Considering that many applications may only want to acquire the critical point information, such as as maxima, minima, and flection points, according to the above analysis, therefore, the energy cost of collecting the sensory data from the monitored physical world can be further reduced.
The data management system of WSNs manages perceptible data from the monitored area and answers queries from users or applications.
A Nagios service check consists of obtaining the relevant data from the monitored host and then checking that value against a expected value or range of values; raising an alert if an unexpected value is detect.
Currently, it supposes that the sensor nodes sense and sample the data from the monitored environment with equal sampling frequency in most of traditional applications, and the sensory data generated by WSNs is regarded as a set of discrete values.
Although data redundancy may be achieved in some configurations of scalar sensors, which could lead us to avoid costly reliable transmissions, some nodes may have unique perspectives or exclusively retrieve a particular data from the monitored field [10, 11], requiring some level of reliability.
Thus, data from the monitored air pollutant exhibiting the best correlation in each cohort [Supplemental Material, Table 2 (http://dx.doi.org/10.1289/ehp.1002918)] were used to adjust for seasonal variability, as in previous studies (Aguilera et al. 2009; Slama et al. 2007).
