## 1 Introduction  
In recent years, wireless sensor networks (WSNs) have seen tremendous applications in different aspects of our lives such as habitat, structure health and remote health monitoring, precision agriculture, home automation, smart electric grids, and intelligent transportations systems. Typically, a large number of tiny computing devices (nodes) constitute a WSN where nodes are considered as constrained in resources, _i.e._, with limited on-board memory, short-range radio transceivers, and battery power. Depending on the application environment, nodes are interfaced with various sensors for monitoring some phenomenon of interest (temperature, humidity, pressure, _etc._) and forward sensory data to special devices (sinks) in a cooperative manner (typically multi-hop). The sink device (base-station) upon receiving the sensory data analyses the reported activity and may further route the data to a remote user/database via some regular infrastructure such as the Internet [1]. A typical WSN architecture is illustrated in Figure 1.  
Nodes in a sensor network are battery operated and in most situations, battery replacement or recharging is not viable. To achieve prolonged network lifetime, sensor nodes must tailor their activities in an energy-efficient way so that the scarce energy reserves are used very efficiently. Upon deployment, sensor nodes sense, process and communicate an observed phenomenon. Among these tasks, communication is considered as the main consumer of sensor energy reserves, thereby imposing strict energy-aware constraints on all communication activities by the sensor nodes [2]. Since routing protocols and media access control (MAC) protocols are directly related to the communication module, hence protocols at these two layers must make an intelligent utilization of the scarce energy resources. For prolonged network lifetime, not only is the energy consumption of individual sensor nodes important, but also balanced energy consumption among all the sensor nodes is desired [3]. In traditional WSNs, sensor nodes are distributed in the sensing field whereupon detecting some event of interest, nodes report the sensed event back to some static sink(s) through multi-hop or single hop communication. One major drawback of such communication infrastructures is that the sensor nodes close to the sink will consume more energy (partly for reporting their own sensed data and partly for  
Figure 1: Wireless sensors network. relaying their neighbors' data), and thus their energy will deplete quickly. Consequently, this will result in isolation of the sink and as a whole the entire network would no longer be operational. This problem is commonly known as the hot-spot or sink-hole problem in wireless communication. To deal with this issue, the concept of mobile sink was introduced in [4, 5], that not only results in balanced energy consumption among the nodes but can also be exploited to connect isolated segments of the network [6]. Another motivation for introducing a mobile sink in a WSN is that some applications explicitly require sink mobility in the sensor field. For instance, a rescuer equipped with a PDA moves around in a disaster area to look for any survivors [7], and a farmer while walking around a field would be interested in knowing which segment of the field requires watering, fertilizers, _etc._ Although the sink mobility improves network lifetime, at the same time it incurs additional overhead for the routing protocol for dynamic route adjustments. Due to sink mobility, the topology of a WSN becomes dynamic and to cope with such a dynamic topology, the routing algorithms specifically designed for static WSNs cannot be directly applied in mobility situations. This has triggered the development of new routing strategies for Mobile sink-based Wireless Sensor Networks (mWSNs). In this paper, sink mobility is covered from different perspectives with the main aim of critically discussing the performance of existing mobile sink-based data collection schemes.