# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 3 Event Detection and Sensory Data Aggregation
as event sources. Note that the function _round_() in Line 1 of Algorithm 5 is the rounding function for decimals, and the function _sizeOf_() returns the number of elements contained in a certain set. Given a sensor node \(uv_{src}\) contained in \(g_{evt}.UV_{evt}\) (Line 2), the edges \(EG_{src}\) connecting \(uv_{src}\) are retrieved (Line 3), and the sum of weights specified upon \(EG_{src}\) is calculated (Lines 4-7). \(uv_{src}\) can be a potential event source when one of the following conditions can be satisfied:

* there exists slots for candidate event sources (Lines 8-9) or
* there exists another candidate event source \(uv_{lst}\) (Line 11), which is not as appropriate as \(uv_{src}\) (Line 12). This is specified by the condition of \(uv_{lst}.wgt_{sum}<uv_{src}.wgt_{sum}\). Consequently, \(uv_{lst}\) is replaced by \(uv_{src}\) in \(UV_{src}\) (Line 13). An example is shown in Figure 1, where two event sources for \(EI\) are the sensor nodes with marks of 84 and 86, and one event source for \(E2\) is the sensor node with the mark of 90. It is worth noting that some connected components in \(g_{evt}\) may contain the majority of event sources, since sensory data for sensor nodes in these connected components may deviate to a relatively large extent from the normal sensing range; while the situation for sensor nodes contained in the other connected components is not that serious somehow. When this situation is encountered, it is appropriate that prompt and proper responses should be taken to remedy those more serious event sources. ## 5 Implementation and Evaluation

The prototype has been implemented in a Java program, and experiments are conducted for evaluating the performance and efficiency of our event coverage detection and event source determination mechanisms. In the following, we introduce the environment settings, present the results of experiments and compare our technique to CARPas the protocol for routing sensory data to \(SN\). ### Environment Settings

The parameter settings for our experiments are presented in Table 1. Specifically, the network is deployed in a three-dimensional underwater space with the geographical volume of 1 \(\times\) 2 \(\times\) 0.5 \(\mathrm{km}^{3}\). The number of sensor nodes is set to 61, where one sink node (\(SN\)) is deployed as the surface sonobuoy, while 60 sensor nodes are deployed in the underwater environment with different depths ranging from 0.01 km to 0.5 km. The transmission radius of sensor nodes (\(r\)) is set to 0.15 km, and it can be changed to other values for diverse experimental purposes, which makes the connection of sensor nodes with the packet delivery from any sensor node to \(SN\) be within four hops. The size of an EPING, EPONG, HELLO, \(HeartB_{evt}\), \(getData\) and ACK control packet is set to \(11B\), \(7B\), \(7B\), \(7B\), \(11B\), \(6B\) or \(6B\), respectively. The robustness factor for the parent-child relation determination is set to 0.7, which means that the geographical distance between parent and child sensor nodes should be no larger than (0.7 \(\times\)\(r\)) = 0.105 km. The smoothing factor adopted for computing the link quality is set to 0.7. The energy consumption for transmitting a data or control packet is set to 2.8 W or 1.5 W, respectively. Generally, one or more events are generated at each time slot, and sensory data for potential events are gathered and routed to \(SN\) through the routing tree. Experiments are conducted over 10, 20 or 30 time slots for evaluation purposes, and these experiments are performed on a desktop with an Intel(R) Core(TM) i5-3470 CPU @ 3.20 GHz, an 8-GB memory and the 64-bit Windows system. ### Experimental Evaluation

Experiments have been conducted for evaluating the performance and efficiency of our event coverage detection and sensory data routing mechanisms.