# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 2 Related Work
### Spatial Query Efficiency  
A geographic routing protocol based on clustering for WSNs (GRCS) [40] was proposed to enhance energy conservation and the degree of success to reach the destination. In GRCS, the network is organized into a set of clusters in which a CH is periodically selected for each cluster. An improved clustering mechanism is employed in the routing process to optimize the path to the destination dynamically. Furthermore, GRCS combines three routing strategies for data forwarding. However,CHs are selected based on the distance from the cluster to the destination, which may cause an energy hole.  
Grid-based enabled geographic routing (GEGR) [41] was proposed to reduce the energy cost in uniformly deployed dense WSNs. Clusters are set up depending on the construction of a 2D logical grid in the geographical region. GEGR uses the CH in each grid to route data. This scheme limits the use of broadcasting in the WSN to the process of CH election and the process of constructing and maintaining the table of neighboring CHs in adjacent grid cells. However, in GECR, the load balance of sensor nodes and CH failures are not considered, and the scheme is unsuitable for large-scale WSNs.  
Markov chain model-based optimal cluster heads (MOCHs) [42] use a simple strategy to select the optimal number of CHs and overcome the problem of uneven energy distribution in the network. In MOCHs, the base station (BS) controls the number of CHs, and the CHs control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured. However, the partition of clusters is irregular, which is not conducive for spatial queries, and the Markov chain algorithm is complex.  
The quadtree-based data collection structure (QTBDC) [43] is a logical hierarchical cluster structure based on the quadtree structure. All sensor nodes are encoded, and a logical multi-level cluster is constructed. A lossless data monitoring mechanism is used to reduce the communication cost. Simulations have shown that QTBDC prolongs the life cycle of WSNs, and the monitoring mechanism may result in a massive reduction in data traffic. However, the node closest to the physical center of the grid easily causes the premature death of several other nodes, and the next-hop node selection approach may easily lead to an energy hole.  
Another study proposed the grid-based clustering and combinational routing (GCCR) [20] algorithm based on the grid structure to improve the network lifetime and scalability of large-scale WSNs. A suitable grid size is calculated according to the size of the area and transmission range, and a virtual grid structure is constructed. A CH is selected in each grid on the basis of the nearest distance to the midpoint of the grid. A localized single-path strategy is employed to forward data within a grid. An angular inclination-based combinational routing model is implemented to forward aggregate data from the CH to the sink. However, the calculation of angular inclination increases computational complexity, and the scheduling of packets is disregarded.  
A comparison of clustering GR protocols is shown in Table 1. We conclude that EEGBR, GRCS, and GEGR are unsuitable for large-scale network requirements. GeoGrid performs well in energy saving but poorly in load balancing and spatial query efficiency. The load balancing of QTBDC and the spatial query efficiency of GCCR should be improved.