## 2 Related Work
In their approach, the network is divided to subareas and based on the node density the trajectory for the mobile-sink will be constructed by following the Hilbert space-filling curve. And at last, the trajectories in the subareas will be combined together for acquiring the whole network's trajectory. In [34], [PERSON] et al. studied the problem of implementing multiple mobile-sinks in the network based on the agent spawning by defining a main mobile-sink that is able to swam other mobile-sinks with various task assignments. In [39], [PERSON] et al. concerned the problem of defining a fix trajectory for the mobile-sink in an urban area by mounting the sink-node on the public bus. For transferring data from any arbitrary sensor-node in the network to the mobile-sink, a hierarchical structure is proposed by defining some of the sensor-nodes as cluster-heads (CHs) and another type of sensor-nodes which are called Rendezvous Nodes (RNs). The CHs are responsible to harvest data from the sensor-nodes and directly or by multi-hop communications transfer it to the related RN. The RNs work as bridge and buffer the data until the mobile sink reaches to the communication-range of the RN. The selection of CHs is based on the technique that is propose in [40] by [PERSON] et al. The process of identifying suitable RNs will be conducted in each cluster that its cluster-head can contact with the mobile-sink directly. Generally, specifying a RN for a cluster is based on weight-assignment for each candidate sensor-nodes by considering two parameters which are the receipt time of the first and last BEACON message. Since the process for locating the candidate sensor-nodes is random, it cannot guarantee to find optimal solution based on the position of the sensor-nodes in the network. In addition, among the candidate sensor-nodes, the final CHs will be selected based on the highest residual energy without considering other parameters such as location or the number of neighbor sensor-nodes. In [11], [PERSON] et al. designed a solution by setting the maximum number of multi-hop communications in the network as the main measure to design CHs in the network. Obviously, when the number of hops is zero then the problem becomes visiting all of the sensor-nodes one-by-one and can be solved by traditional approaches such as TSP. By increasing the number of multi-hop communications, the main idea is to solve the problem for smaller parameters and extend the solution for larger parameters. The nature of the proposed approach requires processing unit which makes it unadaptable for large-scale networks in which it is not possible to obtain global information about the location and status of the sensor-nodes. In [41; 42], the authors attempt to consider the concept of event for developing mobile sinks in the network. In [41], [PERSON] et al. proposed a convex mathematical model inspired by the support vector regression (SVR) technique to design a COT (Continuous and Optimal Trajectory) that considers three main factors which are the group of sensor-nodes that capture events, the velocity of the mobile sink, and reporting time slot. They defined their model without considering any predefinedstructure for the network. In [42], [PERSON] et al. worked on another approach which attempts to control the movement of mobile sink based on event prediction. It predicts an event from collected sensory data by utilizing the maximum likelihood estimation. And then it designs the movement policy of the mobile sink based on reinforcement learning in Markov decision process. Event prediction is promising approach to improve reliability and life time of the network; however the proposed approach assumes spatial correlation among sensor nodes. Therefore, sensory data are being described by normal distribution. Generally, prediction based on spatial correlation has two disadvantages: firstly, it limits the prediction range to local areas which means if an event occurs far from the mobile sink in large scale networks the approach will not be able to predict it.