A Survey of Localization in Wireless Sensor Network <s> Node Self-Localization <s> Many applications and protocols in wireless sensor networks need to know the locations of sensor nodes. A low-cost method to localize sensor nodes is to use received signal strength indication (RSSI) ranging technique together with the least-squares trilateration. However, the average localization error of this method is large due to the large ranging error of RSSI ranging technique. To reduce the average localization error, we propose a localization algorithm based on maximum a posteriori. This algorithm uses the Baye's formula to deduce the probability density of each sensor node's distribution in the target region from RSSI values. Then, each sensor node takes the point with the maximum probability density as its estimated location. Through simulation studies, we show that this algorithm outperforms the least-squares trilateration with respect to the average localization error. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> Node Self-Localization <s> An effective indoor localization method of hybrid RSSI/TDOA is proposed to reduce the big errors occurred during indoor RSSI localization and high cost paid by TDOA localization. It measures RSSI with iterative recursive weighted average filter, and polynomial model is obtained to fitting the RSSI measurement and to obtain polynomial model. Furthermore the hybrid RSSI/TDOA localization algorithm is employed. The experiment results show that the proposed method of iterative recursive weighted average filter will improve the accuracy of RSSI localization on the condition of lower computation complexity, and the achieved localization is more accurate in the polynomial fitting than in the log-normal shadowing model. Moreover, the indoor location accuracy in the experiment is approximate 0.5 meter that satisfies the precision requirement of indoors location precision. <s> BIB002
3.1. Range-Based Localization. The classic methods to estimate the indoor location are time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS). TOA method measures travel times of signals between nodes. TDOA method locates by measuring the signals' arrival time difference between anchor nodes and unknown node. It is able to achieve high ranging accuracy, but requires extra hardware and consumes more energy. As an inexpensive approach, RSS has established the mathematical model on the basis of path loss attenuation with distance BIB001 BIB002 , and it requires relatively low configuration and energy. We can obtain the distance between the beacon node and unknown node through the above three measurement methods. We set the position of beacon node is (x 1 , y 1 ), . . . , (x N , y N ), and the position of unknown node is X = [x, y] T . d i is the estimated distance between ith beacon node and unknown node. We can obtain the coordinate matrix of the unknown node as follows: The angles between unknown node and a number of anchor nodes are used in the AOA method to estimate the location. This method needs the antenna array which is an expensive solution for low-cost sensor node.