# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 2 Related Work
### Energy-Aware Routing  
Energy assumption is the key issue for a wireless sense network. Reference [6] focuses on minimizing the average energy consumption. The metric ETX is taken into account and energy-efficient routes are constructed based on the link reliability. A small number of nodes with a high ETX or close to the border routers may have to forward most of the traffic. As shown in Figure 1, a DAG is built on ETX. \(D\) can select \(C\) or \(B\) as the next hop. \(C\) should be preferred because it provides the lowest cumulative ETX to the sink. However, if all nodes produce the same amount of traffic, \(B\) should be the best choice to balance energy consumption. In [19], the authors divided the traffic into fixed rate and random rate, and transferred the routing problem into a linear planning problem in order to maximize network lifetime. It supposes that all nodes are fixed and the topology changes are slow. Furthermore, it only presents a centralized algorithm and could not support distribution situations. Reference [20] divides energy sources into three kinds--mains-powered, primary batteries and energy scavengers--and presents a calculation standard for every means of power supply. By dynamically adjusting the transmission power of the sensor, the time-insensitive packet is transmitted at a lower power to reduce the energy consumption, and real-time packets are transmitted at a high power. Although this solution is energy efficient for a single node, it does not provide a global solution and does not maximize the network lifetime.  
A novel wave routing framework is proposed in [21]. It supports a multi-source, multi-sink node routing scheme for wireless sensor networks. It adopts a distributed, scalable, class potential, field evaluation algorithm. This method constructs multiple paths and balances the load according to the reciprocal of the cumulative path cost. The authors use residual energy as a routing metric to optimize energy consumption. However, this method can only reduce the path cumulative energy, and does not focus on heavy load nodes. In addition, the concept of this potential field is similar to the rank of RPL. The application of multi-path construction has some limitations. In [22], the authors analyze the reliability measurement and energy measurement. They think that if only the reliability measurement is considered, the distribution of energy between the nodes will not be balanced; if only the energy measurement is considered, some nodes will suffer a higher rate of package loss. They put forward a solution for the linear combination of the nodes' residual energy and ETX. However, this weight is not directly related to the real lifetime of a node. In [23], three RPL multipath schemes are proposed, namely, FLR (fast local repair), ELB (energy load balancing), and their combination FLR-ELB. Then authors apply them to the IPv6 (Internet Protocol Version 6) communication stack for the internet of things. In [24], the authors propose a neighbor node disjoint multipath (NDM) solution, which is proved more efficient when the intermediate node or link fails. In [25], the authors propose cooperative RPL, which creates different instances based on different sensing tasks. Nodes in different instances are responsible for forwarding different data. In this way, energy consumption is reduced compared to standard RPL.  
Figure 2 constructs DAG based on the residual energy. F chooses E or C as a next hop. Even though the link quality of E is very low (ETX = 3), it will be chosen as the parent owing to its residual energy being greater. This will increase the number of data package retransmissions of F. According to the figure, C is a more appropriate choice.