# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol
### CH and PCH Election
#### 3.2.2 PCH Election
In the above network, few candidate nodes were available for updating CHs, and the next-hop routing node and routing lines were not much different. However, excessive CH replacement increased the communication energy cost. 2. The \(\overline{E}\) value of QTGrid was the lowest among those of the protocols, and the difference became more obvious with the increase in the number of nodes in the network. The \(\overline{E}\) value of QTGrid was lower than that of GeoGrid and GCCR because of the use of data fusion technology, which can largely decrease the achieved redundant sensor data and the corresponding energy consumption of redundant data transmission. The \(\overline{E}\) value of QTGrid was lower than that of QTBDC because the use of the new CH selection mechanism and new next-hop selection procedure made the nodes' load more balanced. 3. When query windows had the same size, more nodes were available, and the superiority of QTGrid over the three other protocols in energy saving became more obvious because redundant data increased as the number of nodes increased. QTGrid fused sensing data, which played a crucial role in energy conservation. Although QTBDC and QTGrid used a similar data fusion strategy, QTGrid employed a more energy-efficient next-hop routing node selection method, which made the energy distribution of intra-cluster and inter-cluster communication balanced and energy conservation obvious for large-scale networks. To measure the delay of data transmission, we calculated the average routing hops for the first 1000 rounds of the four protocols that were randomly distributed in the networks with 200, 400, and 800 sensor nodes. The results are shown in Figure 11a-c, respectively. In Figure 11, the average number of routing hops of QTGrid is considerably smaller than that of GeoGrid and almost equal to that of GCCR and QTBDC. GeoGrid used location information to restrict the data propagation area. As the network ran periodically, the optimal routing nodes were more likely to die prematurely. Hence, GeoGrid had to choose the far-distance node as the next-hop routing node, which resulted in the largest average number of routing hops among the four protocols. For these four protocols, the network structure of QTGrid and QTBDC was multi-level, while GCCR and GeoGrid were not. In general, when the query window was 1/64, 9/64 and 16/64 of the entire network, the average number of routing hops of QTGrid and QTBDC were smaller than that of GCCR and GeoGrid. The reason was that when the sink node was not within the query area, QTGrid and QTBDC transmitted the sensing data to the high-level CH (PCH), and this transmission was mostly short-distance path (fewer hops) communication. Next, the data of the entire query area were collected and transmitted to the sink node with long distance path communication at one time. While the transmission of the sensing data, always belonging to long-distance communication, was from CH of each grid to the sink node directly in GCCR and GeoGrid, the resulting average number of routing hops was larger than QTGrid and QTBDC. Particularly, when the query window was 36/64 of the entire network, the sink node was within the query area, which was a certain level PCH in QTGrid and QTBDC. GCCR had a lower average number of routing hops than that of QTBDC and transmitted the data to sink directly in a grid-by-grid manner. Although GCCR adopted two kinds of data routing strategies (multi-hop and combinational routing), its number of average routing hops was slightly higher than that of QTGrid. By adopting the similar cluster partition approach, the improvement of the next hop routing node selection algorithms in QTGrid made the number of average routing hops smaller than that of QTBDC. ## 5 Conclusions

An energy-efficient and spatial query-centric clustering GR protocol called QTGrid was proposed. First, the monitoring area was divided logically into clusters by a quadtree structure, and each grid'slocation was encoded to reduce the memory overhead.