## 2 Routing Protocols in Large-Scale WSNs
### Energy Consumption Mitigation Algorithms
However, the more requirements during route searching, the more complex the algorithm will become. JCOCR is proven to be more energy-efficient than cooperative geographic routing [2] for the following reasons: it exploits power allocation during cooperative forwarding; it optimizes the coalition size to minimize energy consumption; and it chooses the routing path based on global information instead of the local information, which means it chooses the minimum sum of costs along the path instead of minimizing the one-hop cost. JCOCR may easily consider more QoS requirements,  
Figure 6: 4-node cooperative routing graph in JCOCR. but more requirements will add the complexity of the algorithm and limit its applications. Apart from this, the complexity is closely related with nodes density. The coalition size is optimized at individual nodes and the routing graph is visualized and constructed at every single node so that the more neighbors the local node has, the more calculations it has to do for optimizing the coalition size. In addition the geographic information which is desirable for the optimization of coalition size represents an extra cost. _HGMR_. Hierarchical Geographic Multicast Routing (HGMR) for wireless sensor networks was proposed in [37] with the aim of enhancing data forwarding efficiency and increasing the scalability to a large-scale network. HGMR seamlessly incorporates the key design concepts of the Geographic Multicast Routing (GMR) [38] and Hierarchical Rendezvous Point Multicast (HRPM) protocols [39], and optimizes the two routing protocols in the wireless sensor network environment. HGMR starts with a hierarchical decomposition of a multicast group into subgroup of manageable size using HRPM's key concept of mobile geographic hashing. Within each subgroup, HGMR uses GMR's local multicast scheme to forward a data packet along multiple branches of the multicast tree in one transmission. In HGMR, the multicast group is divided into subgroups using the mobile geographic hashing idea: the deployment area is recursively partitioned into \(d^{2}\) equal-sized square sub-domains called cells, where \(d\) is decomposition index depending on the encoding overhead constraints, and each cell consists of a manageably-sized subgroup of members. An Access Point (AP) is responsible for all members in its cell, and APs are managed in turn by a Rendezvous Point (RP). The role of each AP or RP is mapped to some unique geographic location by a simple hash function. The node that is currently closest to that location then serves the role of AP/RP, and routing to the AP/RP is conveniently achieved by geographic routing. To join a hierarchically decomposed multicast group, a node first hashes the multicast group identifier (GID) to obtain the hashed location of the RP via a hashed function and sends a JOIN message to the RP, which is the same as in the flat domain scenario. After receiving the value of the current \(d\) of the hierarchy from the RP, the node utilizes the hash function with \(d\) and the node's location to compute the hashed location of the AP belonging to its cell. Note that computing the hashed location assumes that all nodes know the approximate geographic boundaries of the network. After that the source builds an overly tree, the _Source \(\rightarrow\) APs_ tree, whose the vertices are active APs in a topology graph; and an \(AP\rightarrow\)_Member_s overly tree is also built from the AP, considering each member as the vertex. When a source needs to send data packets, it utilizes the unicast-based forwarding strategy belonging to HRPM to propagate data packets to each AP along the _Source \(\rightarrow\) APs_ tree. In each cell, adjusting the value of \(d\), the number of members for which an AP is responsible does not increase too much. Therefore, GMR's cost over progress optimizing the broadcast algorithm, which is used to select the next relay node at each hop, contributes to reduce the number of data transmissions while maintaining a low encoding overhead compared with the unicast communication.