## 1 Introduction  
Wireless Sensor Networks (WSNs) are an emerging technology that has gained great interest from researchers due to the widespread availability of cheap and tiny sensors. WSNs can be defined as a collection of many tiny sensor nodes forming a self-structured network interconnected by a wireless technology. Since sensor nodes suffer from restricted computing resources and battery life time, the development of effective routing protocols for WSNs have been heavily debated after WSNs were introduced [1, 2]. In the view of limited energy and computing resources, routing protocols for WSNs should be differently designed compared to routing protocols for other wireless networks (i.e., MANETs) which mainly focus on data transmission speed or network throughput [3, 4]. In order to address the factors restricting WSNs routing, many routing protocols have been proposed [5]. In [5], authors summarized diverse routing strategies which consider several issues for WSNs routing: in-network processing [6, 7], clustering [8, 9, 10], and data-centric methods [11, 12]. These earlier research studies contributed to diverse domains such as energy efficiency, abstract data access, real time data transmission, etc. Although the diverse approaches have shown their own initiatives in this research area, most studies have the common assumption that routing protocols are run in an ideal network environment without errors. In other words, existing studies did not consider data losses and route failure of the wireless networks due to unpredictable errors such as node failure, contention, interference or fading [13, 14]. Directed Diffusion (DD) [12] is a prominent WSNs routing protocol supporting data-centric routing, but it also assumes an ideal network environment. In DD, a flood of interests representing application requests plays an important role. During interest propagation period for a route setup, the routing information, gradient, is calculated and stored in each node's routing table while an interest floods. After the interest has been flooded, a sensor node with matching sensing data is triggered to deliver the data to a sink node by using its gradients via the intermediate nodes. The problem is that DD is poor at dealing with the network errors after the gradient is calculated once. In DD, if the lossy situation occurs during a data transmission period, the node cannot send data any more to the given path until a new route setup period is restarted. Consequently, control packets should be flooded into the whole network in order to resolve the lossy situation problem, but frequent flooding of control packets causes a sensor node to consume its energy rapidly. Therefore, when WSNs are exposed to a lossy network environment, DD will suffer from low data delivery rates and excessive energy consumption from frequent flooding of control packets. In order to resolve this issue, a more robust routing algorithm which can handle the lossy wireless environment should be considered. The routing algorithm should be resistant to network errors without additional processing and can decide a routing path by capturing the current status of the WSNs. Recently, a concept of cross-layer design has emerged to utilize radio information which reflects the current status of the wireless network. The radio information is considered as an important design factor to provide reliable data transmission services with WSNs [15, 16]. The cross-layer design is a mechanism to enhance the performance of wireless communication at higher layers by utilizing information of lower layers like PHY and MAC layers. There are many researches to use the cross-layer design, but they mainly focused on the cooperation with lower layers (e.g., PHY and MAC) [17, 18]. We expected the improvement of the network performance by using the cross-layer design at the network layer on DD. In this paper, we proposed a Radio-Aware (RA) routing algorithm which can improve the reliability of DD in lossy WSNs.