# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 1 Introduction  
In recent years, the development of smart sensors in real-world applications has given traction to the progression of Wireless Sensor Networks (WSNs). WSNs consists of micro-sensors with the capability of monitoring environmental and physical factors, such as humidity, seismic events, temperature, motions, vibrations, etc. [1]. These sensors are smart, intelligent, inexpensive and small-sized nodes, to which is owed the drastic enhancement in the development of Micro-Electrical Mechanical Systems (MEMS) [2]. A typical sensor node consists of a sensing unit, power unit, processing unit and radio unit; cumulatively, these sensor nodes are deployed over a chosen area of interest in a structured or unstructured manner [3]. Various factors influence the design of WSNs, such as scalability, fault tolerance, hardware constraint, operating environment and power. Researchers always try to overcome all these issues, but due to the limited energy resources, they mainly focus on the power constraint to design an energy-efficient routing protocol [4]. By studying the existing schemes, it is noticed that the clustering technique is effective among the location-based and flat routing techniques [5]. It organizes the sensor nodes into clusters by dividing the sensor field and allows sensor nodes to transmit data to the Cluster Head (CH), which is responsible for data transmission towards the sink. Various clustering routing protocols are proposed in order to maximize the network lifetime using a static sink, but the traditional static sink has some limitations [6]. Before mobility schemes, these static networks were easy to manage and maintain due to the small variation in the network topologies, which simplified the challenges occurring at different levels [7]. These static sink-based WSNs were found defective recently in large-scale networks because nodes that are near the static sink act as relays for the nodes that are far from sink. Therefore, these nodes lose their energy very quickly, and this causes the energy hole problem in the sensor field and results in premature disconnection of the network [8]. Due to the death of nodes, the sink gets isolated from the other nodes that are alive and functioning properly. A WSN with multiple static sinks is proposed to solve this issue, but the issue remains the same for large-scale networks due to the huge amount of sensor nodes in the field and the few static sinks at constant locations in the network [9]. Much effort has been devoted to designing energy-efficient mobile sink base routing protocols. This extra property of mobility can enhance the performance of the network in real-time scenarios due to the constant movement of the sink over the network, which leads to less packet drops and extends the network lifetime because of the short-range communication throughout the network [10]. Furthermore, the sink moves over the entire network field and collects the sensed information, which alleviates the energy hole problem from the network [11]. For this reason, sink mobility is exploited in WSNs with promising advancement in the performance of the network lifetime. In order to obtain the maximum advantage and cross-check the performance of mobility-based routing protocols, it is better to determine the suitable mobility pattern [12]. A routing protocol should accurately represent the mobility pattern and utilize the network energy resources in an efficient manner. Controlled and uncontrolled mobility schemes were proposed in the past, whereas uncontrolled mobility was mostly based on random mobility, while controlled mobility has different variations such as grid mobility [13]. Random mobility is one the earliest mobility patterns, and different versions of this mobility pattern were proposed in the past; however, it has many flaws, such as nodes are unfamiliar with the location of the sink during the whole network lifetime, which increased the packet drop ratio.