# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 3 Event Detection and Sensory Data Aggregation
The network coverage should address two problems, including the complete coverage of the whole network region and the connectivity of sensor nodes [40]. Besides, a distributed, rather than a central, controller should be adopted as the guide of the AUVs' movement. Therefore, this approach enables AUVs to autonomously decide on and adjust their speed and direction at each step, and the global average neighborhood degree is used as the upper limitation of the number of neighbors of each AUV, for achieving a global optimization. To summarize, traditional techniques for detecting the network boundary may be applied for event coverage detection. However, event source identification, which is of importance for proper response enactment, is out of the scope of these techniques. Besides, events may evolve (quickly) due to the dynamics of the underwater environment, so detecting event coverage is a more challenging task in this context. We argue that techniques for network boundary determination can hardly be used directly for determining event coverage. Event coverage detection and location determination have been studied for two- and three-dimensional terrestrial WSNs. In [41], the topological convex hull of an event region in two-dimensional WSNs is generated in a distributed manner, where no reference and location information or pre-knowledge about the region is required. Sensor nodes report the detection of possible events in a binary mode (one or zero), and sensory data are not gathered. Similarly, [20] proposes a distributed algorithm for the detection of event boundary nodes. Event detection with various user accuracy requirements depends on the specific deployment of sensor nodes and clusters [42]. An event detection method called watchdog is proposed, which can choose and adjust the right energy-efficient sentinel sensor clusters according to certain accuracy requirements. To increase the accuracy and robustness of event coverage, the sensing range of sensor nodes is adjusted at the deployment and event detection phases [43]. Specifically, sensor nodes are one-coverage when deployed and are \(k\)-coverage for improving the accuracy of event detection. A virtualization for representing event sources is presented in [44], which serve as Internet sources and can be accessed by Internet applications. A survey about the anomaly detection in WSNs is presented in [19]. To summarize, these techniques have inspired us to develop our technique in underwater WSNs. They mainly investigate the problem of event detection and coverage determination, while potential event source identification is not studied extensively, which is one of our contributions in this article. Besides, sensor nodes in underwater WSNs are typically deployed very sparely in the network region, and they are dynamic due to the water dynamics. Consequently, we argue that the techniques in terrestrial WSNs can hardly be applied directly in the context of underwater counterparts. ## 7 Conclusions

Due to the vast un-explored ocean space and harsh underwater environments, the importance and difficulty of underwater exploration is well recognized and underwater wireless sensor networks are emerging as a pressing research topic in recent decades. Smart things, or sensor nodes, are deployed for the monitoring of underwater environments and for the detection of possible events. When sensory data of a set of neighboring sensor nodes deviate from a normal sensing range, the occurrence of events is highly possible, whereas that for a single sensor node may correspond to an outlier or error. An appropriate sensor node should be selected as the relay node for gathering and routing sensory data to the sink node (_SN_). Leveraging the geographical locations of sensor nodes provided by underwater localization techniques, the event coverage is determined by _SN_, which is represented as a weighted graph, where the vertices are sensor nodes, and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range.