Both computational performances and energy efficiency are required for the development of any mobile or embedded information processing system. The Internet of Things (IoT) is the latest evolution of these systems, paving the way for advancements in ubiquitous computing. In a context in which a large amount of data is often analyzed and processed, it is mandatory to adapt node logic and processing capabilities with respect to the available energy resources. This paper investigates under which conditions a partially reconfigurable hardware accelerator can provide energy saving in complex processing tasks. The paper also presents a useful analysis of how the dynamic partial reconfiguration technique can be used to enable energy efficiency in a generic IoT node that exploits a Field Programmable Gate Array (FPGA) device. Furthermore, this work introduces a hardware infrastructure and new energy metrics tailored for the energy efficiency evaluation of the dynamic partial reconfiguration process in embedded FPGA based devices. Exploiting the ability of reconfiguring circuit portions at runtime, the latest generation of FPGAs can be used to foster a better balance between energy consumption and performance. More specifically, the design methodology for the implemented digital signal processing application was adapted for the ZedBoard. To this aim, a case study of a video filtering system is proposed and analyzed by dynamically loading three different hardware filters from the management software running on a Linux-based device. With more details, the presented analytical framework allows for a direct comparison between the energy efficiency of a dynamic partially reconfigurable device and a static non-reconfigurable one. The estimated timing conditions that allow the dynamic partially reconfigurable process to achieve relevant energy efficiency with respect to the corresponding static architecture are also outlined.