EyePhy: Detecting Dependencies in Cyber-Physical System Apps due to Human-in-the-Loop

Abstract

As app based paradigms are becoming popular, millions of apps are developed from many domains including energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by 2015. Many of these apps are Cyber-Physical System (CPS) apps. In addition to sensing, communication, and computation, they perform interventions to control human physiological parameters, which can cause dependency problems as multiple interventions of multiple apps can increase or decrease each others effects, some of which can be harmful to the user. Such dependency problems occur mainly because each app is unaware about how other apps work and when an app performs an intervention to control its target parameters, it may affect other physiological parameters without even knowing it. We present EyePhy, a system that detects dependencies across interventions by having a closer eye on the physiological parameters of the human in the loop. To do that, EyePhy uses a physiological simulator HumMod that can model the complex interactions of the human physiology using over 7800 variables. EyePhy reduces app developers’ efforts in specifying dependency metadata compared to state of the art solutions and offers personalized dependency analysis for the user. We demonstrate the magnitude of dependencies that arise during multiple interventions in a human body and the significant ability of detecting these dependencies using EyePhy.