A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> This paper discusses a new localized resource manager for a wireless sensor network of bearings-only sensors. Specifically, each node uses knowledge of the target under surveillance to determine whether it should actively collect measurements and how far to disseminate the data in order for the sensor network to maintain track of the target. At each node, the resource manager requires only knowledge of the relative location to the target for itself and the active nodes from the previous snapshot. The decentralized strategy represents a modification to the global node selection (GNS) method that exploits knowledge of the location of all nodes in the network. Simulations show that despite the lack of global network knowledge, the new localized management method is almost as effective as GNS in terms of balancing the tradeoff between energy usage and localization accuracy. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> This paper discusses a new localized resource manager for a wireless sensor network of bearings-only sensors. Specifically, each node uses knowledge of the target under surveillance to determine whether it should actively collect measurements and how far to disseminate the data in order for the sensor network to maintain track of the target. At each node, the resource manager requires only knowledge of the relative location to the target for itself and the active nodes from the previous snapshot. The decentralized strategy represents a modification to the global node selection (GNS) method that exploits knowledge of the location of all nodes in the network. Simulations show that despite the lack of global network knowledge, the new localized management method is almost as effective as GNS in terms of balancing the tradeoff between energy usage and localization accuracy. <s> BIB003 </s> A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> In this paper, we develop an energy-efficient distributed estimation method that can be used in applications such as the estimation of a diffusive source and the localization and tracking of an acoustic target in wireless sensor networks. We first propose a statistical measurement model in which we separate the linear and nonlinear parameters. This modeling strategy reduce the processing complexity. We then study the distributed implementation of the Gauss-Newton method in the maximum likelihood estimation. After that we propose a fully distributed estimation approach based on an incremental realization of the Gauss-Newton method. We derive three modifications of the basic algorithm to improve the distributed processing performance while still considering the energy restriction. We implement the idea of information-driven collaborative signal processing and provide a sensor-node scheduling scheme in which the Cramer-Rao bound (CRB) is used as the performance and information utility measure to select the next sensor node. Numerical examples are used to study the performance of the distributed estimation, and we show that of the methods considered here, the proposed multiple iteration Kalman filtering method has the most advantages for wireless sensor networks. <s> BIB004 </s> A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> In this paper, we present a cooperative localization algorithm for wireless sensor networks. We adopt an RSS-based localization algorithm due to its lower complexity with respect to other ranging techniques such as TOA or AOA. Since the energy consumption is a key aspect in sensor networks, we propose a node selection algorithm in order to avoid the use of all the nodes in the network for positioning purposes. Thus, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. In particular, nodes in the network select the cooperating nodes by simply comparing their received RSS with a threshold. We present results based on both computer simulations and experimentation with the Crossbow IRIS motes showing that the proposed scheme offers a good trade-off in terms of position accuracy vs. energy consumption. <s> BIB005 </s> A Survey of Localization in Wireless Sensor Network <s> Node Selection Criteria for Localization in Energy- <s> RSS-based localization is considered a low-complexity algorithm with respect to other range techniques such as TOA or AOA. The accuracy of RSS methods depends on the suitability of the propagation models used for the actual propagation conditions. In indoor environments, in particular, it is very difficult to obtain a good propagation model. For that reason, we present a cooperative location algorithm that dynamically estimates the path loss exponent by using RSS measurements. Since the energy consumption is a key point in sensor networks, we propose a node selection in order to avoid the use of all the nodes in the network for positioning purposes. Hence, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. We present results based on both computer simulations and experimentation with the Crossbow IRIS motes showing that the proposed scheme offers a good performance in terms of position accuracy. <s> BIB006
Constrained Network. Due to the limited power of sensor node and hostile deployment environment, the node selection in WSN is different from the traditional node selection in traditional wireless network. If all the sensor nodes are used at the same time to executive localization task without selection, although the energy consumption of nodes selection are saved, but at this time the repeatability of the received information would be quite larger. If the nodes are random selected to executive the localization task, the algorithm is simple and the extra overhead can be ignored, but localization accuracy is low in this case. Obviously, this method cannot satisfy the user's requirements for the accurate localization, the unbalance of energy consumption will appear, and some nodes may fail due to the depletion of energy. This may affect the network connectivity and may result in losing the sensed data. These characteristics of WSN determine selection method which is different from traditional network. Therefore, it is necessary to investigate nodes' selection mechanism in WSN. The primary algorithm makes decision with global information BIB002 ; this method minimized the expected filtered mean-squared position error for a given number of active nodes by using a global knowledge of all node locations. This algorithm needs the positions of nodes and broadcasts them to all nodes and a lot of data communication; therefore, it only can be applied to small networks. Based on the former algorithm, a local selection strategy is investigated BIB003 . This method determines whether or not that node should be active by only incorporating geometrical knowledge of itself and the active set of nodes from the previous information. Based on this approach, the researchers have also investigated other strategies, such as the least square method, Bayes probability method BIB005  . Furthermore, in order to narrow the scope and scale of selected nodes, researchers proposed a method which combines the track and the current state of the robot. Zhang and Cao BIB001 proposed a multinode cooperation dynamic tree algorithm. This method ensured that spanning tree has low energy consumption and high information content by increasing and decreasing the number of the nodes dynamically. But this method still had some disadvantages: the root node needs data fusion and the new node needs to be calculated, and the consumption of energy is quite higher. Yang et al.  proposed online prediction based on particle filter and estimate the probability distribution of the target state under the Bayes framework. This method realized the optimal selection of the node sequence and introduced a shortest path algorithm to reduce the information transmission. Hamouda and Phillips  proposed a method which employs the moving speed of the mobile robot to improve the localization accuracy and consistency. The signal shielding and multipath interference make the channel parameters become too complex to definite error factor. Bel et al. BIB006 proposed two selection principles to reduce the number of active nodes, and the nodes with accurate measured value (RSSI value larger than specified threshold) are selected. This method is effective to balance the accuracy and energy consumption and is suitable for the WSN which is hardware resource constrained. Zhao and Nehorai BIB004 used the Cramer-Rao equation to select the next node to participate in positioning. Because of the complexity of the observed model and the non-Gaussian noise, it is hard to get the optimal solution of the problem.