clearly exceed the given thresholds or not. In other words, a temperature increase of 25 Kelvin exceeding the given threshold by 20 Kelvin is more important than the same increase exceeding the threshold by 5 Kelvin only. That especially holds true, if events are evaluated in terms of a mission. This temperature increase of 25 Kelvin was simulated in an example scenario shown in Figure 5.2. Here, the same sequence of temperature readings are used to determine the iS without a threshold, see diagram (a), as well as with different thresholds (b)-(d). These clearly show that the iS approach can detect significant changes in sensor read-ings. Moreover, the iS improves the detection of the temperature increase with a higher amplitude than the readings do. If thresholds are additionally taken into account, the iS even grows in proportion to the exceedance of the thresh-old. In addition, significant changes may only be announced when the respective measurements also exceed the assigned thresholds.
5.2 Scenario
Based onEDT, the effects of this approach are exemplified at the introduced fire detection scenario. As a reminder, Figure5.3depicts the respective EDT, which was already presented in Figure 4.2. After having determined all indicators iS
for single sensing capabilities, i.e., the EDT-nodes 3, 6 and 9 in this example, the next step is to merge these for complex events. According to the EDT, the indicators are merged at AND and OR tree-nodes. AND nodes take the average iS values of child nodes, whereas OR nodes apply the maximumiS of both child nodes. For the sake of completeness, NOT nodes in EDTs directly adopt the iS
of their child node. In the example scenario, the EDT-node 5 gains the average value of the iS’s determined at the EDT-nodes 6 and 9. Finally, the iS of the complex event fire, which is attached to the root node, is the maximum of theiS
values from the carbon monoxide readings (EDT-node 2) and the average of the smoke and temperature iS values (EDT-node 5).
The simulations applied two large-scale data sets (SDC07 and SDC08)1 from series of real fire tests in a house equipped with one sensor in every room, which have been recorded by the National Institute of Standards and Technology (NIST) [45]. Amongst other things, these sets contain temperature, smoke and carbon monoxide sensor readings2. Here, one set of data of a flaming fire and one of a smouldering fire were used. A sliding window size of ten was applied to de-termine the variance of sensor readings of the last 40 seconds because of an event evaluation rate of four seconds. The iS-based approach and the usual
de-1http://smokealarm.nist.gov/
2The simulations results presented in Chapter4would had greatly benefit from the avail-ability of such large sets of data of real deployments. Unfortunately, the number of sensors and the density of those is far from being enough to simulate aWSN and to show the abilities of using theEDTs and voting.
112 CHAPTER 5. INDICATING THE SIGNIFICANCE OF DATA READINGS
Figure 5.3: EDT for a fire fighting system using carbon monoxide (CO), smoke (S) and temperature (T) detectors. This is just to remind the EDT already introduced in Figure 4.2.
tection by thresholds were simulated at two different sensors, one sensor was directly located above the monitored fire and one sensor was located in a side room. The simulation results of both approaches are presented in Figure 5.4. It shows the local detection results of the two sensors in the flaming fire scenario, see the diagrams 5.4(a) and 5.4(b), and the smouldering fire scenario in the di-agrams 5.4(c) and5.4(d). The threshold-based detection distinguishes between two states, which is 1/TRUE for an exceeded threshold (fire alarm) and 0/FALSE otherwise (no alarm). The iS-based approach only signals significant changes in sensor readings, but in both scenarios it indicates the upcoming fire event much earlier than the thresholds are exceeded. In particular, the iS-based approach indicates the flaming fire 88 seconds and the smouldering fire 48 seconds before the threshold-based method triggers the alarm. Thus, the iS-based detection offers great advantages and gains valuable time for fire fighting systems. The difference between both methods is less in the smouldering fire scenario. Here the detection almost solely depends on the smoke readings, which increase very slowly and hence, are less recognised by the indicator-based approach. According to this, a proper combination of both detection methods is the safest solution, especially for mission- and safety-critical applications. Note, the slow increase is also the reason for requiring more than the double monitoring time before the smouldering fire is detected.
However, the current results strongly indicate a great benefit in event detec-tion quality. On the one hand, the iS can signal significant changes in sensor readings before these reach a critical point. Thus, necessary processes or further analyses can be triggered in sufficient time. On the other hand, the iS is even fully functional without predefined thresholds. Thresholds are introduced to as-sign a higher weight to event relevant readings. Using those thresholds results in
5.2. SCENARIO 113
Figure 5.4: Local detection results of the flaming fire scenario at the sensor above the fire area (a) and in the side room (b). For the smouldering fire scenario, (c) displays the readings of the sensor above the fire area and (d) the respective readings in the side room. The iS-based approach signals significant changes in sensor readings earlier than the thresholds are exceeded. Hence, this approach indicates upcoming fires before the fire alarm is triggered.
a higher and more noticeable amplitude. To give a proof of concept, the four sce-narios have been simulated again without predefined thresholds. The results are given in Figure5.5. In comparison to5.4(a), diagram5.5(a) shows the calculation results at the same sensor with equal inputs but without considering thresholds.
The same applies to the other diagrams 5.4(b) and 5.5(b), 5.4(c) and5.5(c) and 5.4(d) and5.5(d). The fire events are still clearly visible even without predefined thresholds, especially in the smouldering fire scenarios (c) and (d). The number of available test results is yet insufficient to announce the iS-based approach as a suitable stand-alone event detection method, but the good initial results may be confirmed when evaluating further applications.
Despite all good results, there is a short remark necessary. Running the simulations with the provided test data discovered a small pitfall when exactly using the introduced calculation. If sequential measurements are equal for a long time, as it is not unusual for surveillance scenarios, it may occur that the
114 CHAPTER 5. INDICATING THE SIGNIFICANCE OF DATA READINGS
Figure 5.5: Local detection results without predefined thresholds. Please compare the results of (a) to5.4(a) as well as diagram (b) to5.4(c). TheiS-based approach also allows to indicate upcoming fire events without predefined thresholds.
resulting variance is extremely small. Depending on the accuracy to be used for calculation, the variance may even become zero in the worst case due to rounding. In the given test data that case occurred during calculation of the iS for the carbon monoxide readings, which are pretty equal during the initial monitoring phase before thefireis triggered. Hence, even small changes in current readings may result in a very high iS. There exist two possible solutions to cope with that at implementation. The easiest solution is to make sure to only use variables providing enough accuracy for calculation, e.g., by using double precision variables instead of integer. This is obviously not the best solution for WSN since it increases the cost of processing and memory. Instead, a standard deviation used as lower bound for the calculated variance is introduced. Due to the fact that sensor technology always have such standard deviation as potential error of measurement, it is usually given by the manufacturer of the sensing device anyway. Hence it is proposed to include the potential error of measurement as lower bound at implementation. In the given examples the presented problem was properly solved by applying a lower pound of 0.01 ppm.