# SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data
## 2 Related Work  
It is expected that the convergence of the DNN and FC paradigms will be fostered by the results stemming from (at least) three on-going research lines, namely [10]: (i) the design of anomaly detection algorithms; (ii) the design and optimization of hierarchically organized multi-tier Fog platforms; and, (iii) the development of \"ad hoc\" algorithms for the supervised distributed training of DNNs. _Anomaly detection algorithms exploiting DNNs_--A very challenging issue when dealing with SM data is represented by the early detection of anomalies. In this regard, the literature offers several contributions, like [11; 12; 13; 14]. Specifically, the work in [11] is focused on a Bayesian approach for the segmentation of data sequences and the identification of anomalies based on an empirical estimate of the recurrent behavior of observed changepoints. Paper [12] provides a comparative analysis of some traditional \"shallow\" machine learning algorithms (like k-NN, MLP and SVM), which were used to classify measurements as regular or anomaly. In this work, well-known batch and online learning algorithms (both supervised and semisupervised) were employed to cope with the anomaly detection problem. Differently from our paper, works in [11; 12] do not exploit deep learning and do not take account of the distribution of the detection algorithm over several Fog nodes. More recently, another line of research is addressed towards deep learning techniques, e.g., see [13; 14] and references therein. Specifically, the work in [13] integrates a DNN with a cyber-physical protocol to identify and mitigate the information corruption in sensor-acquired data. The proposed technique exploits unsupervised training based on Deep Belief Networks (DBN). The authors in [14] propose a real-time detection of anomalies in Smart Grid by using an architecture based on Conditional Deep Belief Networks (CDBNs). This work is also characterized by a model that copes with the limited number of available measurements. Differently from our paper, works in [13; 14] are based on DBNs that exhibit a slow convergence behavior and high computational costs, while we focus on the use of SDAE that retains a good compromise between convergence speed and computational cost. Overall, _unlike_ our paper, works in [11; 12; 13; 14] and references therein do not take account of the accuracy-vs.-energy performance over a distributed and virtualized networked Fog platform, deeply investigated in this paper. _Multi-tier Fog Computing platforms for the support of DNNs_--The general topic of the design of multi-tier FC platforms is receiving large attention, mainly due to the emerging areas of the Mobile Cloud, Pervasive Computing and Edge Computing [15; 16]. For the most part, these contributions focus on the energy-efficient (and, typically, time-constrained) offloading of (parts of) application programs from mobile devices to nearby FC servers through the exploitation of WiFi/Cellular-based communication technologies (see, for example, [17] and references therein for an updated overview of this topic). Up to now, a few papers [18; 19; 20; 21] are devoted, indeed, to the novel area of the integration of DNN models onto FC platforms. In this regard, the authors of [19] developed a proof of concept that aims at deploying DNNs onto Fog nodes for ML-based health prognosis. The underlying driving principle is to perform a suitable search among the candidate Fog nodes, in order to find free nodes with sufficient spare resources to delegate mining tasks. The work in [20] proposes an ecosystem that exploits the synergic cooperation of mobile devices and Fog nodes running DNN models for fast object recognition. In [21], a DNN-based face recognition application is simulated and the obtained performance exhibits reduced mining time when smartphones' photos are processed by proximate Fog nodes as opposed to the remote Cloud.