  Various implementations of wireless sensor networks (i.e. personal area-,
wireless body area- networks) are prone to node and network failures by such
characteristics as limited node energy resources and hardware damage incurred
from their surrounding environment (i.e. flooding, forest fires, a patient
falling). This may jeopardize their reliability to act as early warning
systems, monitoring systems for patients and athletes, and industrial and
environmental observation networks. Following the current trend and widespread
use of hand held, mobile communication devices, we outline an application
architecture designed to detect and predict faulty nodes in wireless sensor
networks. Furthermore, we implement our design as a proof of concept prototype
for Android-based smartphones, which may be extended to develop other
applications used for monitoring networked wireless personal area and body
sensors used in other capacities. We have conducted several preliminary
experiments to demonstrate the use of our design, which is capable of
monitoring networks of wireless sensor devices and predicting node faults based
on several localized metrics. As attributes of such networks may change over
time, any models generated when the application is initialized must be updated
periodically such that the applied machine learning algorithm maintains high
levels of both accuracy and precision. The application is designed to discover
node faults and, once identified, alert the user so that appropriate action may
be taken.
