## 2 Related Works and Motivation
### The Motivation  
Although DD was proven to have several advantages such as providing a robust, scalable and energy efficient routing scheme, it has several limitations. Firstly, the protocol overhead should be reduced to minimize flooding of control packets. Although the control packets are essential to set up a route from route failure or data loss, increasing flooding packets causes excessive energy depletion of sensor nodes. Secondly, other considerable metrics for route decision had to be applied to support various QoS such as data rate, energy, reliability, or latency. However, there is little research to change the gradient to other metrics as well as DD. That is, the data rate is still the only metric used to set up gradients in DD. Thirdly, DD cannot deal with lossy WSNs resulted from the unstable network, because a single session of DD is separated into interest propagation and data aggregation periods. If the lossy situation occurs in the data aggregation period, a node cannot send sensing data along the pre-decided route because DD cannot make a new decision to recover the lossy situation on the route in the data propagation period promptly. Finally, link asymmetry may be a considerable issue in wireless networks. A supplement is needed to remedy the asymmetry of a routing protocol, because the data path between nodes and a sink is determined by interest propagation from a sink to sensor nodes in DD. A routing protocol for lossy WSNs has to address the asymmetry characteristics to provide link symmetry. In order to resolve these issues of DD, two kinds of approaches has been introduced. Firstly, some studies focus on prolonging the network life time using energy awareness. In [19, 20], the authors used the concepts of clustering to extend the network's life time. These concepts contributed to reducing the energy consumption by sending interest between the cluster heads instead of interest flooding to the whole networks. In [21], the maximum gradient depth and minimum remaining energy were used to select the next hop. The average network lifetime was extended and the load balance was also improved in this scheme. Secondly, there are research supporting QoS that provide different data transmission strategies by considering different types of data traffic such as real time traffic and ordinary data traffic. In [22], the authors pointed out that DD cannot support time sensitive data traffic and a load-balanced routing scheme. They designed a Real Time (RT) filter and a Best Effort (BE) filter to support corresponding QoS. In this approach, the RT filter showed the shorter end-to-end delay and the BE filter achieved better energy efficiency. Generally, real WSNs should be assumed as unstable due to the nature of wireless communication. The instability of WSNs can be defined as lossy WSNs including many reasons: contention, interference, node mobility, fading by object, or node failure. Lossy WSNs are our error model to emulate real sensor networks having those unpredictable errors. The unpredictable data losses and route failures may occur in lossy WSNs. This random failure can be also defined as the lossy situation in our assumption. The earlier studies tried to overcome the problems of DD and contributed to extending network lifetimes by considering energy, load balancing, or QoS. However, the previous studies cannot provide the reliable data transmission in lossy WSNs, because they assumed that the network is stable. Furthermore, the previous studies are lack of methods to complement link asymmetry of the wireless networks. In the previous studies, they carry over the supplement of link symmetry to the detecting and filtering function of MAC layer [12]. When the network topology becomes unstable, the lossy situation of DD will increase and DD needs to flood more control packets across the network to adjust the error conditions.