## 2 Related Work
### Data Collection with Mobile Sinks  
The issue of data collection in WSNs with mobile sinks has drawn much attention recently. Existing works are surveyed in [8; 9], and can be summarized as three categories according to the sink mobility. As introduced in Section 1, they adopt different routing schemes, i.e., single-hop routing, multi-hop routing, and rendezvous-based routing. Random MobilityIn [10], mobile entities are exploited to gather data via one-hop communication. The authors mathematically analyze the performance (e.g., energy efficiency, latency, and delivery ratio) based on the random mobility model. [PERSON] et al. present \(\lambda\)-flooding to achieve real-time data collection via multiple hops [11]. \(\lambda\)-flooding locally updates the collection tree and thus can effectively reduce the route maintenance overhead. In [12], the Predictive QoS Routing incorporates information potentials with the mobility graph to support data delivery in non-local sink movement scenarios. The mobility graph can be learned from training data and used to predict the future relay node nearest to the mobile sink. [PERSON] et al. propose delivering data to stashing nodes along all predicted possible trajectories, and then the stashed data will be collected when the sink passes by [13]. Path-Controllable MobilityUnder this mobility pattern, many works are dedicated to designing the sink path for improving the network performance [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. In [17], mobile elements visit each node to collect the buffered data using one-hop communication. The movements are scheduled to guarantee that the data can be delivered before buffer overflow. For multi-hop routing towards mobile sinks, several works [18; 19; 20] optimize the sink path to achieve energy balance in the network. A rendezvous-based approach [21; 22] is introduced, with which data packets are first transferred to nodes along the scheduled path and further uploaded to a mobile collector when it arrives. [PERSON] et al. aim to find the optimal path that minimizes the total route length and can be toured within the delay constraint. The work in [23] guarantees that all packets can be relayed to the mobile collector within bounded hops while the moving path length is minimized. The authors propose both centralized and distributed algorithms to determine the path. [PERSON] et al. design the sink path so that the energy reduction will be maximized and a round-trip data collection can be completed within a specified time period [24]. In [25], a continuous sub-path on a given constrained trajectory is selected for the mobile sink to collect the maximum amount of data within the delay bound. [PERSON] et al. find the moving paths  
Figure 1: Three routing schemes in wireless sensor networks with a path-fixed mobile sink. for multiple sinks to achieve low-latency energy-efficient data gathering while exploiting the sleep scheduling and the sensing radius adjustment to reduce coverage redundancy [26]. Path-Fixed MobilityThe works in [27; 28] optimize the energy consumption in WSNs with path-fixed mobile collectors. Several other proposals focus on the data collection on a pre-defined path in energy harvesting sensor networks. In [29; 30], the amount of collected data is maximized by optimizing the time slot allocation to individual nodes along the path while their energy budget constraints can be satisfied. However, the above research assumes that all sensor nodes can directly communicate with the collectors in one hop, which might be infeasible in many scenarios. [PERSON] et al. present a protocol named MobiRoute in which the mobile sink pauses at different anchor points along a fixed path and sensors transmit data packets to it through multiple hops [31]. To maximize the network lifetime, a 2-phase algorithm is used to allocate the pause time at each anchor point. In [32], nodes transfer data to corresponding cluster heads via the shortest path.