## 1 Introduction  
With the development of wireless communication technologies, wireless sensor networks (WSNs) have been widely used in various applications, e.g., industry monitoring [1; 2], scientific data collection [3; 4; 5], target tracking [6], military surveillance, and even underwater environment [7]. In these applications, the sensory data needs to be uploaded to the sink quickly and accurately; thus, data collection plays an important role in WSNs. In most of application scenarios, energy of sensor node is limited and can not be replenished [8; 9; 10], although some charging algorithms are proposed recently [11; 12]. Hence, it is significant to economize energy consumption in data collection. Traditional methods of data collection are to utilize static sink or base station [13]. These methods always lead to \"hot spot\" problem. Hence, mobile sinks are introduced to solve this problem. Recent researches reveal that using the mobile property of sink is more promising for energy efficient data collection. In these researches, vehicles or aircrafts, which are equipped with wireless communication device, can move to the source nodes and collect data from them directly. It results in energy consumption balanced of the whole network and the lifetime of networks prolonged. In some application scenarios, nodes in WSNs may not be fully connected because of the presence of dead nodes or obstacles. Appointing a mobile sink to move into each unconnected region to collect data is a feasible solution. In this way, more information can be collected [14].  
However, data collection using a mobile sink introduces new challenges for WSNs. One challenge is how to update the current location of sink with lower overheads and route data to it. The mobile sink can collect data in non-real time or real-time. In non-real time data collection, data is routed and buffered at certain nodes before it is collected by a periodically visiting mobile sink. It results in a high latency of data and the tendency of nodes' memory overflow. In real-time data collection, the mobile sink moves around, and data is routed to the location of mobile sink, which requires nodes to obtain the current sink location [15]. It involves high control overheads for location updating and routing hand-offs. In this paper, we focus on real-time data collection using a mobile sink.  
Another challenge is how to set a relative optimal mobility pattern for the mobile sink. There are three main mobility patterns: trajectory with dynamic adjustment, random walking and predetermined trajectory. The performance of a mobility pattern is judged by the following two parameters: energy consumption efficient, and network lifetime.  
Based on the analysis mentioned above, we propose a greedy scanning data collection strategy for large-scale WSNs with a mobile sink. Our contributions are the following:  
* Sink Location Local Updating Method: In our data collection strategy, the network is divided into virtual grids. When the mobile sink moves from one grid to another, only a few of the grids need to update the location of mobile sink. Sensory data from the whole network can be routed to the sink timely.
* Greedy Scanning Mobility Pattern: The mobile sink moves like a scanning curve in the screen of radar. According to the sum of data received from each direction, the mobile sink makes the decision to move to the area with more sensory data.  
The remainder of this paper is organized as follows. Firstly, the related work of data collection schemes with a mobile sink is introduced in Section 2. The detail of GSDCS is described in Section 3. The simulation results and performance evaluations are given in Section 4. Finally, Section 5 concludes the paper.