  Energy neutral operation of WSNs can be achieved by exploiting the idleness
of the workload to bring the average power consumption of each node below the
harvesting power available. This paper proposes a combination of
state-of-the-art low-power design techniques to minimize the local and global
impact of the two main activities of each node: sampling and communication.
Dynamic power management is adopted to exploit low-power modes during idle
periods, while asynchronous wake-up and prediction-based data collection are
used to opportunistically activate hardware components and network nodes only
when they are strictly required. Furthermore, the concept of "model-based
sensing" is introduced to push prediction-based data collection techniques as
close as possible to the sensing elements. The results achieved on
representative real-world WSN case studies show that the combined benefits of
the design techniques adopted is more than linear, providing an overall power
reduction of more than 3 orders of magnitude.
