# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 1 Introduction
Inner and outer boundaries are identified for localizing event locations and geographic ranges. It is worth mentioning that sensor nodes are typically deployed very sparsely in underwater WSNs, due to the fact that deploying an underwater WSN is very costly nowadays, and the network topology may change (dramatically) due to the movement of sensor nodes caused by the water dynamics. We argue that these techniques in terrestrial WSNs may hardly be used directly in the context of underwater counterparts. Note that there are some techniques that study event detection and localization in underwater WSNs [17, 21]. For instance, virtual sensors are adopted to represent an aggregation point for multiple physical sensors [17], since physical sensors may drift along the water dynamics and, thus, may hardly be localized. Accurate event location is achieved with a relatively low sensing overhead. A monitoring course-based event localization technique is proposed in [21], where monitoring courses can facilitate the event location determination and identify possible network issues before forwarding data packets to sink node(s). Generally, these techniques are promising in determining event boundaries in underwater environments. It is worth noting that the sensory data of sensor nodes, which are internal (or external) to event regions, may not vary dramatically during certain time durations, when the environment to be monitored is relatively steady. Besides, certain applications may tolerate the bias of sensory data to be used and to be sensed in real time to a certain extent. A sample application is marine shellfish monitoring, where the pH value, the biotoxin value, _etc._, may not change to a large extent,when their values have already indicating the occurrence of pollution. In addition, when the value of attributes like pH (or biotoxin) is within a certain range, the pollution is assumed as having the same grade of severity. This means that sensory data gathered at previous time slots may be reused, rather than fetched from the network in real time, for supporting certain applications in the forthcoming time slots, when the value changes slightly. When an event is detected to have occurred, potential event sources should be identified, which should be considered as the key points for the enactment of a prompt and proper response mechanism. Consequently, an energy-efficient mechanism for the detection of event coverage, while considering the reuse of sensory data and accurately determining potential event sources, is a pressing research challenge. Leveraging our sub-region query processing mechanism developed in our previous work [22] and traditional underwater localization techniques [23], we propose in this article a strategy that aims to detect the event coverage. Our major contributions include the following two aspects: (i) our technique can determine the event coverage in an energy-efficient fashion; and (ii) it can identify potential event sources, which are of importance for prompt and proper response enactment. Without loss of generality, the geographical space of an underwater WSN is defined as a three-dimensional rectangular region. The procedure for this technique includes the following steps:  
* Our sub-region query processing mechanism developed in [22] has been improved, where a set of neighboring sensor nodes, whose sensory data deviate from a normal sensing range in a collective fashion, are identified. These sensory data are routed to sink node(s) through our routing tree [22] in an energy-efficient fashion. Note that an event is different from an error, which typically corresponds to an outlier in the network [19, 24]. Sensory data of errors are excluded from being routed to sink node(s). * Based on the sensory data of sensor nodes in possible event regions, the coverage of events is detected, which is represented as a network (or a weighted graph) of sensor nodes. Potential event sources are determined through an algorithm that identifies barycenters in a weighted graph [25].