# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 3 System Model
### The Data
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Acronym** & **Stands for** & **Acronym** & **Stands for** \\ \hline AQS & Actual Queue Size & MRE & Mean Relative Error \\ ARIMA & AutoRegressive Integrated Moving Average & MT & Maximum Transmissions \\ AT & Actual Transmissions & NB & Naive Bayes \\ CSMF & Context Sensitive Matrix Factorization & NMAE & Normalized Mean Absolute Error \\ DL & Deep Learning & NMF & Nor-negative Matrix Factorization \\ DNM & Deep Neural Model & NN & Neural Networks \\ DT & Distance & NoN & Number of Nodes \\ EC & Energy Conumption & OF & Overflow \\ EMF & Extended Matrix Factorization & PCC & Pearson’s Correlation Coefficient \\ errP & Erroneous Packets & PDR & Packet Delivery Ratio \\ GRU & Gated Recurrent Units & PLR & Packet Loss Ratio \\ GS & Grid Size & PMF & Personalized Matrix Factorization \\ HVAC & Heating, Ventilation, Air-Conditioning & PRR & Packet Reception Ratio \\ IAT & Inter Arrival Time & PS & Packet Size \\ IoT & Internet of Things & QoS & Quality of Service \\ IPI & Inter Packet Interval & QS & Queue Size \\ LN\_LMF & Latent Factor Models & R & Pearson’s Coefficient \\ LQI & Link Quality Indicator & RD & Retry Delay \\ LR & Logistic Regression & RMSE & Root Mean Square Error \\ LSTM & Long Short Term Memory & rP & Received Packets \\ LT & Lifetime & RSSI & Received Signal Strength Indicator \\ MAC & Medium Access Control & RT & Response Time \\ MAE & Mean Absolute Error & SNR & Signal-to-Noise Ratio \\ MAPE & Mean Absolute Percentage Error & THP & Throughput \\ MF & Matrix Factorization & TP & Transmission Power \\ ML & Machine Learning & WSNs & Wireless Sensor Networks \\ MPE & Mean Percentage Error & & \\ \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(\alpha\) & Learning rate & \(m_{t}\) & Number of tuples in test data \\ \(A^{[t]}\) & Result of \(g^{[t]}(Z^{[t]})\) & \(PDR_{md}\) & Median of packet delivery ratio \\ \(A^{[t],T}\) & Transpose of \(A^{[t]}\) & \(PDR_{std}\) & Standard deviation of packet delivery ratio \\ \(b^{[t]}\) & Regression constant at layer \(l\) & \(PDR_{T}\) & Ratio of \(Pts_{asked}\) and \(Pts_{stat}\) \\ \(dA^{[t]}\) & Derivative of \(A^{[t]}\) & \(Pkts_{ackd}\) & Number of packets acknowledged \\ \(db^{[t]}\) & Derivative of \(b^{[t]}\) & \(Pkts_{att}\) & Actual number of transmission attempts \\ \(dW^{[t]}\) & Derivative of \(W^{[t]}\) & \(Pkts_{tot}\) & Total packets transmitted \\ \(dZ^{[t]}\) & Derivative of \(Z^{[t]}\) & \(W^{[t]}\) & Weights assigned to inputs at layer \(l\) \\ \(D\) & Payload size (bytes) & Y & Actual outputlabels vector \\ \(err_{k}\) & Difference of \(Y^{actual}_{k}\) and \(Y^{matcl}_{k}\) & \(Y^{actual}_{k}\) & Actual output of \(k^{th}\) tuple \\ \(g^{[t]}\) & Activation function at layer \(l\) & \(Y^{matcl}_{k}\) & Predicted value of \(k^{th}\) tuple \\ \(g^{[t]^{\prime}}\) & Derivative of \(g^{[t]}\) & \(Z^{[t]}\) & Output of layer \(l\) \\ \(H\) & Size of headers (bytes) & \(z_{score}\) & Value 3 deviations away from \(PDR_{md}\) \\ \(m\) & Number of tuples in training data & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of acronyms and symbols used in this text. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Parameters** & **Values** \\ \hline Application & Inter-Arrival Time: IAT (ms) & 10, 15, 20, 25, 30, 35, 40, 50 \\  & Packet Size: PS (bytes) & 20, 35, 50, 65, 80, 95, 110 \\ \hline  & maximum Queue Size: QS & 1, 30, 60 \\ Medium & Actual Queue Size: AQS & actual values (0–60) \\ Access & buffer OverFlow: OF & actual values (0–1) \\ Control & Maximum Transmission: MT & 1, 3, 5 \\  & Actual Transmission attempt: AT & actual values (0–5) \\  & Retry Delay: RD (ms) & 30, 60 \\ \hline \hline \multirow{2}{*}{Physical} & Transmission Power level: TP & 3, 7, 11, 15, 19, 23, 27, 31 \\  & Distance: DT & 10, 15, 20, 25, 30, 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stack parameter configurations.