# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 2 Related Works  
With the development of sensor networks, how to improve the efficiency of data collection has become one of the hot spots of research. [PERSON] _et al._[19] described the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol for data collection in WSNs. Each node randomly decides whether or not to become a Cluster Head (CH) and the parameter used in decision making is the percentage of desired CHs. The CHs assign to each sensor from their cluster, a time slot for reporting, aggregate data received from individual sensors and send them directly to the Sink. While the major problem with LEACH is that the Sink may be very far from many CHs, therefore direct reporting may be extremely energy-consuming, or even impossible. Another type of cluster based data gathering algorithm named Stable Election Protocol (SEP) has been proposed in [20; 21]. The network is divided into several clusters to collect data respectively and only the cluster headers need to communicate with the static Sink. Simulation results have shown that SEP is suitable for WSNs with a non-uniform distribution but nodes near the Sink or the cluster header die quickly, which caused an unbalance of energy consumption. A type of Equalized Cluster Head Election Routing Protocol (ECHERP) for data collection in sensor networks is proposed by [PERSON], [PERSON] _et al._[22]. It also pursues energy conservation through balanced clustering. ECHERP models the network as a linear system and using the Gaussian elimination algorithm, calculates the combinations of nodes that can be chosen as cluster heads in order to extend the network lifetime. [PERSON] _et al._ focused on hierarchical routing in WSNs by using a new enhanced reactive protocol that aims at the achievement of power conservation through energy efficient routing [23]. This protocol performs the cluster leadership in a non-randomized way but takes into account the residual energy of nodes. Moreover, the data routing is based on a route selection policy which takes into consideration both the energy reserves of the nodes and the communication cost associated with the potential paths. In addition to the cluster based protocols, data uploading algorithms based on the tree-structure have also been widely used in WSNs. A type of data gathering method with the help of a Minimum Spanning Tree (MST) is proposed in [24]. Compared with other multi-hop data transmission methods, time delay in MST is lower and the efficiency on data aggregation is also enhanced. However, there is only a static Sink located at the edge of the network which could lead to unbalanced energy consumption. [PERSON] _et al._[25] presented a Spanning Multi-Tree Algorithm for Load Balancing (SMT-LB). SMT-LB aims to balance the load in multi-sink wireless sensor networks with heterogeneous traffic generating nodes. Simulation results show that SMT-LB is able to achieve a longer network lifetime with lower end-to-end delay by deploying sink nodes at the center of both left and right edges. In [26], the problem of building cluster-based topology for Sensor Networks with several sinks is also considered. The topology associated with each sink is modeled as an Independent Dominating Set with Connecting requirements (IDSC). Thus, the solution is a partition of a given graph into as many IDSC as there are sinks. In addition, several optimization criteria are proposed to implicitly or explicitly balance the topology. Network lifetime is improved since it benefits from a clustered structure and the number of hops control. Furthermore, it becomes necessary to choose an appropriate aggregation factor and transmission radius depending upon the requirement of the application for which the WSN has been installed. [PERSON] _et al._[27] presented a Fermat point based data aggregating protocol which is distance vector protocol in nature. Moreover, [PERSON] _et al._ introduces a novel algorithmic approach for energy-efficient itinerary planning of Mobile Agents (MA) engaged in data aggregation tasks [28].