# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 2 Relate Works
Lastly, HydroCast calculates the normalized advance of each sub-candidate set to choose an optimal sub-candidate to forward a packet. Void-aware pressure routing (VAPR) adds all the neighboring nodes to the candidate set and uses the depth information of these nodes to define the path direction [25]. Moreover, the routing direction information of each node is updated through periodically broadcasting. Channel-Aware Reinforcement learning-based Multi-path Adaptive routing (CARMA) is a learning-based protocol that uses reinforcement learning framework to select an optimal candidate set [26]. The performance of this protocol is closely correlated to the effectiveness and reliability of the estimation of the network information. For protocols in this category, the void problem can be resolved to some extent. However, the nodes in the candidate set are only responsible for forwarding data rather than interacting with the sender. Thus, the networks suffer from the hidden terminal problem. Furthermore, the network performance is constrained with real-time acquisition of network information due to the requirement of periodically updating information of the neighboring nodes. In the hybrid approaches of CSS, each node selects the next-hop forwarder by negotiating with the nodes in the candidate set. According to the feedback from these potential forwarders, a sender chooses a valid next hop by using a candidate selection algorithm. Many protocols in this category are developed based on sender-side-based OR protocols, whereas other protocols are developed based on typical receiver-side-based OR protocols. A protocol called GEDAR takes the same strategy to form a candidate set as exploited in HydroCast [16]. In addition, the node that has received the forwarding packet implicitly sets a priority to the current packet as designed in DBR. Moreover, GEDAR resolves the void problem by adjusting the depth of each potential forwarder. Adaptive hop-by-hop vector-based forwarding (AHIVBF) is developed based on typical VBF by adding the neighborhood table constructed in each node [27]. Moreover, a sender dynamically changes the pipe radius according to the feedback of neighbor nodes to bypass void region. Weighting depth and forwarding area division (WDFAD-DBR), based on DBR, uses the depth information of the two-hop node to participate in candidate set selection and to address the void problem [28]. To accomplish hybrid candidate set selection, WDFAD-DBR introduces some mechanisms to divide the forwarding area and to predict neighbor nodes. By introducing negotiation in this approach, these protocols can avoid the void problem as well as resolve the hidden terminal problem. The hybrid approach of OR protocols introduced above are designed for the dense underwater environment of a 3D region, whereas most of the recent applications require data collection in sparse UWSNs. Typical cross-layer OR protocols for sparse UWSNs are FBR protocol [11] and CARP Protocol [5; 29]. FBR use geographic information to accomplish candidate set selection. FBR divides a candidate set according to the fan section. Furthermore, in case of a sparse network, power control is used to adjust the range of the sector, and then to enlarge the candidate set, enhancing the robustness in harsh underwater environment. FBR can resolve the void problem by ensuring the number of nodes in a candidate set. However, the interference introduced by the power control strategy degrades the transmission performance at high traffic rates. In case of networks with high traffic rates, more packets are buffered at hotspot nodes, resulting in the congestion problem. In contrast, CARP uses hop count information other than position information. In CARP, the neighboring nodes with smaller hop counts are added to a candidate set. Each sender selects an optimal forwarder by considering topology information, link quality, residual energy, and buffer space from the candidate set.