This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitor ing. Data is collected using physiological sensors and trans ported back to the middleware using a smart phone. The key to MediAlly's energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring (ATDM) paradigm,where data collection episodes are triggered only when the subject is determined to possess a specified context. MediAlly supports the on-demand collection of contextual provenance using a novel low-overhead provenance collection sub-system.The behaviour of this sub-system is configured using an application-defined context composition graph. The resulting provenance stream provides valuable insight while interpreting the 'episodic' sensor data streams. The paper also describes our prototype implementation of MediAlly using commercially available devices.
The world is rapidly getting connected. Commonplace everyday things are providing and consuming software services exposed by other things and service providers. A mashup of such services extends the reach of the current Internet to potentially resource constrained "Things", constituting what is being referred to as the Internet of Things (IoT). IoT is finding applications in various fields like Smart Cities, Smart Grids, Smart Transportation, e-health and e-governance. The complexity of developing IoT solutions arise from the diversity right from device capability all the way to the business requirements. In this paper we focus primarily on the security issues related to design challenges in IoT applications and present an end-to-end security framework. Index Terms-Internet of Things (IoT); Security; Resource constrained devices; End-to-end (E2E) security.
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