An Improved DV-HOP Localization Algorithm 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.

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.

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.

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.

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.

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.

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.

Node localization is one of the key technologies in wireless sensor networks. Taking into account the reasons for the hardware and cost, DV-Hop algorithm is wildly used. The main DV-Hop localization error is the distance between unknown nodes and anchor nodes. In this paper, the estimation of average hop-length and RSSI value has been applied to reduce the error of measurement, which is the distance between unknown nodes and anchor nodes. Simulation results show that this localization algorithm is effective, which improves the positioning accuracy.

Wireless sensor network localization is an important area that attracted significant research interest. Recently, received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor network for its low-cost. But the radio signal transmission is influenced by the factors of channel attenuation and shielding obstacles, etc. Therefore, the accuracy of RSSI-based localization algorithm is not high. In this paper, a new localization algorithm which is based on the probability distribution model is proposed. Firstly, the anchor node which is nearest to the unknown node is made as the reference node in order to correct the RSSI-based ranging distance. Secondly, the common communication area where the unknown nodes may exist is divided into a number of spatial grids. And then, the confidence degree of each grid is calculated out according to the probability model of radio signal space transmission. Finally, based on the centroids of spatial grids, the coordinates of the unknown nodes are calculated out. Through this algorithm, the localization accuracy is improved greatly compared with conventional RSSI-based localization algorithms, which has been demonstrated by the simulating results.

In order to solve the problem that the DV-Hop localization algorithm has large errors in the wireless sensor network environment, this paper uses the minimum mean square criterion to determine the average hop distance of anchor nodes, and then calculates the mean value of the original average hop distance, which ensures that the improved average hop distance is closer to the real average hop distance of the whole network. The estimated distances between nodes are calculated by using the correction value corresponding to the average jump distance of the anchor node; in the positioning stage, when the anchor node is small, the estimated coordinates of unknown nodes are obtained by the minimum-maximum method; when the number of anchor nodes is large, the coordinates of unknown nodes are calculated by the maximum likelihood estimation method; this not only reduces the amount of calculation, but also the accuracy is more stable. This step is not only suitable for DV-Hop algorithm, but also can be used to estimate the coordinates when the distance between the unknown node and the anchor node is known. However, this improved method is only applicable to the premise that the simulation area is not large, so this improvement has its scope of adaptation, according to the needs of choice. Finally, the unknown node coordinates are iteratively optimized by using the quasi Newton method. Simulation results show that the proposed positioning algorithm has higher accuracy and better stability.

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.

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.

ABSTRACTWireless sensor networks (WSNs) are broadly employed in lots of applications. WSN is comprised of spatially dispersed sovereign devices enabled with sensors to examine some physical phenomenon. The node localization, which is to be responsive of nodes’ location in the system, is a crucial component of numerous WSN functions and applications. Various localization algorithms have been proposed to precisely locate nodes in WSNs. The localization algorithms are normally categorized into range-based and range-free schemes based on range measurements. The range-based methods use exact computation measure (distance or time) among nodes in the network. So range-based methods need some additional hardware for such computation, thus expensive to be used in practice. Because of hardware constraints of sensor network devices, range-free methods are considered cost efficient alternatives. Though, these methods normally contain higher localization error in comparison with range-based methods. DV-Hop is an usual range-free method that use hop-based evaluation. In this article, we have modified DV-Hop method and applied a meta-heuristic (PSO) technique to overcome the positioning error. The algorithm has been implemented in MATLAB R2015a for the validation of results. The performance of implemented algorithm has been analyzed in terms of localization error, error variance, accuracy, and coverage. The results of modified algorithm confirm that our algorithm decreases the localization error and improved the localization accuracy as compared to DV-Hop and improved DV-Hop (IDV-Hop) algorithms.