# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 2 Related Work
### Server-Side Predictions  
Server-side approaches put the load of computation on a remote server with end-devices primarily utilizing the recommendations from the server. Having adequate processing capacity, sophisticated learning algorithms can be applied to large-scale performance data. Thus, the potential to achieve good results is intrinsically high. Although the computational burden is eased, a certain amount of periodic communication overhead has to be afforded in such settings. Nevertheless, considering the limitations of IoT devices, such an overhead, if controlled properly, can be reasonably justified. Some of the main server-side prediction approaches are presented in Refs. [19; 24; 32]. [PERSON] [19] used packet reception ratio, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR) and Link Quality Indicator (LQI) to predict the probability of delivery of the next packet. Importantly, RSSI, SNR, and LQI are all receiver side values that are used to determine the success probability of next packet delivery at the transmitter. Neural Networks (NN) based Packet Loss Ratio (PLR) prediction results are presented in Ref. [24]. The work mentioned [24] used inter-packet interval, number of nodes, received Packets (rP) and erroneous Packets (errP) as input features. It is interesting to notice that rP and errP are directly used as features for predicting PLR. [PERSON] [32] used neural networks to predict TP level in relation to network Lifetime (LT) and inter-node DT. In order to predict one of these metrics, two others were taken as features in this work [32].  
The client-side approaches are limited to missing value predictions from the matrix of values for the metric under consideration. This limits the quality and applicability of prediction models to the diverse application scenarios and heterogeneous communication settings present in the IoT. It is argued that a model capturing the variations in different communication, circumstantial and application specific variables, affecting the QoS, can better prepare a model for sustainable learning and potentially yielding more accurate predictions. The amount of resources required for this kind of learning cannot be enabled on sensor and IoT devices with limited capabilities. The server-side approaches, on the other hand, do not take into account a wider set of parameters and often use one predicted metric as a feature to predict another metric (e.g., in Ref. [32], while predicting any of LT, \(TP\) or \(DT\), the other two are used as input features to the learning model), or the values directly  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Objective** & **Metrics** & **Inputs** & **Approach** & **Evaluation** & **Computation** & **Dataset** & **Domain** \\ \hline \multirow{6}{*}{This work} & predictable network performance in terms of reliability and energy in relation & \multirow{2}{*}{PDR, EC} & \multirow{2}{*}{LAT,PS, SD,MT, RD,TP,OF} & RMSE & \multirow{2}{*}{Server} & \multirow{2}{*}{public dataset} & \multirow{2}{*}{IoT/ WSN} \\  & ability and energy in relation & & & & & & \\ \cline{1-1} \cline{4-7}  & on to more than 48,000 configurations of stack parameters & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._translating into the metric of interest are used as input features (e.g., in Ref. [24] rP and errP are used to predict PLR). The realization of such a system is far from being practical. In summary, the common limitations of all these efforts are that (i) the datasets used are confined to a few parameters only and did not grasp the diversity of wider parameter configurations; thus, they lacked in capturing the important relationship between configurable parameters and relevant performance metrics, and (ii) did not consider energy consumption.