## 2 Related Work
However, some drawbacks exist in DV-hop algorithm, since localization accuracy depends on the node density. Besides, irregular deployment will cause the inaccuracy of average hop size and communication cost is still high. In order to improve the accuracy of location information, a distributed location estimation scheme (DLS) is proposed in [15]. In this algorithm, each beacon node exchanges the node ID and location information to all nodes of the network. The unknown node calculates its own estimated rectangle (ER) and regards the center of ER as its location. In [5], the authors propose a distributed range-free algorithm, called Concentric Anchor Beacon (CAB) localization algorithm. In CAB, each beacon node emits several beacon packets with different power levels and each node maintains a table that includes the ID, location, transmit power level and constraint region of the beacon node. Each normal node determines the particular ring or circle it belongs to within range of different anchors. From the intersection points of different rings, the average of those intersection points is estimated as the location of a node. Although CAB uses few beacon nodes for localization, it has still some drawbacks. Firstly, it is not a good method for beacon nodes to transmit packets with different power level. Moreover, averaging the intersection point is not accurate result. If some nodes have same intersection points, then the algorithm will give same location information to those nodes. From the discussion of those two kinds of localization schemes, it is clear that each of them have unique properties. In range-based scheme, it can provide more accurate location estimation, but needs additional equipments. In range-freescheme, low cost location system can be built, but estimated location is not accurate enough than range-based schemes. The lognormal shadowing model is used [16] in wireless sensor networks to analyze the path loss characteristics versus distance relationship through a distance power exponent and random shadowing effects. Subsequently the model is used in their work to synthesize the propagation environments. The authors in [17] propose a localization estimation scheme for the wireless sensor networks in Non-Line-of-Sight environments, where appropriate signal strength is not received due to multi-path fading and shadowing. Though they have considered the lognormal shadowing model in their work, they have used four beacon nodes to estimate the location of a normal node without taking the possible localization error into account. The relationship between the Received Signal Strength Indication (RSSI) values and distance in wireless sensor networks is analyzed in [18] taking lognormal model. They use the lognormal shadowing model in wireless sensor networks to estimate the coefficients in the model, which could be dynamically adjusted with the changed environments. In our work, we design algorithms to find location of normal nodes with help of the location of few beacon nodes. We consider the lognormal shadowing model in our protocol as it affects the received signal strength. The detail procedures of our localization methods are described in the following sections.