# SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data
## 7 Conclusions and Hints for Future Research  
In this contribution, we focused on the optimized design of SmartFog, a Fog-based networked computing platform for the distributed training of deep auto-encoders for anomaly detection in data streams generated by Smart-Meters (SMs). For this purpose, after defining the main building blocks of the SmartFog platform, we proposed a new _Adaptive_ Elitist GA (i.e., the AEGA) for the energy-efficient distributed training of the implemented SDAE. The performance of the resulting optimized SmartFog platform was numerically tested and compared with the corresponding ones of some state-of-the-art benchmark solutions in terms of accuracy, energy consumption and execution time. Overall, the main insights stemming from the reported numerical results are that: (i) the proposed SmartFog platform reaches an accuracy of 92.6% by using an SDAE with three hidden layers and a  
Figure 3: Average energy consumption of the simulated SmartFog platform of Figure 1 for the training of the 3L-SDAE under the: AEGA with the optimized inter-threshold \(\mathcal{T}_{0}\) (continuous black curve), AEGA with the inter-threshold \(\mathcal{T}_{1}\) (dotted dark green curve), AEGA with the inter-threshold \(\mathcal{T}_{2}\) (dash-dotted magenta curve), NA-EGA (dashed blue curve) and only-Cloud (red diamond) mapping solutions. time window of two hours; and, (ii) the proposed AEGA equipping the SmartFog platform allows us to reduce the convergence time of about 1.6 times with respect to the conventional NA-GA, while guaranteeing a 30% of energy saving with respect to the only-Cloud benchmark solution. Being the integration of the Deep Neural Networks and Fog Computing paradigms in its infancy [10], we believe that the reported results are, indeed, only the tip of the iceberg and, then, they could be further developed along (at least) three main research lines. First, the present paper focuses on the energy-efficient implementation of SDAEs atop Fog platforms. However, SDAEs represent only an instance of the overall family of DNNs [4]. Hence, the generalization of the presented results to DNN models described by general directed acyclic graphs could be a first research line of potential interest. Second, this work focuses on the training phase of the SDAEs, that is typically considered the most cumbersome one from a computational point of view. However, emerging IoT-oriented real-time applications demand for stringent delay requirements during the inference phase [10]. Hence, the design and testing of distributed Fog technological platforms for the energy-saving and real-time inference of big data through DNNs may be a second research line of potential interest. Finally, the presented results rely on the (implicit) assumption that both the volume of data to be processed and the states (that is, the bandwidths) of the communication links of the SmartFog platform of Figure 1 stay unchanged over time intervals at least equal to the time required to carry out the training of the SDAE of Figure 2. As (recently) pointed out, for example, in [23], this assumption is typically met in SmartGrid networks for emerging Green Smart Home applications, in which both SMs and Fog nodes are statically installed in buildings that communicate over fixed power line-based wired links (see, for example, Section 3 of [23] and the reference framework of Figure 2). However, in principle, both the here proposed SDAE-based approach to the anomaly detection and the related AEGA-based resource management solution could be effectively employed even in the emerging field of the so-called Vehicular Fog Computing (VFC) [24; 25]. In this environment, devices on board of parked or slowly moving vehicles are used to form a wireless (possibly, mobile) intermediary Fog layer.