# A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems
## 3 Analysis of the Data-Storage and Information-Discovery Schemes Proposed for WSANs
### Schemes Based on the Event-Driven Model  
In the schemes with event-driven models, when sensor nodes detect the events happing in the monitored field, they send them directly to actuators. Thus, actuators can acquire the event data without launching queries, and the real-time information discovery can be achieved. In this model, the challenging problems include how to ensure the real-time, reliable, secure and lightweight routing algorithms from the sensor nodes to the actuators [45; 46; 47; 48; 49], how to improve the coordination among the sensor nodes and the actuators [50], as well as how to execute tasks efficiently for the actuators [51; 52; 53]. In 2010, to improve the reliability and the real-time performances of the event-data transmission from the sensor nodes to the actuators, Dr. [PERSON] proposed a delay-aware reliable event-reporting framework for WSANs [45]. The overall reliability index \(\mathbb{R}\) used in [45] can be formalized as  
\[\mathbb{R}=\sum_{\forall e}\left(\frac{Imp(e)}{\sum\limits_{\forall e}Imp(e )}\times r_{e}\right), \tag{2}\]  
with the condition that the end-to-end delay of data report is smaller than or equal to the latency bound of reporting an event \(e\). In Label (2), \(Imp(e)\) symbolizes the importance degree of the event \(e\), and \(r_{e}\) denotes the reliability index of the event \(e\), where \(r_{e}\) can also be comprehended as the proportion of the data reports that arrive at an actuator within a given delay bound and without data-aggregation and transmission failure. In this framework, the sensor field is divided into grids. In each grid, a random sensor node is in turn selected out as an aggregation node, it aggregates the event data from all the other sensor nodes in its own grid and then sends them to another aggregation node in another grid to get further aggregation. Finally, the aggregation result will be sent to the actuator by a reporter, which is selected from the aggregation nodes. This procedure is illustrated in Figure 2.  
The core model of the framework proposed in [45] is the routing and transmission protocol from the reporter to the actuator. To make as many reports as possible reach the actuator within the latency bound and the reports that have higher importance levels reach the actuator with less latency, the protocol utilizes a priority queue model in each sensor node. In other words, each sensor node has several queues, each of which corresponds to an importance level, in its cache. Packets with higher importance levels will be placed in the corresponding higher-level queues, and will be transmitted prior to the ones with lower importance levels. Moreover, the priority queue model is also used to determine the route selection. Take Figure 3 as an example. When node \(i\) receives an event report \(e_{i}\), it will deliver it to node \(j_{3}\) because the queue with highest importance level in \(j_{3}\) is empty so that \(e_{i}\) can be transmitted with less latency by \(j_{3}\). To the best of our knowledge, the framework in [45] is the first to study WSANs from the data-importance point of view. However, this framework can only work well when the actuators are static. In the scenarios where the actuators can move randomly, the framework does not include a  
Figure 3: Route selection utilizing the priority queue model (priority: \(q_{1}>q_{2}>q_{3}\)). Figure 2: Data aggregation and transmission in the framework proposed in [45]. method on how to search the nearest actuators for the sensor nodes. In fact, due to the duty cycle of sensor nodes and the actor mobility, it is a challenging issue to forward the data from sensor nodes to mobile actors effectively with low delay. In 2011, [PERSON] et al. proposed a location-searching strategy namely ballooning [54] to find out the latest locations of the mobile actuators. In the network model presented in [54], the network field is divided into grids, and all the grids are classified into three categories: the cleared grids, the contaminated grids, and the clearing grids.