# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 5 Future Work
### Exhaustive List of Experimental Parameters  
Table 6 presents various sets of possible parameters for the performance evaluation of, in addition to future research over the applicability of RPL frameworks in real-time evaluation, various RPL extensions both for simulations and experimentations in a mobile environment; the 'X' means that the authors did not mention about these parameters. For example, as shown in Table 6, we note that Co-RPL [43] used Tmote Sky (Tmote Sky is an ultra-low-power IEEE 802.15.4 compliant wireless sensor module. [[https://insense.cs.st-andrews.ac.uk/files/2013/04/tmote-sky-datasheet.pdf](https://insense.cs.st-andrews.ac.uk/files/2013/04/tmote-sky-datasheet.pdf)]([https://insense.cs.st-andrews.ac.uk/files/2013/04/tmote-sky-datasheet.pdf](https://insense.cs.st-andrews.ac.uk/files/2013/04/tmote-sky-datasheet.pdf)) accessed on 1 September 2021) sensor, which is the most commonly used sensor in IoT networks, in addition to other parameters, such as, 10-100 nodes, 1-3 ms node speed, 2.5 s latency, simulation duration of 3600 s, Contiki OS, and a transmission range 50 m; whereas this evaluation does not mention the 'Minimum DIO Interval', 'DIO Interval Doubling', 'Power Consumption', 'Packet Delivery Ratio', and 'Simulation area'. Hence, in order to closely observe the performance of Co-RPL [43], other sensor nodes that support mobility with a variable set of capabilities and an extensive set of parameters for simulation or experimentations could be considered for future evaluation. \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multicolumn{2}{c}{**Categorized Solutions**} & \multicolumn{2}{c}{**Node**} & \multicolumn{3}{c}{**DIO Intervals**} & \multicolumn{2}{c}{**Simulation Parameters**} \\ \hline
**Categories** & **Proposal** & **Node Type** & **Nodes** & **Speed** & **Min Interval** & **Interval Doubling** & **Duration** & **Area** \\ \hline \multirow{3}{*}{Trickle-Time Based} & Co-RPL [43] & Tmote sky & 10–100 & 1–3 m/s & X & X & 3600 s & X \\ \cline{2-7}  & Enhanced Trickle Algorithm [57] & X & 6–72 & 1.25-25 m/s & X & X & X & X \\ \cline{2-7}  & Mobility Enhanced RPL [44] & X & 9 & X & X & X & X \\ \cline{2-7}  & Elastic Trickle Timer [59] & X & 20–80 & X & X & 900 a & 1000 m\({}^{2}\) \\ \hline \multirow{3}{*}{ETX Based} & FLEA-RPL [60] & Tmote sky & 100 & X & 12 s & 10 s & 48 h & 600 m\({}^{2}\) \\ \cline{2-7}  & MI-FL [61] & X & 40 & X & X & 100 s & 500 m\({}^{2}\) \\ \cline{2-7}  & CA-RPL [63] & Tmote sky & 20 & 1–5 m/s & X & X & 360 s & 100 m\({}^{2}\) \\ \cline{2-7}  & Modified RPL [45] & Car & 10 & 25-65 mph & X & X & X & X \\ \cline{2-7}  & Multiple Sinks [55] & Tmote sky & 30 & X & X & X & 3600 s & 20 m\({}^{2}\) \\ \hline \multirow{3}{*}{RSSI Based} & ME-RPL [42] & CC2420 & 25 & 0–2 m/s & X & X & 24 h & 150 m\({}^{2}\) \\ \cline{2-7}  & GTM-RPL [38] & Tmote sky & 12 & X & 8 & 6 & 3600 s & 1600 m\({}^{2}\) \\ \cline{2-7}  & MoMoRo [64] & Tmote sky & 30 & X & X & X & X & X \\ \cline{2-7}  & Enhanced Hand-Off [65] & Tmote sky & X & X & X & X & X & X \\ \cline{2-7}  & mRPL [46] & Tmote sky & X & 2 m/s & X & 0.256 s & X & X \\ \cline{2-7}  & EMA-RPL [66] & X & X & 2 m/s & 180 ms & X & 900 s & X \\ \cline{2-7}  & DMR [41] & X & 100 & 2–10 m/s & X & X & 200 s & 500 m\({}^{2}\) \\ \hline \multirow{3}{*}{Position Based} & KP-RPL [54] & IRS motes & 100 & 2 m/s & X & X & 150 m\({}^{2}\) \\ \cline{2-7}  & MP-RPL [67] & Cooja Tmote & 50 & 1–10 m/s & X & X & 900 s & 600 m\({}^{2}\) \\ \cline{2-7}  & SSCF [56] & Sky mote & 2–8 & X & 4 s & 17.5 min & 600 s & X \\ \cline{2-7}  & Downward Path & \multirow{2}{*}{Tmote sky} & \multirow{2}{*}{37} & \multirow{2}{*}{5–20 km/h} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{1800 s} & 375 m\({}^{2}\) \\ \cline{2-7}  & Construction Mechanism [68] & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Various Solutions in terms of Different Parameters.