A Survey of Localization in Wireless Sensor Network <s> Single-Target/Source Localization in Wireless Binary <s> Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction-of-arrival or time-of-arrival information, are not suitable for low-power sensors with limited computation and communication capabilities. In this paper, we propose a low complexity and energy efficient localization and tracking method for binary sensor networks in noisy environments, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise channels. The proposed method is based on known spatial topology. An efficient wake-up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the localization and tracking mechanism. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> Single-Target/Source Localization in Wireless Binary <s> This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> Single-Target/Source Localization in Wireless Binary <s> This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms. <s> BIB003
Sensor Network. Most of the source localization methods are focused on the measured signal strength; that is, the fusion center knows the measurements of the nodes. In order to obtain the measurements, the node needs the complex calculating process. The above methods require transmission of a large amount of data from sensors which may not be feasible under communication constraints. The binary sensors sense signals (infrared, acoustic, light, etc.) from their vicinity, and they only become active by transmitting a signal if the strength of the sensed signal is above a certain threshold. The binary sensor only makes a binary decision (detection or nondetection) regarding the measurement, and consequently, only its ID needs to be sent to the fusion center when it detects the target, otherwise it remains silent. So the binary sensor is a low-power and bandwidth-efficient solution for wireless sensor network. Limited papers investigate the source localization in binary sensor network. And previous works have been proposed to try to estimate the location of the single source in wireless binary sensor network (WBSN). In  , the authors proposed a maximum likelihood source location estimator in WBSN. A low complexity source localization method BIB001 which is based on the intersection of detection areas of sensors was introduced in noisy binary sensor networks. A subtract on negative add on positive (SNAP) BIB002 algorithm was proposed to identify the source location using the binary sensor networks. This is a fault-tolerant algorithm that is slightly less accurate but it is computationally less demanding in comparison with maximum likelihood estimation. In BIB003 , the authors proposed a trust index based subtract on negative add on positive (TISNAP) method to improve the accuracy of localization for multiple event source localization. This algorithm reduces the impact of faulty nodes on the source localization by decreasing their trust index. And the TISNAP algorithm assumed that the distance between any two sources is far enough; that is, the node is influenced by only one source initially. So the localization process is similar to the single-source localization process. However, all of the previous works mainly focus on single-source localization. Fewer papers investigate the multiple-source localization in WBSN.