Wireless Sensor Networks (WSNs) are an emerging technology with a wide range of potential applications. A large number of nodes, with sensing and wireless communications capabilities, deployed in an area of interest, build a WSN. Thanks to the advances in MEMS (Micro Electronics Mechanical Systems) it is nowadays possible to realize small and cheap devices, capable of wireless communication. WSNs differ from other wireless technologies because of a set of specific requirements and characteristic features, including for instance node density, energy requirements, and computing capabilities. The Institute of Electrical and Electronics Engineers (IEEE) classify network technologies by such characteristics. Usually the WSNs are limited to 1Mbps of data rate and 1km of wireless coverage. The actual limit of such quantities depends on the adopted technologies and constraints introduced by specific applications. A set of WSNs specifications, dealing with both network operation and node architectures is described in the IEEE Standard 802.15 and 1451 family.[1-2].

An additional parameter is the WSN operational life, which strongly depends on the balance between power consumption and energy storage. In particular, WSNs are characterized by limited power storage, with possible mitigation coming from power harvesting techniques. Nevertheless, energy efficiency is a critical issue, to be pursued both at node and network level. Typical assumptions include considering the radio interface as the main contributor to power consumption. As a consequence, great attention has been given in the literature to protocol optimization, aimed for instance at minimizing the amount of data transmissions throughout the network, and the maximization of node low-power residence time [3-5]. However, designing a sustainable WSN, relying on power harvesting techniques, would require a deeper and careful modeling, due to the limited and non-steady power supply achievable through harvesting techniques, establishing for instance the maximum consented duty cycle for each node. Such a scenario may require modeling and accurately measuring power consumption associated to the activation of other node functional blocks, in addition to the well-known RF interface. To this aim, both simulation techniques and measurement procedures can be found in the literature. Simulation techniques are available both to describe the network behavior and the node behavior, the latter being based on code profiling techniques and on the description of the node as a finite state machine [6]. Measurement procedures are described as well, typically relying on current measurements at the power supply input, assuming a constant supply voltage [7]. This kind of measurement needs to satisfy conflicting constraints, since it requires to accurately measure short phenomena occurring at a low rate. Moreover, in a distributed context, timing information should be provided, since providing spatial-temporal coordinates to energy consumption measurement may help characterizing the network activity and its operational life.