# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 2 Related Work  
During recent years, much attention has been paid to energy-efficient routing protocols. Energy-efficient cluster-based dynamic routing algorithm (ECDRA) [13] is a mobile sink-based routing schema. In ECDRA, the mobile sink is deployed at the outer side of the circular sensor field and rotates in a circular manner. The topology of the network changes dynamically according to the position of the mobile sink. LEACH [15] is one of the most well-known and representative hierarchical routing protocols that was first proposed. In LEACH, all sensors are divided into two types, cluster heads (CHs) and ordinary nodes (ONs). An ON will deliver its monitored data to its corresponding CH, and the CH will fuse and forward the monitored data to the base station (BS). LEACH is much superior to traditional routing protocols in terms of extending the network lifetime. However, due to the random election of CHs, CHs are often unevenly distributed, and CHs communicate with the BS directly, causing large energy dissipation. Power-efficient gathering in sensor information systems (PEGASIS) [16] is an enhanced version of LEACH, which is a chain-based hierarchical protocol. In PEGASIS, each node only needs to transmit the data package to its nearest neighbor, which is closer to the BS than the source node. CHs are connected into a chain by the greedy algorithm for intercluster communication. Then, each chain leader, which is closest to the BS, takes the responsibility to forward the data packages to the BS. The chain construction makes an economical use of energy by avoiding long-distance communication. Meanwhile, because of using multi-hop propagation, serious network delay could be caused. Therefore, this protocol is not suitable for delay-sensitive applications. Hybrid, energy-efficient, distributed clustering approach (HEED) [17] is greatly improved alternative regarding aspects of CHs selection compared to LEACH. It uses the residual energy as a main parameter and nodes with higher energy have a bigger possibility to be the CHs. In this way, the time when the first node dies could be greatly postponed, and more uniform CHs could be generated. Intracluster communication cost is the secondary parameter to determine which cluster a node should take part in. Intracluster communication cost could be estimated using the average minimum reachability power (AMRP) as shown in Equation (1):  
\[\text{AMRP}=\frac{\sum_{i=1}^{M}MinPwr_{i}}{M} \tag{1}\]  
where MinPwr denotes the minimal transmission power demanded by a node to communicate with its corresponding CH. Using AMRP further balances the energy consumption of the whole network. In the above routing protocols, the position of the sink is usually fixed, which results in the \"hot spots\" problem. With the popularity of the portable mobile devices, such as Radio Frequency Identification (RFID), smart phones, and Personal Digital Assistants (PDAs), the further application of the robots, Unmanned Ground Vehicles (UGVs), and Unmanned Aerial Vehicles (UAVs), routing protocols using mobile sinks have become a hot topic in recent several years. Below are some routing protocols using single mobile sink. [PERSON] et al. [18] proposed an algorithm called Load Balanced Clustering and Dual Data Uploading (LBC-DDU). In LBC-DDU, the whole network is divided into three layers: sensor layer, cluster head layer, and SenCar layer. At the beginning of each round, the SenCar could calculate the optimizedpath in advance and then walk along the path to gather information utilizing single-hop transmissions. After visiting each selected point, the SenCar will return to the base station and make preparations for the next round. The SenCar is equipped with two antennas such that it can exchange information with two CH simultaneously utilizing the technology of Multi-User Multiple-Input and Multiple-Output (MU-MIMO), which makes contributions toward delay reduction and efficiency improvement. [PERSON] et al. [19] proposed a tree-cluster-based data-gathering algorithm (TCBDGA).