm for wireless sensor network (WSN) nodes based on support vector machine (SVM). First, distances between nodes to all anchor nodes in sensor network are estimated. Distance vectors are constructed in each node. Distance vectors in the anchor nodes are considered as training feature vectors for SVM and that in unknown nodes are regarded as testing feature vectors. The trained SVM is applied to class the distance vectors and locate cubic units for unknown nodes. Then, the unit centroids are used as the estimated locations of nodes. The experimental results indicate the algorithm proposed has small localization error and is insusceptible to the proportion of the anchor nodes and range error. Hence, the algorithm proposed is robust and stable, which can be applied in network environment where anchor nodes are sparse and range error is large.

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 this paper, we consider how to localize individual nodes in a wireless sensor network when some subset of the network nodes can be in motion at any given time. For situations in which it is not practical or cost-efficient to use GPS or anchor nodes, this paper proposes an Anchor-Free Mobile Geographic Distributed Localization (MGDL) algorithm for wireless sensor networks. Taking advantage of the accelerometers that are present in standard motes, MGDL estimates the distance moved by each node. If this distance is beyond a threshold, then this node will trigger a series of mobile localization procedures to recalculate and update its location in the node itself. Such procedures will be stopped when the node stops moving. Data collected using Tmote Invent nodes (Moteiv Inc.) and simulations show that the proposed detection method can efficiently detect the movement, and that the localization is accurate and the communication is efficient in different static and mobile contexts.

Recently, network localization systems that are based on inter-node ranges have received significant attention. Geographic routing has been considered an application which can utilize the location information from these localization systems. In this paper, we firstly recognize that sensor network localization algorithms generate positioning data with different error patterns compared to those networks where node positions are determined directly from GNSS measurements. Secondly, by simulating practical sensor network scenarios using data from our localization algorithm, we observe that existing geographic routing algorithms in wireless sensor networks (WSNs) adopt very simplistic methods in the treatment of position error, without due consideration of error distribution. Additionally, an insight is given into localization algorithms for WSNs with inhomogeneous error environments. Our observations represent an initial step toward a detailed understanding and design of efficient geographic routing algorithms in location aware WSNs.