# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 2 Related Work  
In literature, a number of studies that adopted mobile sink for collecting data are mainly classified into two categories, including single mobile sink collecting data [11; 12; 13; 14; 15] and multiple mobile sinks collecting data [16; 17; 18; 19; 20]. The following briefly reviews these related works. Studies [11; 12; 13] fall in the first class mainly used single mobile sink to collect data from the sensor nodes. Study [11] proposed a heuristic tour-planning schedule for single-hop data collection. This study aims to decrease path length but visit all sensor nodes. However, the algorithm may raise the problem of long delay in large-scale sensor network. In [12], a mobile sink collects data from all sensor nodes in a given wireless sensor network. The main objective of this algorithm is to minimize the latency of data collection by designing the shortest path which passes through all sensor nodes. [PERSON] et al. [13] proposed an algorithm to build path of the mobile sink for collecting all the data before the buffer of each sensor node overflows. However, with an increasing number of sensor nodes, it will still become impractical due to the high computational complexity. The previously mentioned studies visiting all sensors become impractical when the number of sensor nodes grows. In order to cope with the data collection issue, some other works [14; 15] fall in the partial data-forwarding category where the mobile sink only visits some sensor nodes. Those nodes that are not visited by mobile sink should forward their data to the closest visited sensors. [PERSON] et al. [14] proposed a heuristic algorithm which consists of two steps. The first step is finding the root of a data aggregation tree such that the hop distance from any node to the root is minimized. Then the second step selects sensors as collection points to be visited by the mobile sink for data collection. However, the heuristic path construction algorithm did not consider that the distance between current CP and the next CP. This might construct a long path for the mobile sink. [PERSON] et al. [15] proposed a weighted rendezvous planning (WRP) where each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest CPs. Then a set of CPs is determined for constructing a near-optimal traveling tour that minimizes the energy consumption of sensor nodes. However, it did not consider the path cost between two consecutive CPs. This reduces the number of sensors visited by the mobile sink and hence causes the problems of shorter network lifetime or fewer collected data. The abovementioned studies used only single mobile sink to visit each sensor node and collect its data. When the size of network area or the number of sensors grows, the path length of the mobile sink must be increased accordingly. The mobile sink will spend much more time for each round and hence cause high latency for data collection. Therefore, a single mobile sink may not be sufficient for certain applications that require low latency in a large-scale WSN. There have also been several studies [16; 17; 18; 19; 20] that use multiple mobile sinks for data gathering in WSNs to reduce latency. The following reviews these studies. [PERSON] et al. [16] straight lines and receive data sent from sensors via multihop transmission. In case the sensors are deployed uniformly and the density is high, the algorithm has good performance. However, if some sensors in the network is blocked by obstacles or holes, the performance will be inefficient because that the mobile sinks only move along straight lines. To cope with this problem, [PERSON] et al. [17] adopted multiple mobile sinks that randomly move in the monitoring region for data collection. In addition, the mobile sinks delivered the received data to the other mobile sink when they fall in the communication range. Since the route is randomly determined, the latency is difficult to be estimated and is unstable. [PERSON] et al.