Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Centroid <s> Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on angle of arrival (AOA) or distance measurements. In this paper, we propose a enhanced centroid localization method using edge weights of adjacent nodes based on TSK fuzzy modeling. In the proposed method, first we find adjacent reference nodes which are connected to the node to be localized, and then develop the fuzzy membership function using genetic algorithms (GAs) for edge weights based on received signal strength indicator (RSSI) information. After calculating edge weights, we employ weighted centroid method to localize the node. Finally, we simulated the proposed method to demonstrate the performance. <s> BIB001 </s> Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Centroid <s> In this paper, we propose a new localization algorithm which can be effectively used in three-dimensional (3D) wireless sensor networks. This scheme needs no additional hardware support and can be implemented in a distributed way. The proposed method can improve the location accuracy with relatively low communication traffic and computing complexity. Simulation results show that the performance of the proposed algorithm is superior to that of the conventional centroid algorithm. <s> BIB002 </s> Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Centroid <s> 1. Introduction 2. Network deployment 3. Localization 4. Time synchronization 5. Wireless characteristics 6. Medium access and sleep scheduling 7. Sleep-based topology control 8. Energy-efficient and robust routing 9. Data-Centric networking 10. Transport reliability and congestion control 11. Conclusions. <s> BIB003
Proximity information gathered globally or locally depending on the size of the network can be used to greater advantage when the density of reference nodes in sufficiently high that there are several reference nodes within the unknown node BIB003 [30]. An unknown node within the reception range of a group of reference nodes can estimate its location with ease. The algorithm implementation contains two core steps. First, all anchors broadcast their position to all nodes in the network within the transmission or reception range. Each unknown node listens for a fixed period of time and collects all beacon signals it receives from various reference points. Secondly, all unknown nodes estimate their location by centroid determination with the proximity information gathered from the anchors nodes. This technique is simple but dependent on anchor nodes, which can introduce some error in location estimation with minimum number of the anchor nodes. To solve the problem of localization error BIB001 proposed an enhanced centroid localization method using edge weight of adjacent reference nodes based on TSK fuzzy modeling. Besides, the conventional centroid localization algorithm only focuses on node self-localization for 2D networks. BIB002 proposed a 3D positioning algorithm for WSN based on linear relationship between rectangular and volume coordinates. This technique also depends heavily on anchor nodes and the proximity of the signal used. It also involves unknown nodes listening to anchor signals for some random time which adds to power usage.