In the current version, the testbed is mainly used for implementing Localization algorithms based on Received Signal Strength (RSS). The following is a list of currently supported algorithms. The algorithms marked in bold were developed at NSL as a part of this project:

1. Centroid
This is a range-free algorithm and comprises of the localizing node simply computing the centroid (arithmetic mean) of the co-ordinates of the anchors/beacons it can hear.
2. Weighted Centroid
An improvement over the basic centroid method, this algorithm involves a weighted average of the co-ordinates of the anchors. The weights are assigned based on the distance from the anchor, which in turn, is calculated using RSS.
3. Grid Scoring
Liu, Ning, and Du proposed the use of a majority voting protocol in which the network area is partitioned into an evenly-spaced grid. Each node uses RSS based techniques to estimate the distance to each neighboring beacon and constructs a virtual annulus, or ring, around the beacon’s coordinates, where the inner and outer radius of the annulus represent a fixed error bound on the estimated distance. Each grid point receives a score equal to the number of annuli that contain it. The sensor then estimates its location as a point inside the region with maximum score, such as the center-of-mass of the region.
4. Gradient Descent
Kwon et al. proposed the use of a minimum mean-square error (MMSE) estimation for range-dependent localization in WSNs. In a distributed implementation, each sensor node estimates the distances to neighboring nodes and iteratively adjusts its current location using a gradient descent algorithm which converges to the MMSE estimate. The distributed implementation yields location estimates in a virtual coordinate system, which can be mapped into a global coordinate system using the reference coordinates of fixed-location beacon nodes.
5. Weighted Grid Scoring
This is a weighted version of the Grid Scoring algorithm. The anchors/beacons that are farther away are found to give noisier measurements and are hence assigned a lower weight/trust value than nearer nodes. This modification shows significant improvement over the basic version.
6. Weighted Gradient Descent
This is a weighted version of the Gradient Descent algorithm. The anchors that are nearer to the localizing node have a greater effect on the minima calculation than the ones that are farther away.