Implementation of a PSO Based Improved 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.

On accuracy of region based localization algorithms for wireless sensor networks

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.

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.

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

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.

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.

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.

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.

Most of the state-of-the-art localization systems assume an idealistic radio propagation model that is far from the reality, and will lead to lower localization accuracy in real wireless sensor networks. In this paper we describe a coarse-grained link state based annulus (LSBA) localization algorithm that takes into account the anisotropic feature of real radio propagation to improve the localization accuracy and adapts to deployment irregularity as well. We compare LSBA with four typical coarse-grained localization algorithms: centroid, APIT, DV-HOP and amorphous in simulated realistic settings, and experimental results show that LSBA achieves the best tradeoff between localization accuracy and convergence speed in networks with moderate number of anchors. Based on our observation, we also make an improvement suggestion on DV-HOP and Amorphous to redefine the concept of neighboring nodes to reflect the radio irregularity, and simulation results show that both improved algorithms see increased localization accuracy.

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

Localization is an essential problem in Wireless Sensor Networks (WSNs). Many localization algorithms have been proposed, but few efforts have been paid on theoretical analysis on the accuracy of these algorithms. Because it is naturally to formalize range-based localization problems as deterministic parameter estimation problems, for range-based localization algorithms Cramér-Rao Lower Bound (CRLB) has been used to lower bound the variance on the estimation of sensor's positions. However, few similar works have been done for range-free localization algorithms. In this paper, based on geometry properties, we theoretically analyze bounds on accuracy for Region-Based Localization (RBL) algorithms which can be classified as one type of range-free localization algorithms. We prove that if in a RBL algorithm, the deployment region R with the area size s is partitioned into k regions (they can be with any shape and any area size), the localization accuracy is bounded below by no matter how the algorithm partitions R. Although the lower bound is not theoretically tight, our simulation results show that the gap between this bound and achievable accuracy is very small. We conjecture a tighter lower bound when k is large enough. We also observe that in order to achieve high localization accuracy, partitioned regions should have nearly the same size. We give three examples with simulation results to show how the results can be used to set the values of the parameters, like k and the corresponding anchor/event number, in a RBL algorithm in order to achieve desired localization accuracy.

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.

Node localization is the basis of a variety of applications as an important technology in wireless sensor networks. This paper studies the existing localization algorithms, and puts forward a sensor node localization algorithm based on particle swarm algorithm, this algorithm uses the fitness value of the particle to adjust the inertia weight value, in order to achieve , and fast and accurate positioning of the target node. The simulation results show that the algorithm is an effective refinement algorithm in nodes localization.

The gradual expansion of the earth's population and the increasing shortage of land resources promote people's exploration of marine resources undoubtedly. Underwater wireless sensor networks (UWSNs) technology has been developed. Its applications involve military field, civil resource detection, scientific research and experiment, etc. The implementation of related applications need to rely on UWSNs node localization, which is one of the key technologies of UWSNs. Accurate node location information is the premise of all applications. Aiming at the low location accuracy and location coverage of existing localization algorithms, a predictive localization algorithm (MGP) based on modified backtracking search optimization (MBSA) and gray wolf optimizer (GWO) for UWSNs is designed in the paper. The whole localization process includes two parts mainly: first floor nodes localization and secondary floor nodes localization. Firstly, MBSA is introduced into the first layer to improve the location accuracy and convergence speed of nodes; Secondly, GWO algorithm is introduced into the secondary layer localization. GWO algorithm optimizes the location information with low estimation accuracy. Finally, the simulation shows that MGP has better localization results than SLMP, MCL-MP and MP-PSO.