# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 4 Classification of Energy Efficiency Methods
### Mechanisms for RFID Active Tags
#### 4.1.3 Bluetooth and BLE Bluetooth  
Several works highlighted the role of network organization and configuration on Bluetooth energy efficiency. [PERSON] et al., in [99] were among the first to analyze the power consumption of Bluetooth networks, but relevant works also include [96; 97; 98]. As a result, several approaches for energy efficiency in Bluetooth based on network techniques and resource allocation were proposed, as summarized in Figure 7 and described in the following. **Network techniques. ** Similarly to other technologies, Bluetooth devices spend most of the energy during data exchange, while the energy consumption decreases when the device is in hold state. As also analyzed in [125], the creation of energy-aware scatternets, able to monitor and reduce energy consumption, is also very important in Bluetooth networks. This aspect has several requirements:  
* The role of devices in a scatternet should be assigned with respect to their residual energy;
* Short links between devices should be preferred [125];
* The number of piconets in a scatternet should be minimized, in order to preserve scatternet performance. Network techniques based on clustering, in particular have found an important niche in their application to Bluetooth systems. An adaptive clustering algorithm was proposed in [126], making use of fuzzy logic to select the CHs between devices, aiming at creating a network able to flexibly share the power consumption and the traffic load with the CH. In order to evaluate the power consumption, a network composed of \(m\) clusters is considered, where each cluster has \(n\) nodes, including the CH. Between these nodes, there are \(l\) so-called bridge nodes, that is, devices that are part of more than one piconet, with possibly different roles in each piconet, for example, a bridge node can be at the same time a slave and master node in two different piconets. The CH consumes more power than normal nodes, since it is in charge of intra-cluster and inter-cluster communications. For this reason, the proposed method implements an algorithm that generates CH candidates, and uses fuzzy logic to select the CHs, in accordance to the residual energy and traffic load of each candidate. The first phase of the algorithm consists of topology discovery, where each node has to find its 2-hops neighbors. During the following phase, the CH candidates are selected and in the third phase, fuzzy logic is applied to decide a CH. Being an iterative process, CH  
Figure 7: Energy efficiency techniques for Bluetooth [125; 126; 127; 128; 129]. switches can happen, so that a new CH can replace an old one, depending on its residual energy and traffic load. When this happens, the old CH informs all the nodes about the new one, so that they can properly react to this connection change. The method was compared to more traditional methods that do not make use of clusters in a scatternet scenario, and was found to achieve higher energy saving and longer network lifetime, despite the additional power consumption caused by CH switching. Distributed Energy-Aware Multi-hop Tree Scatternet (EMTS) was proposed in [127] for enhancing energy efficiency in multi-hop scatternet networks, by introducing an energy-aware formation process and a reorganization technique aiming at extending scatternet lifetime. EMTS is based on previous work on scatternets, mostly focused on the formation of 1-hop scatternet, where all the devices are within each other's transmission range. In particular, EMTS takes root from:  
* SF-Devil [128], that is a scatternet formation method which shortens the communication links in order to increase the network lifetime. SF-Devil assigns the devices with the highest energy to be masters, but it incurs in high delay formation and does not consider the network diameter;
* ACB-Tree Scatternet Formation (ATSF) [129], that is proposed in order to create energy-efficient, binary tree scatternets (ACB-tree) in 1-hop networks, while ensuring more energy-efficient devices.