Smart Grid Network Optimization: Data-Quality-Aware Volume Reduction.

The power industry is in the early stages of a fundamental change, driving the integration of energy, communications, and information technologies into one intelligent utility network, known as the smart grid. This paper investigates data traffic management in smart grid networks, in which huge volumes of data produced by advanced meters cannot be fully delivered to utility data centers due to limited bandwidth. To develop a solution for optimizing traffic flow, we exploit a particular characteristic of this network—power-related applications can benefit from different levels of data quality along the path to the final destination. We thus handle congestion by performing intelligent quality-aware volume reduction of the flows within the network. Our optimization problem is that of computing for each flow the amount of volume reductions in different locations, so as to maximize overall revenue. We address both an off-line scenario, in which all flows are known beforehand, and an online framework, in which new flows can be generated during system operation. For the off-line case, we propose an efficient, near-optimal solution, while for the online case, we derive almost tight polylogarithmic upper and lower bounds on the competitive ratio. We also consider a more restricted dynamic setting, for which we demonstrate how to compute an optimal solution. This paper initiates a rigorous treatment of cross-layer traffic management in the smart grid. [ABSTRACT FROM PUBLISHER]