Dynamic Ring Structure Based Target 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.

RSSI quantization and genetic algorithm based localization in wireless sensor networks

In this paper, a distributed algorithm for localization and tracking in wireless sensor network is designed and implemented. Combining the fingerprint approach and subgradient optimization, the proposed algorithm, called golf algorithm, uses different strategies to attain fast movement for tracking a moving object and a fine tune to approach a stationary object, like a drive and a putt on the putting green in the golf. Based on the WLS objective function, a recursive form for both the fingerprint approach and subgradient optimization is derived and can be accomplished by anchor nodes in collaboration. From simulation results, we can see the proposed algorithm has better estimation performance than the conventional decentralized algorithms to localize a stationary or moving target in large wireless sensor networks. In addition, its hardware architecture is designed. The CORDIC operation is employed to compute the vector norm. Parallel processing is used to handle the calculation of fingerprint data. The tracking trajectories of the floating-point program and fixed-point hardware implementation are shown to be quite similar with small finite precision effect, which verifies the feasibility of this hardware solution.

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

Based on the requirements of low energy consumption and high bandwidth efficiency, a primary area of study is how to improve the estimation accuracy and tracking performance for target localization in wireless sensor networks. In this paper, we propose a weighted sign algorithm (WSA) for positioning and tracking in wireless sensor networks. In terms of the reliability of distance estimation, the location estimation of a target is formulated as a weighted least squares (WLS) problem, which is solved based on the WSA in an iterative and decentralized manner. We also propose a cooperative message-passing algorithm for inter-sensor information exchange, adaptively picking the participating sensor nodes, and making the paths for cyclic routing in the network. On each routing path, a target's location estimate is updated on each cycle and continues among the participating sensor nodes that surround the target. To ensure that the WSA-based scheme has a convergence behavior, a convergence analysis is given. Computer simulation results demonstrate that, as compared with previous methods, our proposed scheme has a faster convergence rate, better location accuracy, and improved tracking performance.

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Many applications provided by wireless sensor networks rely heavily on the location information of the monitored targets. Since the number of targets in the region of interest is limited, localization benefits from compressive sensing, sampling number can be greatly reduced. Despite many compressive sensing–based localization methods proposed, existing solutions are based on the assumption that all targets fall on a sampled and fixed grid, performing poorly when there are targets deviating from the grid. To address such a problem, in this article, we propose a dictionary refinement algorithm where the grid is iteratively adjusted to alleviate the deviation. In each iteration, the representation coefficient and the grid parameters are updated in turn. After several iterations, the measurements can be sparsely represented by the representation coefficient which indicates the number and locations of multiple targets. Extensive simulation results show that the proposed dictionary refinement algorithm achieves more accurate counting and localization compared to the state-of-the-art compressive sensing reconstruction algorithms.

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.

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.

To localize mobile target timely and improve the accuracy of the target state estimation in wireless sensor networks, this paper presents a novel mobile target localization algorithm. By using the mean shift algorithm to construct the proposal distribution to make use of the current measurements, and weighting particles according to the virtual hamming distances in the particle filter, this proposed algorithm improves the accuracy of the target state estimation and reduces the necessary number of samples. It has lower computation and communication cost than the conventional particle filter because of more accurate state estimate of samples. Extensive simulation results confirm that this localization scheme outperforms conventional particle filter and its localization accuracy is comparable to the unscented particle filter, but its computation cost is 50% less than that of UPF.

An improved multihop-based localization algorithm for wireless sensor network using learning approach

An effective evaluation criterion is necessary to investigate the performance of a localization algorithm for wireless sensor networks. The Cramer-Rao Bound (CRB) is the lower bound of the localization error and can be applied as a performance evaluation criterion of a localization algorithm. The CRB on estimation accuracy for both range-based and range-free localization is studied. The CRB for range-based localization, which is suitable for different measurement methods, is derived on the basis of Gaussian noise model. For the range-free localization, the condition of CRB and the error distribution of the estimated distance which is computed by the average hop size and hop counter between nodes are analyzed. The result reveals that the condition is satisfied when the node location is calculated by the estimated distances. Subsequently the CRB for range-free localization is acquired. The localization accuracy of three typical algorithms, AHLos, DV-distance and DV-hop, are analyzed by the simulation experiments. The results verify the CRBs for both range-based and range-free localizations.

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

SummaryIndoor localization in wireless sensor networks is a challenging task. Static localization and moving target monitoring are addressed using ensemble learning method and received signal strength indicator. The suggested strategy combines several regression trees to have better performance. This solution has been experimentally evaluated using real measurements in an office room. The performance results have been analyzed through a comparison with learning‐based localization algorithms currently available in the literature. The analysis shows that the adopted solution is simple in term of computation, accurate and robust to environmental variation.