## 2 Background Work
### Path Determination  
The paths that the ferry takes to collect sensed data from sensors can be classified into two categories: random paths and planned paths. Usually in the case of a random path the ferry is attached to people or animals moving randomly to collect sensed data whenever they are in the communication range of the static sensor nodes. In [13] mobile entities called mules were deployed in the environment. Mules pick up data from sensors when they are in close proximity, buffer it, and drop it off when within communication range of wired access points. The authors use a two-dimensional random walk to model the mobility of mules. Both mules and sensors are required to have memory capacity as they are buffering data. In [14] mobile nodes were used in the sensor field as forwarding agents. When a mobile node moves in close proximity to sensors, data is transferred to the mobile node for later depositing it at the destination. Analytical models were used to understand key performance metrics such as data transfer, latency to the destination, and power consumption. Due to the random mobility of the ferry it is difficult to gather sensed data from all deployed nodes. Unlike the random path approach, in the planned path approach a path is determined before the ferry is dispatched and thus the ferry is sent to cover a certain area of deployed sensors to collect data. In [15], a wireless sensor network architecture for a traffic surveillance application with mobile sinks was proposed. All sensor nodes in this architecture were assumed to be located within direct communication range of the mobile sink. All multi-hop transmissions of high volume data over the network are converted into single-hop transmissions to further preserve energy. Therefore, nodes will transmit only in a single-hop fashion to the mobile sink. In Mobi-Route [16], a routing protocol where the sink moves on a planned path to prolong the network lifetime in WSNs was proposed. In this protocol, the sink moves and stops at certain points of interest. The stopping time periods are designed to be long enough to allow for the collection of data. All deployed static sensor nodes need to be aware of the sink movement, its location and the length of stops to send any sensed data to it. In [17] the authors proposed path-planning algorithms for an autonomous underwater vehicle (AUV) which acts as a mobile sink node for underwater sensor nodes. They used Value-of-Information (VoI) as the metric for choosing the path of the AUV. The VoI serves as a marker for evaluating the quality of information with respect to the time spent collecting that data. The authors in [18] used a single ferry to collect data from a circular dense sensor network. They showed that the optimal mobility strategy of the ferry was achieved when moving at the border of the sensing area. They divided the area into circles starting from the source. The inner circles forward the data to the outer ones until the border was reached, where the ferry was used to collect the sensed data. Thus multi-hop forwarding was used to finally reach the ferry. In [19, 20] the ferry visits exact rendezvous points to collect data. These points buffer and aggregate data to the ferry from the nodes though multi-hop forwarding. In [21] the so-called weighted rendezvous points (WRP) algorithm was proposed, where nodes are used as rendezvous points. Cluster heads and nodes send their collected data to these points though multi-hop forwarding. The tour path of the ferry to these points is built by assigning a weight to each one represented by the distance in number of hops from the path and the number of data packets each node is forwarding to the closest point. In [22] the authors chose cluster heads with the highest energy as rendezvous points and then built the tour of the mobile sink to these energy rich cluster heads to collect data. In Section 4.6 we compare our FNRCA against the WRP algorithm and the one in [22].