MASU: A Framework that Supports Distributed Pervasive Data Sensing

ESUNLY MEDINA; RODRIGO SANTOS; DAVID LÓPEZ; SERGIO F. OCHOA

SENSORS

MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL-MDPI

Lugar: Basel; Año: 2016 vol. 16 p. 1062 - 1089

1424-8220

p { margin-bottom: 0.08in; direction: ltr; color: rgb(0, 0, 0); line-height: 0.24in; text-align: justify; }p.western { font-size: 12pt; }p.cjk { font-family: "Times New Roman"; font-size: 12pt; }p.ctl { font-family: "Times New Roman"; }a:link { color: rgb(0, 0, 255); } Pervasivedata sensing is a big and transversal issue for several researchareas and application domains. It allows identifying people behaviourand patterns without overwhelming the monitored persons. Althoughthere are many pervasive data sensing applications, they are focusedon addressing specific problems in a single application domain;therefore, they are not easy to generalize or reuse. On the otherhand, the platforms for supporting pervasive data sensing imposerestrictions to the devices and operational environments that makethem unsuitable to monitor loosely-coupled or fully distributed work.In order to help address this challenge this paper present aframework that supports distributed pervasive data sensing in ageneric way. Developers can use this framework to easy theimplementations of their applications, reducing thus complexity andeffort in such an activity. The framework was evaluated usingsimulations and also through and empirical test, and the obtainedresults would be indicating that it is useful to support such asensing activity in loosely-coupled or fully distributed workscenarios.