# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 3 Energy Efficiency Analyses of Radio-and-Fiber Networks
### Power Saving Techniques in the Wireless Domain of FiWi Networks  
Besides the optical domain, different PS techniques have been developed in the wireless domain of FiWi networks. Since the highest consumers of energy in the radio part of the network are wireless network (front-end) devices (e.g., BSs, APs, LPWAN gateways, etc. ), different solutions have been proposed for improving the EE of the wireless segment of FiWi networks. Most of the proposed solutions are based on dynamic control of the activity state of wireless network devices in accordance with traffic variations. Besides ONTs, wireless network devices can also be, at a certain moment, in an active or in a sleep state. Whether it is transmitting or being idle in the active state, wireless network devices consume significantly more energy compared with the sleep (partially or fully turn-off) state [57]. Hence, different PS approaches have been presented in refs. [33; 34; 35; 58] as techniques for wireless network station on- and off-activity adjustments depending on the data load. Moreover, a mechanism called an adaptive power saving mechanism (PSM) was introduced in ref. [36], which, instead of inefficient fixed wake-up intervals, uses an adjustment constant in order to adjust the wake-up intervals of wireless network devices in a more efficient manner (Table 1). Similar to ONU sleep techniques, using PSM and adaptive PSM, the wireless network station needs to buffer the incoming data, which consequently causes additional transmission delay [18]. To address these issues associated with initial PSM, the authors in ref. [37] presented a scheduled access point (AP)-centric PSM protocol based on a time-slicing mechanism. The proposed protocol enables the improvement of energy-efficient scheduling of AP activity at the expense of minimal delay in a multi-traffic environment with heavy background traffic (Table 1). The authors in ref. [38] developed a generic power management model (GPMM), according to which the wake-up scheduling mechanism is controlled by the AP. Additionally, to address the downlink scheduling optimization problem and to achieve more energy savings, in ref. [38] two heuristic algorithms for optimal AP activity adjustments were proposed (Table 1). Furthermore, the authors identified the importance of tuning the length of the beacon interval in order to conserve energy and reduce delay. Since both of the aforementioned scheduling techniques are computationally demanding, the authors in ref. [39] proposed a centralized power-saving mode (C-PSM). In order to reduce latency and increase EE, the C-PSM approach uses traffic pattern statistics to calculate the optimal AP listening intervals, beacon intervals and congestion window size (Table 1). Effective management of WLAN power consumption on smartphones can have a significant impact on energy consumption. The authors in ref. [40] have shown through experiments that the WLAN power management process on various smartphones is autonomous and occurs entirely at the driver level. However, the limitation of driver-level implementations is that essential power management decisions can only be made by monitoring packets at the MAC layer, which disables distinguishing between applications. As a result, each application has an equal chance to consume more energy and determining which applications can impact WLAN power management is crucial. To solve this problem, the authors have introduced a smart adaptive power save mode (SAPSM). SAPSM uses a machine learning (ML) classifier to assign a priority label to each application. Only applications that have high-priority can affect the client's behavior to switch to active mode, while traffic with low-priority is optimized for EE. It is shown that the SAPSM implementation on an Android smartphone device significantly improves EE under typical usage scenarios. As an extension of the SAPSM approach, the authors in ref. [41] have proposed a new classification method of network traffic using ML classifiers to optimize WLAN power saving.