# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 2 Related Work
In TCBDGA, the weight of each node is calculated using several main factors including residual energy, the number of neighbors and the distance to the BS. Each node chooses its neighbor with maximum weight as its parent node. In this way, a tree-construction is set up and then each tree is decomposed into several sub-trees according to its depth and its traffic load. Simulation results show that it has better performance in terms of energy consumption. [PERSON] and [PERSON] [20] introduced the mobile sink in WSNs with obstacles, where a spanning graphs-based scheduling mechanism was proposed. On the basis of this scheduling mechanism, the authors propose a heuristic path planning algorithm to find a shortest path to avoid the obstacles. A mobile sink is set to walk along the path and collect data from CHs via direct transmission. The mobile sink returns to the origin after data collection. Simulation results demonstrate that their proposed algorithm can extend network lifetime effectively. The scheduling mechanism they proposed makes a good contribution to reduce the complexity traversing in WSNs with obstacles. [PERSON] and [PERSON] [21] proposed a velocity energy-efficient and link-aware cluster-tree (VELCT) scheme. This scheme mainly contains two phases, set-up phase and steady-state phase. In the set-up phase, CHs are selected based on the threshold value and clusters are formed with better performance via intra-cluster communication. After that, a few nodes are selected to serve as data collection nodes (DCNs) to construct a data collection tree (DCT). In the steady-state phase, CHs collect monitored data from its members and then forward the aggregated data to DCN. Experimental results show that their proposed scheme has better performance in network throughput, energy consumption and network latency. [PERSON] and [PERSON] [22] presented an efficient data aggregation algorithm called cluster-chain mobile agent routing (CCMAR). In CCMAR, the cluster head selection value (CHSV) is utilized to choose the optimal CHs. Cluster members form chains to transmit their data to the sink. The residual energy level, signal strength and path loss are considered to schedule an optimal routing path for the mobile agent (MA). Furthermore, some literature tries to optimize the performance of the network in aspects of energy consumption and lifetime by combining the particle swarm optimization algorithm [23; 24; 25], immune algorithm [26], bio-inspired ant colony optimization algorithm [27], clustering algorithms, etc. Some researchers introduce multiple mobile sinks in WSNs and further improve the performance of the network. [PERSON] et al. [28] presented an event-driven algorithm using multiple mobile sinks. In the algorithm, each node has two states: monitoring and transmitting states. When an event is captured, the related node turns its state from monitoring to transmitting and a group of active nodes are generated to send packages to the mobile sink. In large scale WSNs, due to the limit of the mobility of the mobile sink, the network is divided into several parts and each part introduces a mobile sink. [PERSON] et al. [29] presented a distance-aware routing algorithm using multiple mobile sinks to decrease the energy consumption of the network. [PERSON] et al. [30] introduced multiple mobile sinks to move along the predetermined paths to gather raw data. With the aim of prolonging the lifetime of the whole network, [PERSON] et al. [31] introduced multiple mobile CHs with rich energy. The mobile CHs work in a collaborative manner to collect information from different sections of the sensor field. The base station takes responsibility for communicating with the mobile CHs. Three different strategies were adopted to reduce multi-hop communication and enhance the lifetime of the network.