The ever-increasing complexity of applications covered by wireless sensor networks (WSNs) demands for increasing memory size, which in turn increases the power drain. It is well known that SRAM power consumption can be reduced by employing a banked structure, where unused banks are switched into the low leakage retention mode. Although several power management strategies and algorithms for allocating the memory contents to the banks have been proposed, the energy savings limits of these techniques were not completely explored. In this work, we propose a new strategy for memory banking, taking advantage of the software properties intrinsic to WSN, and achieve aggressive power savings. We present a detailed model of the energy saving for uniform banks with two power management schemes: a best-oracle policy and a simple greedy policy. The model gives valuable insight into key factors (coming from the application, the technology, and design decisions) that are critical for reaching the maximum achievable energy saving. Using our model the optimum number of banks can be estimated at design time to reach more aggressive energy savings. The memory content allocation and the power management problem were solved by an integer linear program formulation for two real wireless sensor network applications (based on TinyOS and ContikiOS). Experimental results show memory energy reduction up to 78.3 % for a partition overhead of 1 ,% representing an overall energy saving close to 19 % in data collection WSN applications, including the communication energy and sleep power. The saving would increase to 34 % in more intensive processing applications.