# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 3 Event Detection and Sensory Data Aggregation
To mitigate this problem, the concept of virtual sensors is introduced, which corresponds to an aggregation point for multiple physical sensors. Specifically, sensor nodes are assigned into several clusters, and head nodes in clusters delegate and function as virtual sensors for gathering and routing sensory data to sink node(s). Consequently, event detection and localization is achieved with the sensory data of virtual sensors. The compressive sensing technique is adopted for recovering the signal with insufficient measurements in a sparse environment, and this strategy can be adopted to improve our approach when sensor nodes are sparsely distributed. Due to the water dynamics, sensor nodes may drift away, and clusters, which are generated in previous time slots, may change their topology afterwards. The adjustment of cluster structures may be energy consuming. Virtual nodes (_i.e._, cluster head nodes) may have to be re-selected, and thus, the topology for sink node(s) and virtual nodes may be re-established after certain time slots. On the contrary, our approach depends on the routing tree for gathering and routing sensory data, where hop counts are adopted for the avoidance of pitfalls, including connectivity holes and shallow zones. The maintenance of the routing tree is mostly to examine parent-child relations between sensor nodes, which are local and not energy consuming. Due to the huge volume of ocean space, autonomous underwater vehicles (AUVs) are usually deployed in the deep sea, especially for supporting search-and-rescue tasks, where AUVs have to surface frequently to transmit sensory data or events to surface station(s) [35]. Since AUV resurfacing may take quite a long time, sensory data reporting delays occur, which may not be tolerable for certain applications. Hence, a cooperative AUV trajectory planning mechanism is proposed, where the number and locations of AUV resurfacing events are adjusted for cycles with non-sensing edges. Generating an AUV trajectory plan for potential event locations is the main focus, whereas the problem about whether events occur or not is to be explored further. The use case of this technique is to monitor oil pipes, where potential events may be located along oil pipes, which are stable in the deep sea. When the objects to be monitored change their location, the applicability of this technique is not discussed and to be explored further. Am m-course (monitoring course)-based solution for the detection of underwater events is developed in [21]. Generally, a sensor network can be divided among a set of cycles, where sensor nodes can be located in at least one cycle, and the links between sensor nodes, called edges, may pass through these cycles. Intuitively, m-courses may form a type of acyclic flow network, where cycles can establish a tree structure among sensor nodes for routing sensory data to sink node(s). Therefore, when an event occurs, the event location can be determined according to the cycles of corresponding m-courses. The cycles in m-courses are somehow similar to hop counts, which is used for establishing the routing tree in our technique. Generally, this technique aims to localize events, whereas event source identification, which may be important for response enactment, should be explored further. Since the occurrence of events is unpredictable, the information (_i.e._, sensory data) should be higher in value when they can contribute more to the detection of potential events. This means that sensory data relevant to potential events should be routed to the sink node as soon as possible [36]. To maximize the value of information (VoI), AUVs are adopted for sensory data gathering, and initial paths are planned through an integer linear programming model. A distributed heuristic is proposed for path planning online, where the AUV chooses the next sensor node to be visited based on VoI. Generally, this technique aims to gather and route sensory data, which facilitate the event identification, to the sink node with a higher priority.