## 7 Classification of Mobile Sink based Data Collection Schemes
### Controlled Sink Mobility-Based Schemes
This configuration is shown in Figure 13. To incorporate real-time application requirements, all the CHs are moved in a collaborative manner ensuring their connectivity with the base-station while covering most likely event reporting nodes at the same time. Furthermore, CHs are moved using three different strategies to reduce multi-hop communication and improve nodes' and network lifetime. In the first strategy, CHs are moved towards those sensor nodes where the sensor nodes have relatively more residual energy. This approach is inspired by the fact that an event that occurs is likely be sensed by a group of nearby sensor nodes, thus leading to a spatially distributed energy dissipation. In the second mobility strategy, CHs move towards the event region indicated by more traffic flow. This strategy is adopted to reduce the expected transmission and reception time, thereby reducing the distance between the CHs and the event source. Finally, in the third strategy, a hybrid approach is adopted, thereby first selecting to move the CHs towards the event source and while making the next move towards the source of event, the residual energy-based mobility strategy is being adopted. The experimental results produced in [49] show an increase of up to 75% in the residual energy by employing multiple mobile CHs and controlling their movementsusing the aforementioned strategies. However, the use of multiple energy-rich mobile CHs and the associated cost limits the widespread applicability of the proposed strategies. Another contribution towards prolonging network lifetime exploiting controlled sink mobility has been made by [PERSON] _et al._ in [50]. In the proposed approach, few considerations are made such as how the next move of the sink will affect the latency of data delivery to the sink and the energy consumption of the nodes in adjusting the routes towards the mobile sink. It is argued that usually during sink movement, not only the data delivery latency is increased as packets need to be buffered at that time, but also the energy consumption in readjusting the routes due to sink mobility. Thus in order to control the rate of sink mobility, a minimum pause interval is imposed at various sites. For sink mobility, it defines a heuristic called Greedy Maximum Residual Energy (GMRE) which dictates sink mobility towards energy rich sites, but in accordance with the cost of data route release and establishment due to sink movement to that site. Consequently, if the site is an area that has relatively high energy than the current site and the route control overhead is not significant, the sink will greedily move to that new site, otherwise it will stay at its current site. To know about the residual energy of nodes in adjacent sites, the sink appoints a sentinel node responsible for collecting residual energy of nodes in its site. The sentinel node whenever asked by mobile sink responds with the collected information. The controlled sink mobility approach results in network-wide balanced energy consumption and hence improves the network lifetime. It is claimed that this controlled mobility results in 50% to 100% longer network lifetime compared to random uncontrolled mobility. The main limitation of this scheme is that the less energy areas would always be neglected and would cause huge latency for data delivery to the mobile sink. Furthermore; to maintain energy profiles of different areas of network, a considerable amount of node's energy reserve would be consumed as nodes have to report their residual energy level whenever requested. The Anchor-based Voronoi-scoping Routing Protocol (AVRP) [29] proposed for moderate to high traffic load mWSNs aims to reduce communication overhead and improve data delivery performance. It employs multiple mobile sinks where each sink collects data via anchor nodes from a subset of sensor nodes located in its Voronoi cluster. The anchor nodes act as immediate sinks for nodes within  
Figure 13: Multiple cluster-heads based mobility model [49].