To evaluate the computational efficiency of the proposed algorithm outlined in Table 2, we use the algorithm to evaluate the reliability of the WSN deployments in Table 4.
In order to examine the sensitivity of R(S) of a given deployment to changes in the probabilities of failure of its constituent SNs, we arbitrarily choose one of the deployments in Table 4, deployment S3-D1, assumesume all of its 21 SNs are of type 1 only.
Fig. 6 Comparison between the reliability of WSN deployments in Table 4 evaluated using the proposed three-mode SN model and the two-model SN model adopted in existing studies in [8], [11], [14], and [16] for the deployment scenarios 1 through 5 (a e).
As can be observed from Fig. 6, the value of R(S) evaluated using the two-mode SN model is significantly smaller than that using the proposed three-mode model for all the deployments in Table 4, exceeding 6% for some deployments.
In this study, we consider the calibration deployment given in Table 3.7 in [14], where macro BSs offer basic coverage, and micro BSs are used for capacity-demanding applications and local coverage.
Fig. 7 Reliability R(S) for the deployment S3-D1 in Table 4 at different probabilities of failure of the sensor, transceiver, processor, and battery, assuming all deployed SNs are of type 1.
In general, issues related to manpower numbers, training, service capacity, and deployment summarized in Table 1 apply to the early CHPS implementation era.
To demonstrate the significance of modeling the SNs as three-mode (on, relay, and off) devices, we evaluate the reliability of the deployments presented in Table 4 using the reliability metric proposed in [16], which adopts the conventional two-mode (on and off) SN model.
A description of the four wave measurement sites considered for deployment is provided in Table 1.
Roche researchers use SDTM variables in our deployment as identified in Table 1.
