# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 6 Limitations and Challenges of Data-Driven Models
In addition to these issues, switching between models, prioritization, authentication, handling the critical and non-critical data, storage of data, and data processing and analyzing issues need to be improved and focused on for the further research. Table 3 is based on Table 2 for analyzing the performance metrics in WSNs that help the researchers with further research, and the future works related to each data-driven model are described in detail. * In the time-driven data model, the network lifetime is considered as a highly critical issue. The dense deployment sensor nodes are in a hostile monitoring environment for continuous data collection. When any node fails to perform the specific functions, hardware risk occurs due to the changing condition of the surroundings such as overcooling and overheating. Therefore, there is a need to increase the network lifetime so that the model can work in harder conditions such as with glaciers and at high temperature in industrial environments to enhance the network's functionality. * Additionally, there are various implementations of WSNs for continuous data collection in hostile environments. In this scenario, several sensor nodes are moved far away from the wireless connections in the base location. Hence, these nodes depend upon the entire network of sensor nodes for data transfer to the base station, so that there is a need to work on these out-range sensor nodes for the data collection and location identification. * The event-driven data model is mostly used when an event occurs in any location of WSNs. A lot of primary data are lost because certain threshold values are set to detect an event so that there is a need to recover the lost data during the initial stage of an event. * In certain areas, when an event occurs, only active nodes transmit the data. Due to high data transmission, the nodes' energy is imbalanced across the whole network, which also causes high energy consumption. * Various applications based on irregular or fault events sent information to the sink nodes such as in busy traffic where no accident has occurred but the data are interpreted as there being a road accident; all these problems could be improved in future work. * The query-driven feature in WSNs: only the sink node generates queries for the whole network due to the high communication cost, many queries could collect a similar data response by various sensor nodes in the network. However, due to double communication (forward and backward query), the network's performance became slow and weak. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Model**} & \multicolumn{6}{c}{**Limitation and Challenges**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Energy Consumption**} & \multicolumn{1}{c}{**Transmission**} & \multicolumn{1}{c}{**Data Delivery**} & \multicolumn{1}{c}{**End to End**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Reliability**} & \multirow{2}{*}{**Mobility**} & \multirow{2}{*}{**Latency**} \\ \cline{2-10}  & **Node** & **CH** & & & & & & & \\ \hline Query driven & high & medium & low & medium & medium & medium & medium & high & Medium \\ \hline Event driven & medium & medium & low & medium & high & high & high & high & High \\ \hline Time driven & high & high & high & high & medium & medium & low & low & Low \\ \hline Hybrid driven & high & high & high & high & high & high & high & high & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analyzes the limitation and challenges of each data-driven model in WSNs. * Investigation of the interaction of a cross layer and double communication with routing and query processing still needs to be explored in future work. * A hybrid data-driven model is considered more challenging and limiting than other data-driven models. Suppose a hybrid model is based on time and event-driven models. Mostly the data redundancy is removed, and the energy efficiency is enhanced by using the time-driven model.