Robust connectivity issues in dynamic sensor networks for area surveillance under uncertainty

We consider several classes of problems that deal with optimizing the performance of dynamic sensor networks used for area surveillance, in particular, in the presence of uncertainty. The overall efficiency of a sensor network is addressed from the aspects of minimizing the overall information losses, as well as ensuring that all nodes in a network form a robust connectivity pattern at every time moment, which would enable the sensors to communicate and exchange information in uncertain and adverse environments. The considered problems are solved using mathematical programming techniques that incorporate quantitative risk measures, which allow one to minimize or bound the losses associated with potential risks. The issue of robust connectivity is addressed by imposing explicit restrictions on the shortest path length between all pairs of sensors and on the number of connections for each sensor (i.e., node degrees) in a network. Specific formulations of linear 0-1 optimization problems and the corresponding computational results are presented.