# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) are data-centric networks, and sensing data have a high time-space correlation [1; 2]. Spatial queries in WSNs are useful for obtaining data concerning events limited to a well-defined geographic region [3]. Routing protocols are responsible for ensuring that spatial query data packets are transmitted from a target region through multiple relay nodes to a destination without being dropped or/and compromised [4; 5]. In most WSN applications, sensor nodes are powered by batteries, which are impossible to recharge or replace after deployment [1]. A node has three basic states, namely, sensing, data processing, and data communication. Transmitting or receiving packets during data communication consumes a considerable amount of energy. Hence, efficiency and energy conservation are key issues in the design of routing protocols. Routing protocols in WSNs can be divided into flat-based, hierarchical-based/clustering, and location-based/geographic routing (GR) depending on the network structure. When the location of nodes and the target area are known, GR uses this information to create routes, thereby consuming less energy than protocols that do not employ location information [6]. Thus, these protocols are usually the best choice for spatial queries [4; 7]. Classical GRs, such as greedy perimeter stateless routing [8], geographic energy-aware routing [9], and graph embedding [10], are attractive approaches for WSNs because no end-to-end route is established before data transmission and network scalability [11]. However, GR still has drawbacks, such as high latency, high collision, and energy hole, and these need to be addressed [12; 13]. The energy-efficient spatial query-centric GR protocol has elicited considerable attention due to the energy constraints of nodes. Flat-based routing [14; 15] protocols are unsuitable for large-scale WSNs because of their single structure and limited extension. Clustering is a popular topology control method for data routing through multi-hop communication [16]. According to whether a cluster is divided evenly or not, clustering protocols can be divided [17; 18; 19; 20] into even clustering (EC) and uneven clustering (UEC). Compared with EC, UEC [21; 22; 23] is unsuitable for spatial queries for the following reasons: (1) the clustering process consumes substantial energy, (2) the unequal size of clusters easily leads to uneven energy consumption of nodes in different clusters, and (3) irregularity in network topology between clusters usually makes routing increasingly complex. Meanwhile, EC divides the entire sensing network into multiple equal-sized clusters, and the energy consumption of intra-cluster nodes communicating with the cluster head (CH) is approximately similar. Therefore, the energy consumption of EC protocols is more balanced than that of UEC protocols, and the network topology is more regularized, which is more conducive for spatial queries. In summary, EC geographic routing protocols are suitable for spatial queries in large-scale WSNs, such as geographic adaptive fidelity (GAF) [24], Grid [25], and GeoGrid [26]. GAF enables nodes in the grid to sleep through a distributed negotiation mechanism, thereby reducing the energy consumption of nodes. However, GAF selects CHs randomly, which inevitably wastes energy and leads to a network that is not uniformly distributed. Grid selects CH according to the degree close to the grid geometry center to improve transmission stability. However, when the number of nodes is small or the network topology changes quickly, network holes are likely to emerge and interrupt the transmission. Based on Grid, GeoGrid uses two geocast forwarding methods that can effectively prolong network life and reduce network traffic. However, its spatial query efficiency is low, and the energy utilization is not efficient enough. In this study, an EC-based GR called quadtree grid (QTGrid) was proposed to achieve energy conservation and spatial query efficiency for large-scale WSNs.