# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 6 Discussion  
Interacting at the node level and further upstream at the network level significantly reduces energy. This section assumes the role of highlighting the added value of the developed HEEPS energy manager. Increasing the reliability of the electrical supply contributes to the good functioning of the sensor nodes and to a better quality of service, and stops the network breaking. This fully supports our strategy. Indeed, a one-off power reduction strategy is insufficient to affect significantly a CPU's power consumption profile (often because of the transition costs). HEEPS crosses over DPM, DVFS, and GEDF to achieve better energy savings.  
The opportunities offered by HEEPS are numerous. It is able to adapt to all types of processors of sensor nodes and to give results closer to reality. It provides a portable solution on several platforms and simulators by changing some components (CPU frequencies, penalties of transition, power-modes, etc.). After identifying, reviewing, and validating a specific scenario, the solution can be used to explore other instances that fall within the same logic without complicated parameterization. Tracking the node's behavior would be a way to create a relevant energetic and performance-assured strategy. Therefore, HEEPS is a highly modular system providing utmost scalability and flexibility.  
Although most power manager are intended for energy harvesting like [42; 63], they remain weak and the trend scenario is not viable despite the advances recorded on energy-gathering components. A disadvantage of Kan-PM proposed by [PERSON] et al. [63], is its low response to the change in energy (unable to take into account the reloading of the storage unit), which induces an error when measuring the available energy.  
DVFS alone is not the best strategy to apply to modern processors, but it could be used with other energy management techniques. The DPM algorithm may malfunction if \(T_{transition}\) it is usually longer than the idle time available. Therefore, in our mixed approach, we have taken these two considerations into account. The techniques of [34; 37] target to prolong the execution time as much as possible. As illustrated in [37], structuring energy management techniques exclusively on two frequencies of voltage/frequency leads to difficult design errors. They cause an imbalance in the energy manager's decisions and the real execution states. This problem was overcome with HEEPS by taking into account discrete frequencies and voltage.  
Table 6 summarizes, for each solution, the corresponding features and metrics. The work of [39] is based on precedence constraints. It results usually in a cumulative delay in execution resulting in deadlines violation. That is why we have privileged tasks with deadlines on request. Although [26] is limited to three power modes, there are five low-power states in our framework. We assume a deep sleep state with a \(T_{transition}\) lower than or equal to the idle interval length.  
Figure 10: Influence of the AET and Task Number on Energy.  
The comparison of our work with the other proposed solutions allowed us to consolidate our contribution and take into account its limits also in our future work. The energy model is implemented in STORM, encapsulating the power consumption of all operating modes of the CPU component and the transitions between those different power modes. In addition, in order to take advantage of HEEPS, we have developed several simulations based in particular on the variation in the number of tasks, the utilization rate and the best and worst execution time ratio to explore more the reliability of the adopted algorithms. Results show that the power manager proposed effectively schedules tasks to utilize the dynamically available energy estimated gain up to 50%.