## 2 Related Work  
Wireless sensor network is _data-oriented_ as every node might generate some data, and data gathering is indeed a broad topic in the field of wireless sensor network [2, 22]. Most the data gathering research focus on either the energy efficiency or smaller amount of data gathered, and they usually depend on infrastructures such as query trees or clustering to collect data. [PERSON] _et al_. [23] proposed a prediction-based data collection protocol in which a double-queue mechanism is designed to synchronize the prediction data series of the sensor node and the sink node. The results showed the approach reduced communication redundancy and improved the lifetime of wireless sensor networks. [PERSON] _et al_. [24] proposed a hierarchical data aggregation method using compressive sensing that combines a hierarchical network configuration. The model was showed to guarantee accurate signal recovery performance and provide substantial energy savings. [PERSON] _et al_. [22] proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the data gathering algorithm scalable for large-scale network operations. In mobile wireless sensor networks, it is expensive to maintain these infrastructures, so infrastructure-free strategies with mobile elements are adopted in the data gathering algorithms. In this section we survey some related work of data gathering schemes in sensor networks using mobile elements. Clearly, the mobility pattern of the mobile element significantly impacts the optimal data collection scheme; so we roughly classify the gathering schemes into three categories according to the type of node movements: _trajectory based, roaming based_, and _opportunistic based_. In _trajectory based_ scheme, one or more collectors are scheduled to periodically move along a track and collect the data. [PERSON] _et al_. [1] proposed the DataMULEs system, where the collector (MULE node) collects the sensing data and routes them to the access point through one or multiple hops of transmissions. [PERSON] _et al_. [3] proposed a method for using a mobile sink for data collection and increasing network lifetime. It uses a linear optimization model to determine the collector's trajectory: which nodes should be visited, and how long the sojourn time should be. [PERSON] _et al_. [25] defined a trajectory as a closed polygonal chain and derived the trajectory of the collector in different phases. At the partitioning phase, nodes are grouped based on distance and buffer overflow times; during the scheduling phase, paths within each group are calculated as solutions to the Traveling Salesman Problem (TSP), and the group paths are concatenated to obtain the complete trajectory in the network. In this way, it avoids message loss at sensors due to buffer overflows. [PERSON] and [PERSON] [4] proposed a moving path planning algorithm by finding some turning points. The algorithm is adaptive to the sensor distribution and can effectively avoid obstacles on the path, and sensors would forward packets to the mobile collector along each moving line segment in a multi-hop fashion. [PERSON] and [PERSON] [8] extended the work in [4], they formalized the problem as two convex optimization problems aiming to maximize the overall network utility while guaranteeing the given network lifetime and data gathering latency. The scheme considers cases when the collector spends fixed and variable sojourn time at each anchor point, and involves the joint design of rate control, optimal routing, data control, and sojourn time allocation problems. [PERSON] _et al._[9] proposed a scheme that adopts the mobility of the sink node and the spatial-temporal correlation of the event for data gathering. Data gathering is modeled as a sensor selection problem, and it designed a feasible moving route for the mobile sink to maximize the network lifetime at a guaranteed event collection rate and to minimize the velocity requirements for a practical system.