# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) have been used in many applications, including healthcare, trajectory tracking, environmental monitoring, smart home, military surveillance, coverage, rechargeable and data collection [1; 2; 3; 4; 5; 6; 7; 8; 9]. The data collection issue has received much attention in recent years. In literature, many studies develop data collection approaches, aiming to cope with the energy unbalanced issue in a given region. An advanced routing transfer-low-energy adaptive clustering hierarchy approach (ART-LEACH) has been proposed in [10]. It assumed that the sensor nodes and sink node are static. These studies have minimized energy consumption and extended network life. But the closer the sensor nodes are to the sink, the greater the data-forwarding workloads and power consumption of the node, leading to an energy imbalance problem. To deal with energy imbalances issue, numerous studies have adopted mobile sink to collect data from sensor nodes. These studies are mainly classified into two categories: single mobile sink and multiple mobile sinks. In the first class, some studies [11; 12] adopted one mobile sink to patrol all sensor nodes and collect their readings. Since there is no data forwarding, the power consumption of sensor nodes are minimized. However, the length of the constructed path is too not efficient, raising the problems of buffer overflow of sensor nodes. To resolve the path inefficient problem, some other studies [13; 14; 15] fall in the partial data-forwarding class, which uses a mobile sink to pass through only some nodes. All the visited sensors were called collection points (CPs). Most of these mechanisms partitioned the sensor nodesinto several groups and then established a subtree for each group, where the root of each subtree was a CP. Along the topology of the subtree, each sensor node transmitted its data to the root. Under the given constraint of the total path length, the mobile sink built a route passed through the roots of all subtrees. Then the mobile sink treated these roots as data collection points and collected data from them. However, the selected CPs might not be appreciated, increasing the time required for data collection and reduces the life time of the network in some large-scale applications. To avoid the occurrence of this situation, some other studies [16; 17; 18; 19; 20] fall in the class of multiple mobile sinks. All sensor nodes were geographically divided into several clusters. Based on the remaining energy of each sensor node, a cluster header will be selected in each cluster. Each mobile sink was responsible for collecting data for each cluster. Each mobile sink will establish a route which passed through all nodes in a cluster. The mobile sink then traversed the route and collected data from these nodes in the cluster. However, most of them did not take into account the length cost between any two consecutive cluster headers, the velocity control of mobile sinks and the cooperation between the mobile sinks. As a result, the latency of data collection was long and the established route still can be improved. Given \(k+1\) mobile sinks and a set of \(n\) sensor nodes, this study presents a cooperative data collection algorithm, called CDCA, which initially partitions the \(n\) sensor nodes into \(k\) subsets \(T=T_{1},T_{2},\ldots,T_{k}\) and assigns one local mobile sink \(m_{i}\) to collect the data generated by sensors in \(T_{i}\). Then the proposed CDCA aims to select \(k_{i}\) CPs from the sensors in \(T_{i}\) and establishes a route \(\pi_{i}\) which passes through the \(k_{i}\) CPs for data collection. To further collect all data from the \(k\) mobile sinks, the \((k+1)\)-th mobile sink, called global mobile sink, will construct a path where the \(k\) local mobile sinks can rendezvous with the global sink on the path and transmit the collected data to the global sink. This can improve the energy consumption of sensor nodes and can maximize the network lifetime of the WSNs.