## 1 Introduction
### Context and Problem Definition  
The above justifies the need for WS to meet Industry 4.0 objectives. However, the issue of energy limitation causes many problems because battery-charging solutions can be expensive (since they very often involve a cessation of production) and can sometimes be impossible when the WS is deployed in places that are difficult to access. To overcome this limitation, a field of research has been expanding in recent years; it is that of ambient energy harvesting [4], which consists of targeting an ambient source (heat, light, vibration, electromagnetic signals, etc.) around the sensor and convert it into appropriate electrical energy. This work is then part of the context of ambient energy harvesting to ensure the energy autonomy of the WS. The vibrations generated by the actuators are the primary energy source considered.  
Note that, unlike battery-powered WS, one of the limitations (regardless of the primary power source) in Energy Harvesting WSs (EH-WS) is the fluctuating nature of harvestable power. In solar energy harvesting, this is due to the alternation of day/night and the change of seasons. Radiofrequency waves, for example, are vulnerable to the propagation channel between the emitting and receiving source, which is often dynamic. The primary energy source considered in this paper is vibration. Most vibration signals will exhibit many fluctuations that depend on the operating regimes of the machines that generate these vibrations [5]. It should be noted that regardless of the industrial process, the engine will be either empty or under full load and even overloaded at a given time. This fluctuating (sometimes random) nature of the harvestable power makes it difficult to define the specifications of the autonomous WS. Yet, it is important to transmit information at regular intervals to every controlled location on the monitored system. This will prevent the loss of information due to a data queue.  
On the other hand, predictive maintenance is also one of the requirements of Industry 4.0, and to effectively control an industrial process, a significant amount of operational data is required. For example, in the same industrial process, accelerometers can be used to measure vibration levels at the reducers, temperature sensors are useful to measure the heating of conductors due to current passages in an engine, pressure sensors can be used during lubrication processes, etc. These massive amounts of data, usually stored in the cloud [6], are not only used for maintenance operations but also for quality improvement. Thus, several sensors are necessary for the control of the entirety of an industrial process. However, much of the work proposed in recent years is only concerned with a single WS's energy autonomy. It is often the one located on the engine, which is where the harvestable amounts of energy are important. Ensuring a single sensor's energy autonomy, as is often the case, would not be enough to guarantee full control of an industrial process. Thus, two issues are addressed in this paper: the _fluctuating nature of the harvestable power_ and that concerning the _efficient management of energy at the level of the Wireless Sensor Network_ (_WSN_) and not of a single WS as in [7].