# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 5 Energy Efficiency Analyses of FiWi Networks Based on Multi-Access Edge Computing
[92] proposed a priority-based offloading model that takes into account offloading and caching optimization that is combined with a local computation policy. The study shows that the proposed model has a substantial impact on decreasing both, the delay and energy usage in a cellular network. Two collaborative computation offloading schemes using the CM-FiWi architecture, namely, an approximation collaborative computation offloading scheme (ACCO) and a game-theoretic collaborative computation offloading scheme (GT-CCO), were studied in ref. [93] (Table 4). The simulation results showed that using both MEC and centralized cloud services resulted in notably better energy efficiency of the network compared with the MEC schemes without centralized cloud offloading. Furthermore, in ref. [94], a generic FiWi architecture with a combination of centralized cloud and distributed MEC for IoT connectivity was presented (Table 4). The problem of collaborative computation offloading for the IoT over FiWi was addressed through the GT-CCO scheme. The numerical results showed that the proposed scheme was energy efficient and able to effectively handle a large number of mobile devices. Due to limitations of mobile UDs, e.g., reduced computing resources, memory capabilities and limited battery capacity, offloading compute-intensive tasks to the MEC server or a remote cloud server emerged as a viable and promising solution for today's computation and delay-sensitive applications [7]. This offloading scheme, called cloud-MEC collaborative computation offloading (CMCCO), takes advantage of both types of cloud services, i.e., centralized remote cloud service and a decentralized MEC service as two complementary technologies. The authors in ref. [7] proposed an energy-aware collaborative computation offloading (EA-CCO) system which can perform various computing tasks in the CM-FiWi network (Table 4). An iterative searching algorithm for collaborative computation offloading scheme (ISA-CCO) was developed in order to decrease task offloading overhead by taking into account residual battery rate, transmit power allocation and the scaling of computing resources. The simulation results showed that the proposed ISA-CCO solution achieved superior results compared with the aforementioned ACCO and GT-CCO paradigms in terms of energy consumption and processing response time of the mobile UDs. Due to the removal of unnecessary data traffic from optical backbone networks, the MEC-enhanced FiWi network presents a viable choice for practical implementation in cases where resource-intensive and delay-sensitive mobile applications will be used. However, the assumption that the FiWi infrastructure offers unlimited and free resources is not realistic and, hence, the energy-efficient offloading techniques must be considered in the realization of MEC-enhanced FiWi networks. Motivated by the need to develop energy-efficient offloading mechanisms for the MEC-enhanced FiWi networks, the authors in ref. [95] presented the two-layer Stackelberg game offloading (TSGO) strategy (Table 4). The FiWi layer and mobile edge computation offloading layer of this strategy are responsible for performing bandwidth allocation and offloading decisions, respectively. In addition, three EE benchmarks were proposed to evaluate the performance of the proposed strategy. The simulation results showed that the TSGO strategy supports the concept of green communications and could effectively decrease the energy usage of the MEC-enhanced FiWi networks.