Non-convex optimization in connectivity-based sensor network localization

The sensor network localization based on connectivity can be modeled as a non-convex optimization problem. However, current models only consider the convex constraints i.e. connections among the nodes. The proposed method considers not only the connection constraints, but also the disconnection constraints, which are non-convex in nature. It is argued that the connectivity-based localization problem should be represented as an optimization problem with both convex and non-convex constraints. To solve the non-convex optimization problem, this paper analyzes the special characteristics that can be used to decrease the computational complexity in searching algorithm. A “single node treatment” procedure is also designed to jump out of the local minimum. Simulation results have shown that much more accurate solution can be obtained method when compared with convex-constraint methods.