## 2 Related Work
Similarly, [PERSON] and [PERSON] [10] proposed a polling-based mobile gathering approach where a subset of sensors will be selected as polling points that buffer the aggregated data locally and upload the data to the mobile collector when it arrives. They formulated the problem into an optimization problem of bounded relay hop mobile data gathering. More recently, [PERSON] _et al._[6] proposed a hierarchical data gathering scheme that used two types of mobile elements: the mobile collector (\(MC\)) and the mobile relay (\(MR\)). \(MC\)'s collect data from sensors and forward them to the \(MR\), which will deliver them to the sink. It formulated the problem as an integer linear programming (ILP) optimization problem aiming to find the optimal trajectories for \(MC\)'s and the \(MR\) such as to minimizing the traveling distance of mobile elements. The _roaming based_ schemes are similar to the trajectory based schemes, which also have a trajectory or track. However, in trajectory based schemes the collector strictly follows the trajectory once the trajectory is scheduled or calculated; while in roaming based schemes the mobile collector might roam away from its track or trajectory due to special events or constraints, such as buffer overflow or latency due to data collection. The collector could freely move to any location in the field to collect the data. [PERSON] _et al._[26] proposed a _node-initiated message ferrying_ approach. When an event is detected by the stationary nodes, the nodes would send a request to the the collector in order to be visited; the collector could modify its trajectory by visiting the requesting node, and then go back to the original route. [PERSON] _et al._[19] introduced the _mobile element scheduling_ problem where nodes operate with different sampling rates. They formulated the problem of scheduling the mobile element in the network to prevent buffer overflows at source nodes. The problem is shown to be NP-complete and an integer-linear-programming formulation is given. [PERSON] _et al._[27] extended the work in [25] and proposed a scheme that differentiates message delivery considering both regular and urgent message collection. They incorporated multi-hop communication into the mobile element scheduling problem, where the investigated performance metrics include the minimum required speed of mobile element to prevent data loss and guarantee the maximum tolerated urgent message delay, and the urgent and regular message loss rates for a given speed. In _trajectory_ and _roaming_ based data gathering schemes, ordinary nodes are stationary. They just wait the collector to come and pick up the data. Yet, in _opportunistic_ data gathering schemes both the data collector and the ordinary nodes are mobile, and an optimized trajectory is very expensive to maintain and is usually impossible when designing the data gathering schemes [13]. Examples of these network have been successfully employed in the context of wildlife monitoring applications, such as trackingof zebras in the ZebraNet project [11] or whales in the SWIM system [12]. Sensor nodes are attached to animals and act as peers, and adopt a \"Store-Carry-Forwarding\" strategy for message transmission. Based on the history of node movements, each node maintains a probability to the sink, and the node with higher probability would forward its messages to the node with lower probability if there exists chance of communication at some proper time. When mobile peers get close to a base station, the gathered data is uploaded to the base station, and is flushed by peers in order to save storage. Also, the concept of _people sensing_ is introduced for opportunistic data collection through mobile peers in urban sensing scenarios [14,28]. Sensors are not used mainly for monitoring the environment, but are rather exploited to characterize people in terms of both interactions and context (or state) information.