Voting enhances the reliability of detection but in turn requires an overhead in time and energy consumption. Voting might be triggered in case of local positive event evaluation or if a measured value deviates more than a certain percent-age from previous measurements. The latter case is presented and discussed in Chapter5. Here, the voting in case of local events is of interest. All nodes within the voting region are authorised to participate in the voting and may submit their own event detection result to the initiating node. The initiating node col-lects all votes and counts the positive as well as the negative ones. Finally this node decides based on the majority of the votes whether or not an event was detected. The initiating node may trigger further actions if necessary. The pre-sented algorithm also interprets a tie situation in voting as a positive result, since it shall rather detect a non-occurred event than to miss one. This is considered as necessary issue especially for mission- and safety-critical applications.
To reduce the energy consumption, Reactive Majority Voting (RMV) is in-troduced where voting is done if and only if it is necessary. A reactive voting algorithm provides a high efficiency while achieving the same or similar relia-bility as other approaches, even in case of faults. Existing approaches make use of fixed voting regions as well as selected nodes for decision making, which collect and evaluate all measurements in their voting region. Fixed voting re-gions are usually built in the initial phase or are given at deployment and hence, are not flexible enough to reliably detect different phenomena of varying spatial resolution. Further, fixed voting regions cannot suitably cope with changes in the environment and varying node density and even less with mobile nodes. In
3.3. Reactive Majority Voting (RMV) 37
Figure 3.3: Comparison of applying active MVandRMV on a sequence of event detection intervals. Active MV requires to perform voting at each detection interval, even if there exists no noticeable phenomenon (event). In contrast to that, RMV needs to perform a voting on event only and hence, significantly reduces the number of transmission and voting procedures. According to this, RMV provides a high energy efficiency.
existing approaches only the decision making node finally is able to identifies the phenomena and hence, these approaches require to perform a voting at each detection interval even if no event is indicated by current sensor readings.
In contrast to that, establishing unfixed local voting regions around the nodes enables each node to independently trigger a voting on demand. On detecting an event, RMV requests all nodes in the assigned voting region to perform an usual MV. In the case of negative local event evaluation results, RMV allows to abandon voting and switch to the sleeping mode immediately. That significantly reduces the energy consumption. This is underlined by Figure 3.3, which com-pares the usual (active)MVto the proposedRMV. It displays application of both methods in a sequence of event detection intervals considering the cases of non-event and non-event (in andin+3), which clearly shows the benefit of RMV. To give a proof of concept, the possible evaluation results are analysed in the following.
It is discussed how RMV detects and overcomes failures efficiently. Local event evaluation may result in four possible states, depending on the actual existence of the phenomenon to be measured. These are:
38 CHAPTER 3. COMPLEX FAULT TOLERANT EVENT DEFINITION
Correct positives are events identifying an existing phenomenon. In that case, voting cannot be avoided to distinguish these from false positives but should result in positive voting evaluation as well.
False positives are erroneous reported events of non-existing phenomena. Wrong-fully detected events are usually identified by voting and are overruled by other devices.
Correct negatives rightly identify no noticeable event. Hence, also other devices usually do not identify an event. Thus, voting becomes useless since it would result in a negative voting result as well.
False negatives are wrongly non-identified events caused by faults in sensor nodes or sensing devices. These events are most likely detected by other devices in the event region. Hence, these devices trigger the necessary voting, which should identify faulty state. A special case of this is the failure of a voting initiator while the voting is still in progress. If the event actually exists, the other nodes participating in the voting will fire the event after their next detection interval. This introduces a delay of only one detection interval.
To conclude, RMV offers the same or similar reliability but gets by with significantly less voting procedures. In fact, it reduces the number of voting pro-cesses by a factor of 1/pt, whereas pt is the event probability. For surveillance and event-based applications it is considered sufficient to evaluate only detected events. This allows to reject False positives and to identify the nodes that most probably correctly detected an event. In addition, the ESL enables the appli-cation engineer to fine-tune voting constraints or even to completely switch off the voting for every event. That allows for more economic power consumption than the majority of the already published ideas, in which voting is enabled by default. Customised majority voting provides more precise and flexible detec-tion of events than voting within fixed regions, but could even be improved by customising the voting algorithm. Hence, the ESL may be extended to provide other voting algorithms like TIBFIT [34] orCWV [64] as well.
RMV does not rely on a certain failure model. The focus is on the cor-rectness of measured events as well as to reduce the energy consumption. There already exist voting algorithms that cope with stucked measurements and Byzan-tine faults [14, 27, 41]. Those require an enormous overhead in processing and communication by at least a factor of two or three compared to usualMV, which is yet inefficient compared to RMV. Further work intends to integrate selection means for the voting algorithm that allows the user to choose the one that best meets the application requirements regarding performance and communication overhead.