|
A Mobile Device Prototype Application for the Detection and Prediction of Node Faults in Wireless Sensor Networks
|
|
|
Anthony Marcus1,Ionut Cardei2,Borko Furht3,Osman Salem4,Ahmed Mehaoua5
*1, Florida Atlantic University, Email : amarcu10@fau.edu
2, Florida Atlantic University, Email : icardei@fau.edu
3, Florida Atlantic University, Email : bfurht@fau.edu
4, Université Paris Descartes - LIPADE , Email : osman.salem@parisdescartes.fr
5, Université Paris Descartes - LIPADE , Email : ahmed.mehaoua@parisdescartes.fr
|
|
|
|
Abstract
.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.
|
|
|
|
Keywords
:
wireless sensor networks ; data mining and machine learning ; smartphone
|
|
|
|
URL: http://dx.doi.org/10.7321/jscse.v3.n3.113
|
|
|
|
|