# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 4 Efficient Management of Harvested Energy
### IWS Energy Consumption Model  
To accurately estimate the node's specification based on the recovered energy, it is essential to consider a node consumption model that incorporates most sources of dissipation. Two main factors are involved in the definition of IWS activities, the network's topology, and the communication protocol. Topology refers to the organization of the network; the main topologies are the star, the mesh, and the cluster. The protocol refers to techniques for accessing the channel shared by the various IWSs in the network to avoid collisions and interference. Most of the current IoT networks are star networks; therefore, each IWS directly transmits the collected data to the BS. In this work, it was assumed that the BS schedules transmission times under the Time Division Multiple Access (TDMA) to avoid collisions. Following the star topology of the IoT networks and of the different blocks that constitute the IWS (see Figure 1), three main sources of dissipation must be considered to estimate the energy requirement of the IWS for \(b\) bits of data. This is the energy related to data acquisition, energy due to processing, and the energy required for data transmission [51]. According to many previous studies [51; 52; 53; 54], and some IWs commercial off-the-shelf components (Cf Table 1), the communication module is the biggest energy consumer in the IWS. For example, in [52], the amounts of energy for data sensing and for data processing of an accelerometer MMA7260Q are respectively estimated to be 0.000268 times and 0.044 times the energy dissipated for data communication. In [54], the energy dissipated for communication is evaluated at 51% of the total energy cost of the IWS in industrial processes for which the sensor must be used for the activation of a pre-actuator (contactor or speed variator, for example). Given these observations, only the energy cost associated with data transmission will be considered in this work. In most studies, the energy consumption estimation is based on short-range communications modules like the CC2520 of Texas Instruments. Recently, thanks to advances in the field of microelectromechanical systems, many long-range communications protocols are increasingly offered to meet the current requirements of IoT applications. Among these protocols, we can mention the Long-Range Wide Area Network (LoRaWAN), the Narrowband IoT (NB-IoT), and Sigfox [55]. From previous work [55; 56; 57], it emerges that LoRa is the most advantageous in terms of lifespan; this technology is then considered to maximize the performance of the IWS in terms of transmission range or maximum data sizes. In LoRa technology, Chirp Spread Spectrum (CSS) modulation is used; it is based on a spectrum spread, allowing the signal to be transmitted over a spectral width greater than all the frequencies compose it. There are three operating modes in LoRa technology called classes A, B, and C [58]; the most energy-efficient is class A in which the sensors go into sleep mode after acknowledging the data. LoRa modulation provides the ability to use multiple orthogonal spreading \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor Metric** & **EWMA** & **PHEV** & **Improvement** \\ \hline Maximum absolute error (mW) & 1.02 & 0.857 & 16\% \\ Relative error (\%) & 25.25 & 21.2 & 16.03\% \\ RMSE (\%) & 28.63 & 19.52 & 31.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of EWMA and PHEV. factors (SF) to improve spectral efficiency and network capacity. Each symbol is made up of SF bits and changes in a BandWidth (BW) window around the central frequency [59]. The transmission time, \(T_{OA}\) (for Time on Air) in ms of a number symbol \(N_{\text{sym}}\) is defined by:  
\[T_{S}\ =\ \frac{2^{SF}}{BW}N_{\text{sym}} \tag{5}\]  
The SF is between 5 and 12, and the bandwidth is expressed in kHz. The value of \(N_{\text{sym}}\) varies depending on the modulation parameters.