# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 4 Classification of Energy Efficiency Methods
### Energy Efficiency in LPSANs
#### 4.1.1 RFID  
An RFID tag must operate in an energy-efficient way, in or out the range of a reader. Moreover, an RFID reader has to deal with energy constraints as well, and thus also requires energy-efficient operations. As a result, Sleep/wake-up and Routing techniques have been widely proposed for RFID readers and active/passive tags, as detailed in the following dedicated sections. The surveyed literature is classified under such categories in Figure 5.  
Mechanisms for RFID readers  
In order to minimize the energy consumption and thus increase the energy efficiency of RFID readers, two main techniques have been proposed and used, that is, network techniques based on clustering, and resource allocation techniques based on time-division mechanisms. **Network techniques. ** Most of the network technique proposed for RFID readers are based on the concept of clustering. Clustering is a way to resolve possible congestion issues in a network, arising from the fact that multiple devices may require resources for data transmission in a rather uncoordinated manner. By forming clusters, the devices also select CHs; then, all the devices in a cluster send their information to the CH, which in turn transmits it toward the network, for example, to the base station. This method helps saving energy, since it reduces data congestion and collisions. For RFID, an energy-efficient clustering protocol was presented in [101]. It adopts the Dragonfly algorithm [102], and it is also based on previous works, which aim at solving different issues of RFID systems via clustering. In particular: \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper (s)** & **Technologies** & **Methods** & **Results/Conclusions** \\ \hline \(\left[96\right]\) & BLE, Zigbee & Experiments & BLE has lower power consumption than Zigbee. \\ \hline \(\left[78\right]\) & BLE, Zigbee & Experiments & BLE consumes less energy compared to Zigbee. BLE has a good ratio of energy per transmitted bit. \\ \hline \(\left[97\right]\) & Bluetooth, BLE, Zigbee & Theoretical analysis & Zigbee offers improved energy efficiency compared to Bluetooth. BLE consumes only 10\% of the power consumed by Bluetooth. \\ \hline \(\left[98\right]\) & Zigbee, BLE, Wi-Fi & Simulations & Zigbee and Wi-Fi consume lowest and highest amount of energy for establishing a connection, respectively. Zigbee, BLE, and Wi-Fi suit better transmission of small, medium, and high amount of data, respectively. \\ \hline \(\left[99\right]\) & Bluetooth, Zigbee, Wi-Fi, Ultra Wideband (UWB) & Experiments & Bluetooth and Zigbee consume less energy compared to UWB and Wi-Fi. Bluetooth and Zigbee suit better low data rate applications, while Wi-Fi and UWB offer more efficient solutions for high data rate applications. \\ \hline \end{tabular}
\end{table} 
Table 3: Existing comparative analyses of LPSANs in terms of energy efficiency and consumption. Figure 5: Energy efficiency techniques for RFID [100; 101; 102; 103]. * Pulse and Geometric Distribution Reader Anti-collision (GDRA) algorithms [103; 104]--The aim of this algorithm is to solve collisions between readers, happening when signals from different readers overlap. To deal with collision issues, the Pulse protocol enables the transmission of a beacon signal when the reader is reading a tag, in order to stop other readers from transmitting signals at the same time. GDRA instead minimizes reader collision by using the Sift geometric probability distribution, which minimizes the collision probability among readers, and in turn maximizes the probability that a single reader can transmit. GDRA provides high throughput when used in dense reader environments;
* Distance-based clustering [100]--The RFID systems is composed of the RFID and \(n\) tags, with the reader able to communicate with all the tags that are part of its interrogation zone.