Network localization using tree search algorithm

we consider a network localization problem by modeling this as a unit disk graph where nodes are randomly placed with uniform distribution in an area where connectivity between nodes are defined when the distances fall within a unit range. Under a condition that a number of nodes know their locations (anchor nodes), this paper proposes a heuristic approach to find a realization for the rest of the network by applying a tree search algorithm in a depth-first-search manner utilizing graph properties to speed up the search, aka pruning the search tree by modeling an evaluation function from those properties. The evaluation function is used to select the order of the unknown nodes to iterate to. In [1,2], it is demonstrated that number of connections to previously localized nodes reduces the average feasible iterations within reasonable time. This paper also extends the idea further by accommodating various other properties of graph into the evaluation function. The results show that node degrees, node distances and shortest paths to anchor nodes drastically improve the number of iterations required for realizing feasible localization instance by 40%.