A robust branch-and-cut approach for the minimum-energy symmetric network connectivity problem

This paper considers the minimum-energy symmetric network connectivity problem (MESNC) in wireless sensor networks. The aim of the MESNC is to assign transmission power to each sensor node such that the resulting network, using only bidirectional links, is connected and the total energy consumption is minimized. We first present two new models of this problem and then propose new branch-and-cut algorithms. Based on an existing formulation, we present the first model by introducing additional constraints. These additional constraints allow us to relax certain binary variables to continuous ones and thus to reduce significantly the number of binary variables. Our second model strengthens the first one by adding an exponential number of lifted directed-connectivity constraints. We present two branch-and-cut procedures based on these proposed improvements. The computational results are reported and show that our approaches, using the proposed formulations, can efficiently solve instances with up to 120 nodes, which significantly improve our ability to solve much larger instances in comparison with other exact algorithms in the literature.

► We present two models for the minimum-energy symmetric network connectivity problem.
► In the first model, we relax certain binary variables to continuous ones.
► We strengthen the first one by adding lifted directed-connectivity constraints.
► We present two branch-and-cut approaches using the two proposed models.
► Results show that our approaches outperform other exact algorithms in the literature.