Figure 4 Shortest path representation for finding maximum detection probability at time at k th mobile node.
In the following, we consider the problem of finding the smallest set of mobile nodes in order to maintain the maximum detection probability achieved by time is greater than some threshold value.
To assess the performance of such networks, two statistical indicators are therefore designed: the detection probability, computed on a large number of possible source locations, and the saturation time, which is the time when the maximum detection probability has been reached.
In this paper, we present a method to determine the breach paths and the deployment quality defined as the minimum of the maximum detection probabilities on the breach paths in the presence of obstacles.
We consider only the NP test criterion, which aims at maximizing P d,k with the constraint P f,k ≤ P f,target, or alternatively minimizing P f,k with the constraint P d,k ≥P d,t a r g e t, where P f,target and P d,target represent the tolerable maximum false alarm probability and minimum detection probability, respectively.
Since the CSS algorithm belongs essentially to the Bayesian rule, in order to maximum the total detection probability P D, the false alarm probability has been hidden in the P D, where P f will be fixed hardly.
Comparison of maximum detection ranges suggests that detection probabilities of small insects are indeed rather similar across bats.
Second, we will validate whether the detection probability is maximum with the above obtained parameters.
Equivalently, we need to find the best direction that th mobile node should move at time, for and, to obtain the maximum (over all possible movements) detection probability at time.
Furthermore, we also judge whether the obtained detection probability is the maximum.
One specific drawback to the method of Bornato et al. (8) is the detection probability to be maximum when P fa is fixed, the second threshold has to be chosen as equal to "1".
