# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
###### Abstract
For a terrain where a ground robot can move, ground robot-based solutions can be applied. In some application areas, however, terrain may not be accessible to ground robots; thus, aerial robots come into play. Although the data-collection trajectory is easy to plan with the help of an aerial robot, they also have energy constraints and delay problems. The specific limitations of different types of sinks raise the importance of their comparative performance evaluation. This paper addresses this gap and presents comparisons that will reveal the relative performances among static sinks, ground robot-based mobile sinks, and aerial robot-based mobile sinks. Figure 1 shows the visualization of data gathering in WSNs based on different sink types. \begin{table}
\begin{tabular}{c c} \hline \hline
**Ref.** & **Main Contribution** \\ \hline
[44] & In this work, a thorough discussion is provided on the mobile element \\
2011 & ME is provided, based on its trajectory, motion, and speed. This paper also presents a discussion on different forms of MEs practiced in the literature. \\  & In this work, eight data collection protocols in WSNs are critically analyzed, and the obtained results are shown in tabular format. The discussed schemes are EEDC, CAG, GSC, SBR, SCCS, DQEB, LEACH, and PEGASIS. Special discussion is given on multipath and hybrid data collection protocols. \\  & This work presents an in-depth discussion on the distributed database management systems proposed for WSNs. Alongside the advantages and disadvantages of data and query management techniques, an effective future research direction is also provided. \\  & This survey focuses on the routing mechanism of WSN-aided target tracking application by presenting the strengths and drawbacks of the existing schemes. A quantitative comparison is given based on energy consumption, data gathering efficiency, and target tracking time. \\  & In a time-constraint data collection scenario in WSNs, a real-time database management system can play a vital role. This paper provides a short description of such studies and proposes a model for simulation in a distributed environment. \\  & The research work consolidates data gathering techniques proposed from a different perspective such as networking, compressive sensing, signal processing, and information theory. The author provided an analytical model to obtain the energy performance and conducted the simulation to point out the best performing technique. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing surveys on data gathering in WSNs. The main contributions of this research are as follows:  
* This paper presents both qualitative and quantitative comparisons among WSN data-gathering frameworks categorizing them based on their sink types (i.e., static sink, ground robot-based mobile sink, and aerial robot-based mobile sink). Instead of providing a comprehensive review of all the existing data-gathering frameworks for each category, this study selects four protocols for static sinks and three protocols for the other two categories to provide the fundamental understanding of each data-gathering paradigm. * An overview of the data-gathering techniques is provided to afford a basic idea for each of the categories. The frameworks are individually described in terms of their working procedures, advantages, and disadvantages. * A qualitative comparison among the data-gathering techniques of the three categories is provided by mentioning their main ideas, optimization criteria, and performance evaluation parameters. * A simulation-based comparison was conducted to evaluate the performance of the data-gathering frameworks. One protocol from each category is selected for quantitative comparison in terms of energy efficiency, the number of dead nodes, control overhead, the number of packets dropped, and the delay. * Finally, the lessons learned from the comparative analysis are presented. In addition, some recommendations for designing efficient WSN data-gathering frameworks are highlighted.