# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 2 Related Works
### Controlled Mobility  
As for a controlled mobility scheme, it is possible to guarantee the data delivery efficiency with the help of efficient communication protocols and data collection schemes while the trajectories of the mobile Sinks are constrained or controllable [13]. The primary objective to control the mobility is to improve data gathering performance of the mobile Sink, yet handling the mobility of the Sink in a controlled manner is much more challenging than that of a random scheme [38]. Thus, most of the current work in this case has focused on how to design the optimal trajectories of mobile Sinks to improve the network performance [13].  
In [39], a routing protocol, called MobiRoute, is suggested for WSNs with a path predictable mobile Sink to prolong the network lifetime and improve the packet delivery ratio. The Sink sojourns at some anchor points and the pause time is much longer than the movement time. Moreover, in MobiRoute all nodes need to know the topological changes caused by the Sink mobility which is unrealistic [13].  
[PERSON] _et al._ [40] have studied the scheduling problem of mobile Sink in WSNs to optimize the path of the Sink to visit each node and collect data before buffer overflows occur. Furthermore, a partitioning-based algorithm is presented in [40] to schedule the movements of the mobile element to avoid buffer overflow. However, the mobile Sinks in [40] need to visit all nodes to collect data which causes huge energy consumption.  
A type of Mobile Sink based algorithm for Stable Election (MSE) is proposed by [PERSON] _et al._ [16]. Sink moves back and forth along a certain designed trajectory which solves the energy hole problem. Moreover, MSE has also been proved to be used in a non-uniform network. However, nodes near the static trajectory die more quickly than others, especially in large networks.  
[PERSON] _et al._ [41] propose a biased, adaptive Sink mobility scheme that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. Sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively staying longer in network regions that tend to produce more data. Nevertheless, the Sink usually takes a long time to finish a complete data gathering task that causes high latency problem.  
The contributions of this paper can be concluded as follows: Firstly, with the help of the minimum network coverage model in WSNs, the network is divided into virtue regions and the leaders are selected according to their residual energy as well as the average distance to all of the other nodes. Network lifetime could be prolonged by periodically selection of the leader and the number of traverse points in LDGM is less than in other mobile Sink strategies. Secondly, the sleeping scheduling and the sensing radius adjustment strategies are adopted in LDGM that not only reduce the number of active nodes in data collection but also decreases the energy consumption on sensing. Moreover, with the help of multiple mobile Sinks, the end-to-end delay in data uploading is greatly reduced.