Search

The passive object localization problemPOLPaims to detect the location of the target. This task requires the target does not have any device to receive signal or transfer. The sensor fusion model for localization is more popular, such as the Kalman Filter(KF). In this paper, an novel fusion model based on the KF is used in fusing some parameters, like the RSSI(Receive Signal Strength Index), infrared data and ultrasound data, which are come from the measurements. The proposed fusion model can promote the accuracy of localization, and provides the higher available of localization. The simulation result have proved that the proposed methods is adequate to the passive localization, compared with other traditional methods, the localization accuracy is greatly improved.

In optical character recognition, text strings are extracted from images so that it can be edited, formatted, indexed, searched, or translated. Characters should be grouped into text strings before recognition, but the existing methods cannot group characters accurately. This paper proposes a new approach to group characters into text strings based on the consistency constraints. According to the features of the characters in the topographic maps, three kinds of consistency constraints are proposed, which are the color, size and direction consistency constraint respectively. In the proposed method, due to the introduction of the color consistency constraint, the characters with different colors can be grouped well; and this method can deal with the curved character strings more accurately by the improved direction consistency constraint. The final experimental results show that this method can group the characters more accurately, and lay a good foundation for text recognition.

Based on data matrix, a novel algorithm termed multiple bi-direction optimization (MBDO) is proposed to implement two-dimension (2-D) adaptive beamforming and interference sources localization. The main contribution of MBDO is to process the long weight vector. We decomposed the long weight vector into two short weight vectors. MBDO is used to compute the two short weight vectors by data matrix to finish beamforming. Moreover, the inverse of high dimension correlation matrix is replaced by low dimension data matrix. Theoretical analysis and simulations are provided to demonstrate the significant reduction in computational complexity and the improvement in performance compared with the well-known LCMV beamforming in most cases.

Self-Localization is one of the key technologies in the wireless sensor network (WSN). The traditional self-localization algorithms can provide a reasonable positioning accuracy only in a uniform and dense network, while for a non-uniform network, the performance is not acceptable. In this paper, we presented a novel grid-based linear least squares (LLS) self-localization algorithm. The proposed algorithm uses the grid method to screen the anchors based on the distribution characteristic of a non-uniform network. Furthermore, by taking into consideration the quasi-uniform distribution of anchors in the area we select suitable anchors to assist the localization. Simulation results demonstrate that the proposed algorithm can greatly enhance the localization accuracy of the anonymous nodes and impose less computation burden compared to traditional trilateration and multilateration.

Wireless sensor network commonly used to monitor wild environments with sparse events. In this application, the primary concern is how to maximize the network lifetime. In order to save energy, the intermittent connected wireless sensor network(ICWSN) is formed by putting nodes into sleeping state for a long time. In the paper, we propose a link metric which considers both link quality and residual energy of nodes for delivering packets on wireless links and presents a routing protocol called EBRP based on the link metric in ICWSN. Using data packet retransmission mechanism based on predicting active time slots of nodes solves the problem of data transmission failure due to unreliable links. Simulation shows that the proposed protocol achieves a good balance between reliability and real-time, as well as the maximum network lifetime.

The proliferation of wireless and mobile network has fostered the demand of context aware applications. Location is one of the most basic and significant contexts. An improved DV-HOP localization algorithm based on connectivity of network is proposed in order to decrease the hardware cost and implement in complexity WSN. First of all, by using the weighted least-mean-square error criterion to estimate the network average single hop distance, we can obtain the distance information between any interconnected nodes. Then, the geometric position of the reference nodes affects the localization accuracy; we propose a selective strategy of reference nodes involved in the position calculation process. Finally, based on trusted links to get the location information. The simulation results show that compared with the DV-HOP localization algorithm, the positioning accuracy is improved, and also improves localization performances.

The passive object localization problem (POLP) aims to detect the location of the target. This task requires the target does not have any device to receive signal or transfer. For this reason, the data type used for the passive object localization is limited in WSN. The great challenge is how to use rare data to estimate the location in the large area. In this paper, based on our preliminary work, we propose a novel approach to localize objects which is referred as SLAL(Sensing Link Adaptive Localization) in which we can judge the location attribute by the adaption rule, calculate the target position by the diffraction model and point out the area by the RSSI affected. Compared with the traditional methods, our major contribution is that study the diffraction model and the affected degree of RSSI, ensure the localization accuracy with the link length extending. Experimental results demonstrate that the localization accuracy of SLAL outperforms previous methods. Laid the foundation for improve the accuracy in further.

Due to the contradiction between overwhelming traffic and limited bandwidth, congestion is one of the most challenges in wireless sensor networks (WSNs). In this paper, based on network coding and cross-layer optimization, we propose a reliable transmission control scheme to avoid congestion. Our scheme uses network coding to improve the reliability and robustness of system, and applies cross-layer design for suitable rate assignment. Our simulation results and analysis show that our scheme can achieve good performance in terms of reliability and throughput.