Improving Localization in Wireless Sensor Network Using Fixed and Mobile Guide Nodes

Wireless sensor network contains very large number of tiny sensors; some nodes with known position are recognized as guide nodes. Other nodes with unknown position are localized by guide nodes. This article uses the combination of fixed and mobile guide nodes in wireless network localization. So nearly 20% of nodes are fixed guide nodes and three nodes are intended as mobile guide nodes. To evaluate the proficiency, the proposed algorithm has been successfully studied and verified through simulation. Low cost, high accuracy, and low power consumption of nodes and complete coverage are the benefits of this approach and long term in localization is the disadvantage of this method

A Localization Method Avoiding Flip Ambiguities for micro-UAVs with Bounded Distance Measurement Errors

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions; to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://github.com/QingbeiGuo/AFALA.git.Comment: 14 pages, 8 figures, IEEE Transactions on Mobile Computing(Accepted

An Algorithmic Approach to Wireless Sensor Networks Localization Using Rigid Graphs

In this work estimating the position coordinates of Wireless Sensor Network nodes using the concept of rigid graphs is carried out in detail. The range based localization approaches use the distance information measured by the RSSI, which is prone to noise, due to effects of path loss, shadowing, and so forth. In this work, both the distance and the bearing information are used for localization using the trilateration technique. Rigid graph theory is employed to analyze the localizability, that is, whether the nodes of the WSN are uniquely localized. The WSN graph is divided into rigid patches by varying appropriately the communication power range of the WSN nodes and then localizing the patches by trilateration. The main advantage of localizing the network using rigid graph approach is that it overcomes the effect of noisy perturbed distance. Our approach gives a better performance compared to robust quads in terms of percentage of localizable nodes and computational complexity

An Algorithmic Approach to Wireless Sensor Networks Localization Using Rigid Graphs

In this work estimating the position coordinates of Wireless Sensor Network nodes using the concept of rigid graphs is carried out in detail. The range based localization approaches use the distance information measured by the RSSI, which is prone to noise, due to effects of path loss, shadowing, and so forth. In this work, both the distance and the bearing information are used for localization using the trilateration technique. Rigid graph theory is employed to analyze the localizability, that is, whether the nodes of the WSN are uniquely localized. The WSN graph is divided into rigid patches by varying appropriately the communication power range of the WSN nodes and then localizing the patches by trilateration. The main advantage of localizing the network using rigid graph approach is that it overcomes the effect of noisy perturbed distance. Our approach gives a better performance compared to robust quads in terms of percentage of localizable nodes and computational complexity