# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 1 Introduction  
Recent advances in wireless communication, and computer and micro-electronic technology have enabled the rapid development of tiny, low-cost, and multi-functional sensors. These sensors are commonly deployed in the target areas via a random way to monitor the physical features of the surroundings such as temperature, humidity, and pressure. The monitored data is usually forwarded to the data collector (sink) using a cooperative method (generally multi-hop) and the collector may relay the data to a remote server for further data analysis. Besides, sensors can self-organize themselves based on their local collaboration to form wireless sensor networks (WSNs) [1; 2; 3]. The favorable characteristics of WSNs, such as rapid deployment, high fault tolerance, self-organization, real-time data transport, etc., make them suitable to be deployed in unfriendly, or even harsh environments, especially in military or disaster surveillance. Additionally, WSNs are also extensively applied in industrial product line monitoring, agricultural and wildlife observation, healthcare, smart homes, etc. [4; 5]. Sensors are generally powered by energy-constrained batteries and it is unrealistic for battery replacement because of the enormous quantity of sensors and expensive cost. The best solution for the insufficient energy in WSNs is to equip the sensors with higher-capacity batteries. However, the performance of the battery has reached its choke point and is hard to be improved [6; 7]. Therefore, the energy shortage problem in WSNs could only be addressed by adopting energy efficient routing protocols or algorithms [8; 9]. Additionally, WSNs are also troubled with the unbalanced energy of sensors. Each sensor owns its monitoring range and once a node dies, fade zones will appear and the performance of the network will also rapidly decrease. The phenomenon of unbalanced energy in WSNs is similar to the energy holes, and they are both caused by the \"hot spots\" problem. The \"hot spots\" problem usually happens in WSNs with a static sink and fixed network topology. Nodes around the sink are usually burdened with busier traffic because they need to transfer the data packages from the outer layer to the sink and exhaust their energy faster. Since the sink is static and the network topology remains unchanged, the energy becomes more and more heterogeneous as time goes on. Network lifetime is an important evaluation criterion to estimate the performance of the network and it is commonly defined as the time when the first node dies. A great deal of work has focused on the problem of energy efficiency and energy balance, and great results have been achieved [10; 11; 12; 13]. Clustering technology greatly decreases the energy consumption of WSNs by dividing the sensors into clusters according to some special rules. In each cluster, one or more cluster heads (CHs) are elected to be the relay nodes for its members. Clustering helps the network simplify the topology structure and avoids the direct communication between sensors and the sink. Additionally, data fusion can be adopted in CHs to filter the redundant data to decrease the burden of CHs. One of the classic routing protocols that adopts clustering is the low-energy adaptive clustering algorithm (LEACH); however, the approach for CHs selection is unreasonable and much further work is being conducted based on LEACH. Sink mobility technology is an efficient method to deal with the unbalanced energy in WSNs. In mobile sink-supported WSNs, the sink is usually carried by the intelligent vehicles or robots and it can move freely around the sensing field. The following advantages can be achieved by introducing sink mobility technology. First, the \"hot spots\" problem can be greatly alleviated by the movement of the sink. Areas around the sink are usually traffic hubs and when the sink moves, the traffic hub also transfers. Sensors take turns to be the \"hot spots\" to evenly distribute the energy consumption.