# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 2 Related Work  
Currently, the existing literature focuses on developing energy-efficient routing protocols for WSNs that deal with the entire static network environment. Some of the recent research work took the initiative of proposing sink mobility with the existence of static nodes in order to collect information more accurately from the cluster-based self-organized sensor nodes. Sink mobility offers a unique opportunity to access the different network areas to avoid packet drops due to the longer distance transmissions of sensor nodes. In realization of this fact, several mobility-based energy-efficient routing protocols have been proposed recently. These protocols are based on different mobility patterns moving in the sensor field. All of these protocols are classified into two main schemes: controlled mobility pattern and uncontrolled mobility pattern. In [20], the controlled mobility pattern, speed or direction of the sink can be controlled and managed by the end-user according to the variation in the network; whilst in the uncontrolled mobility pattern, the sink changes the speed or direction autonomously, which causes unknown time for CHs' communication with the sink [21]. This paper considers the spiral-based controlled mobility pattern. We briefly describe the related work in this context including the strengths and weakness of their methodologies in the following paragraph. An Intelligent Agent-based Routing (IAR) [22] protocol is developed to reduce signal overhead and efficient data delivery to the sink. The authors choose some sensor nodes as agents, and the sink visits the sensor field. At the time when the sink arrives in the range of agents, agents start transmitting data to the sink. If the sink is not in the range, then the sink chooses a provisional node as a relay node, which receives information from the agent and transmits it to the sink. To prolong the network lifetime, a Mobile Sink-based Routing Protocol (MSRP) [23] is proposed. The sink moves to the top of the highest energy node in a cluster and receives data from that node. Introducing CHs as the root of a tree [24], when the mobile sink arrives in the sensor field, it will register itself with the nearest CH as a root; in this way, the CH will update other CHs with the route and location of the sink. When the sink arrives in a cluster, the CH only maintains the connection with the sink and evades the update to the root of the sink location. When the sink arrives in the next cluster, it will choose another agent node, which will be a new CH; this CH shares the information about the location of the sink according to the root and other nodes. It appears to be a good technique for reducing the overhead of clusters because of multi-hopping. However, the root node plays an important role in route management, which causes a quick reduction of the energy and reduces the network lifetime. A hierarchical cluster architecture (HCDD) [25] is proposed to introduce the second level CHs, named as the agents. These agent CHs are chosen to keep track of the sink location and route selection. All the data from CHs are routed to the closest agent, and when the sink moves in the sensor field, it informs the closest agent via the nearest CH. Upon the arrival of the sink, the agent broadcasts the sink location to all the other agents using inter-agent communication. Due to this high mobility pattern, the HCDD node utilizes more energy, which causes the quick death of the network. Moreover, limited broadcasting of the sink location can cause high latency. A predetermined trajectory is proposed [26], in which the author utilizes multiple sinks on a predefined trajectory where mobile sinks move along the lines of a hexagonal tiling and stop for a predefined specific time at six and twelve different points for data collection. It appears to be a good technique for network lifetime enhancement, but this technique utilizes direct transmission to the mobile sinks, which causes extra cost for farther nodes in terms of energy dissipation.