# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 2 Localization Approaches
### Iterative Localization Approaches  
This approach creates network topology with smaller network elements by dividing the network [14]. A network element can be either an individual node or a cluster of nodes, and every element possess its precise coordinate framework. Blending is indispensable in this approach and can be applied recursively to share the coordinate framework and generate more vital elements. Once the integrated coordinate framework is shared, all the nodes in the network get localized. The iterative localization approach helps in avoiding the local minima through the dimension reducing procedure. The distributed localization approaches can be enhanced by using the iterative mechanism for improving preliminary location precision. Iterative Cooperation DV (ICDV) hop is a distributed localization approach that follows the iterative mechanism. It chooses an optimum number of anchor nodes for higher localization precision and utilizes the hop threshold to restrict distance among the nodes.  
Performance of centralized, distributed and iterative approaches can be analyzed by considering parameters such as position estimate accuracy, node density, design complexity computational cost and energy proficiency. Table 1 shows the performance comparison of these approaches.  
Compared with centralized localization, the distributed localization approach is well suited for high-density networks and is computationally effective. Nevertheless, the IoT networks designed for health, agricultural and environmental and traffic regulation are based on centralized data collection architectures. In these frameworks, the information from individual sensor nodes has to be gathered and administered centrally, and hence the individual sensor nodes possess limited processing ability for energy saving. From the analysis of location estimate accuracy, it has been observed that centralized approaches generate precise estimation outcomes than the distributed, as they have the global perception of the network. On the other hand, higher computational complexity and irregularity due to losing information over multi-hop are pitfalls of centralized approaches.  
On the contrary, designing distributed algorithms is more complex than centralized due to the local and global behavior difficulties. In specific scenarios, distributed algorithms provide the locally optimum solution but fail to optimize glob  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Localization** & **Accuracy** & **Node Density** & **Design Complexity** & **Computational Cost** & **Energy Efficiency** \\ \hline Centralized & High & Low & Low & High & Low \\ Distributed & Low & High & High & Low & High \\ Iterative & Moderate & High & Moderate & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparison of Localization Approaches.  
estimation between sensor nodes propagated to other nodes further deteriorates the distributed algorithm's estimation accuracy. For calculating the energy consumption, the energy required for processing, conveying and receiving in the particular hardware and the transmission range must be considered. Due to the large number of communications in centralized approaches, energy consumption is higher than the distributed approaches.  
On the other hand, distributed approaches are comparatively energy-efficient [15]. It can be observed that any distributed framework can pertain to a centralized one. Moreover, distributed forms of centralized algorithms can also be designed for specific applications. Such algorithms can have optimal trade-offs among the centralized and distributed localization approaches.