# Reliable Internet of Things: Challenges and Future Trends
## 5 Review of Recent Work in Literature on Reliable Data Transmission in IoT
### Latency Management
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **R** & **A** & **M** & **P** & **O** & **Key Idea** & **Results** \\ \hline \multirow{2}{*}{NB-IoT [120]} & \multirow{2}{*}{✓} & \multirow{2}{*}{✗} & \multirow{2}{*}{✓} & \multirow{2}{*}{✗} & \multirow{2}{*}{✗} & \multicolumn{4}{c}{Retransmissions impact on energy and latency} \\  & & & & & & Optimal control \& data channel duration selection & Reduced latency \\ \cline{5-7}  & & & & & & Optimal selection of coverage class per user & \\ \hline \multirow{6}{*}{multiple RATS to connect with MEC [121]} & \multirow{6}{*}{✗} & \multirow{6}{*}{✓} & \multirow{6}{*}{✗} & \multirow{6}{*}{✗} & \multicolumn{4}{c}{Optimal task offloading decisions} \\  & & & & & & Amount of task locally computed at MEC server & \\  & & & & & & & Amount of task computed at MEC server & \\  & & & & & & Task splitting between MEC servers & \\  & & & & & & Optimal transmit power selection & \\  & & & & & & Optimal RAT selection for task offloading & \\  & & & & & & Alternate Convex Search (ACS) algorithm & \\ \hline \multirow{3}{*}{Cloud with several VNFs [122]} & \multirow{3}{*}{✗} & \multirow{3}{*}{✓} & \multirow{3}{*}{✗} & \multirow{3}{*}{✓} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} \\  & & & & & & VNFs are mapped to different applications & \\  & & & & & VNFs allocation to reduce latency & \\  & & & & & Regression for predicting application demand & \\  & & & & & Optimal allocation of VNFs as per demand & \\ \cline{5-7}  & & & & & & & Initial offline training followed by online training & \\ \hline \multirow{3}{*}{Terrestrial networks, satellite IoT and MEC [123]} & \multirow{3}{*}{✗} & \multirow{3}{*}{✓} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & Data from IoT to satellites and gateways & \\  & & & & & & Satellites have energy constraints & \\  & & & & & & Data offloaded to gateways for processing & \\  & & & & & & Optimal IoT association with a satellite \& a gateway & \\  & & & & & & Lagrange multiplier and DRL used & \\ \hline \multirow{6}{*}{UAV assisted MEC based IoT [124]} & \multirow{6}{*}{✗} & \multirow{6}{*}{✓} & \multirow{6}{*}{✓} & \multirow{6}{*}{✗} & \multicolumn{4}{c}{UAV facilitates storage and computation} \\  & & & & & & UAV improves network coverage using relaying & \\  & & & & & Problem divided into three subproblems & \\ \cline{5-7}  & & & & & First, IoT node and UAV association & \\ \cline{5-7}  & & & & & & Second, communication resource scheduling & \\ \cline{5-7}  & & & & & & Third, the UAV placement & \\ \hline \multirow{3}{*}{Semi-blind downlink NOMA based IoT [125]} & \multirow{3}{*}{✓} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & \multirow{3}{*}{✗} & Improve SIC technique & Improved signal to noise ratio \\  & & & & & Interference ACA used & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Latency management in IoT: Recent literature review (R = Smart Retransmissions, A = Resource Allocation, M = Multiple Access and Physical Layer Techniques, P = Traffic Prediction, O = Cooperation from other networks such as UAVs). The work in [125] considers a semi-blind Non-Orthogonal Multiple Access (NOMA) based IoT scenario. The proposed technique uses an interference Alignment Independent Component Analysis (AICA) as compared to the traditional Successive Interference Cancellation (SIC) techniqueThe results highlight improved signal-to-noise ratio and reduced latency.