# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions
#### 4.2.5 Multiple Sinks  
A network consisting of a single sink causes scalability and energy efficiency issues. Author [55] proposed a solution by deploying multiple sinks in the network via virtual root with minimum complexity. In a single sink node, the average number of hop counts increases between sensor nodes and a sink node as the network size increases. This leads to more packet loss, consumption of high energy, and a decrease in sensor nodes' lifetime. Therefore, deployment of multiple sinks provides a solution to all problems as the sensor nodes communicate to the nearest sink, reducing the average number of hops causing increased performance, and consume less power. Furthermore, the concept of virtual sink is proposed in which actual sinks behave as a child of common sink nodes in a wired network. A combination of two techniques is carried out: first in which packets are forwarded to the correct sinks having entries of a sensor node. Second, packet forwarding to all sink nodes in which packets are forwarded to all sinks and to the destination sensor nodes. A combination of both techniques is applied where the sink node sends a packet to the correct destination node having an entry in its routing table. Simulations results show that when nodes are positioned within the 20 m range, 32% packet loss is observed and when the number of hops increases to 3, 50% packet loss is observed.  
Comparison of average ETX to a Preferred Parent of a node is done with the number of hops to the sink. With multiple sinks, the average number of hops gets reduced to more than 50%. Furthermore, packet loss is reduced due to the addition of more sinks, data started flowing in different directions resulting in fewer packet collisions. Similarly, the energy consumption is reduced due to decreases in the average number of hops to transmit a packet.  
Table 2 summarizes the working principle, issues addressed, strengths, and weaknesses of solutions under the category 'ETX-based Solutions'.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Working Principle** & **Issues Addressed** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{FLEA-RPL [60]} & Applies fuzzy logic over metrics such as ETX and RER to select the best route for data transmission. & Delay caused by ETX & Shows maximum delay of 2.9 s for 10 hops as ETX & To perform efficient unicast routing, fuzzy compact to RPL, which has 3.8 s. & To perform efficient unicast routing, fuzzy logic is not suitable as it considers only a few metrics. \\ \hline \multirow{4}{*}{MI-FL [61]} & Used for unicast transmission from a child node to root node in order to minimize delay and suppresses duplicate packets. & Delay caused by ETX & It consumes 8\% less power and provides a 90\% packet delivery ratio with a packet loss rate of 4\%. & Simulations are done just for the static nodes (mobility of nodes is not introduced). \\ \hline \multirow{4}{*}{CA-RPL [63]} & Dynamically adjusts the path selection scheme and balances the network load. & Increase in hand-off delay. & Minimizes the average delay towards DAG root and the packet loss rate reduces to 25\% as compared to RPL. & As the devices are battery-operated, network lifetime measurements are not evaluated. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of ETX-based Solutions.