# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 5 Simulation Results
### Costs on Data Gathering  
To further verify the performance of LDGM on total length of data transmission path as well as network energy consumption, we compared it to MST (a shortest path based data gathering method) and MWST (a type of backbone based data collection strategy with the help of randomly moving Sink). Figure 29 shows the total length of Path for Data Transmission (PDT) during one round of data gathering time. It is known that the length of PDT in MST is the longest. Because each node in the network needs to do a continuous sensing task and upload data directly to Sink. Thus, MST performs well if and only if there are not too many nodes in the network. When the density of nodes is larger, the length of PDT obviously increases. MWST is another efficient data gathering method by finding out the shortest uploading path. With the help of the mobile Sink, not all nodes need to transmit data to the receiver directly. Thus, the length of PDT in MWST is shorter than that in MST. However, the mobile Sink moves randomly along the backbone path. Other nodes need to upload data to Sink by single-hop or multi-hop manner. As shown in Figure 30, rectangles with different colors are the mobile Sinks. Each Sink needs to visit the nodes that have the same color as itself. So its length is still much longer than that of LDGM. Figure 30: Data collection path of MWST. Figure 29: Total length of path for data transmission (100 m \(\times\) 100 m, 200 nodes). While in our algorithm, each active node only needs to upload data to the leader closest to it, so the length of PDT in LDGM is the shortest. Moreover, trajectories of mobile Sinks in LDGM are controllable. These Sinks visit each TP and receive data from the leader that shorten the distance of single-hop transmission. In addition, the advantage of LDGM on the length of PDT is more obvious when the number of nodes is over 200, as shown in Figure 29. In this case, the number of active nodes is still stable with the help of the sleep scheduling and sensing radius adjustment processes. Thus, the total length of paths for data transmission in LDGM does not significantly increase. Average energy consumption of nodes is shown in Figure 31. It is known that, the advantage on energy saving in LDGM is not very obvious when there are a small amount of nodes deployed in the network because the total lengths of PDT in MST and MWST are short when there are not too many nodes in the network (see Figure 30). Moreover, in this case, the hop distances between nodes and Sink are also short in both of these two algorithms. Thus, according to the data transmission model proposed by [PERSON] [19], it is known that, energy consumption on communication in MST and MWST is low. In LDGM, there are fewer nodes in each DGA when the number of nodes is less than 200. In this case, most of the nodes prefer to upload data to the leader by using a single-hop way which enhances their energy consumption. However, with the help of the sensing radius adjustment strategy, energy consumption on sensing is greatly decreased for most active nodes. So, total energy consumption of LDGM is still of the same level as that of MST and MWST. With the increase of the density of nodes, the advantage of LDGM on energy saving is more and more obvious and the average energy consumption of nodes is also stable because in this case, the number of active nodes increases slowly by carrying out the sleep scheduling strategy. Moreover, the optimal path for data transmission in each DGA could also be found out, which restrains the increase of energy consumption in LDGM. Average residual energy of nodes in the three types of data gathering algorithms is shown in Figure 32. Similar to Figure 31, when the density of nodes in the network is large, the transmission paths in MST and MWST are longer. Thus, residual energy of nodes is lower. With the help of one mobile Sink, the residual energy of some nodes decreases slowly in MWST. But this Sink moves randomly in the network.