# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 6 Limitations and Challenges of Data-Driven Models  
In this section, limitations and challenges for all data-driven models in WSNs are highlighted for the future research:  
_Query model_: In a network, a query is used for the specific information of a place. For a request from the user, it is important to have geographical knowledge of the sensors' location. To complete a request, the wireless communication links, topology, and deployment of sensors should not have inconsistency. To receive a query and send it back, sensors choose the single best route path from all the other paths. The biggest challenge that the query model faces is that there are many obstacles in large area wireless deployment; such obstacles are roads, mines, outdoor hostile areas, and pipelines. When large spaces occur among the sensor nodes, transmission cost increases, as collision occurs due to the small space from node to node. _Event model_: In this model, data are highly important at times when a specific region of an event occurs. For data accuracy, high-density sensors node deployment is required. However, in most high-density deployment, event-occurring sensors send redundant data, which causes energy consumption. The node coverage must be good for event detection. The biggest challenge is the transmission link between the sensor nodes and the sink. The event information must deliver in the shortest time with a high probability to reach the sink with the exact location. _Time model_: In the time model, the biggest challenge is energy consumption due to hostile areas as well as a large environment where humans cannot replace the sensor's battery easily. The fault in link communication between transceiver and receiver latency is not correct, and packets drop due to the high transmission cost. Researchers should focus on enhancing the network lifetime, which directly reduces data redundancy and affects data accuracy. Mostly, data delivery is based periodically in real-time applications. Data delivery latency depends upon radio connectivity and is used for delivering data in multi-hop communication. The communication between transceiver and receiver is decreased by using multi-hop, which affects the latency and energy of sensors. _Hybrid model_: Many limitations arise when a sensor switches its data from one model to another, as there are some difficulties during data processing and communication. A sensor's life depends on the energy and battery lifetime. In hybrid data models, every data model uses different operations for data storage and sending. To identify the sensor's location, the data delivery ratio, accuracy, and high transmission cost are limitations of the hybrid model. Table 3 shows the limitations and challenges based on data-driven models in WSNs. For future work, the construction and enhancement of a new and innovative routing protocol are demands for the data query model to save the energy of the sensor nodes of the routing path. The elimination of data redundancies is also required for the data route. Moreover, time-sensitive scenarios in any query to further improve data delivery delay problems are required to be resolved. In an event-driven model, various protocols and schemes are required to solve the data delivery delay, end-to-end delay, data accuracy,  
latency, and reliability issues. This is because when an event occurs, the recognition of true or false event information is needed for a quick and safe response. Furthermore, the time-driven model considers the data redundancy cause of high transmission and network lifetime at the sensor node level; thus, the cluster head level still needs to improve for energy consumption and data accuracy. Similarly, in the hybrid model, time-driven, event-driven, and query delivery-related issues and challenges need to be the focus of future researchers.