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In recent years we have seen the emergence of context-aware mobile sensing apps which employ machine learning algorithms on real-time sensor data to infer user behaviors and contexts. These apps are typically optimized for power and performance on the app processors of mobile platforms. However, modern mobile platforms are sophisticated system on chips (SoCs) where the main app processors are complemented by multiple co-processors. Recently chip vendors have undertaken nascent efforts to make these previously hidden co-processors such as the digital signal processors (DSPs) programmable. In this paper, we explore the energy and performance implications of off-loading the computation associated with machine learning algorithms in contextaware apps to DSPs embedded in mobile SoCs. Our results show a 17% reduction in a TI OMAP4 based mobile platform's energy usage from off-loading context classification computation to the DSP core with indiscernible latency overhead. We also describe the design of a run-time system service for energy efficient context inference on Android devices, which takes parameters from the app to instantiate the classification model and schedules the execution on the DSP or app processor as specified by the app.

By combining multiple factors during authentication, a service can provide better assurance of security. However, the users are likely to feel inconvenient, or even discard the service. This paper, therefore, addresses this issue and introduces a novel method, referred to as the Quantified riSk and Benefit adaptive Authentication Factors combination (QSBAF). QSBAF balances the requirements for both security and usability in the authentication of an information system and improves the system's ability to respond quickly to emerging risky events. In QSBAF, the authentication factors can be dynamically combined on the basis of quantified risk, benefit measurements, and combination policies. Furthermore, QSBAF provides an adaptive mechanism, which is driven by history data to justify the measurements of risk and benefit. In this paper, we use the online banking system as a typical scenario to demonstrate the usage of QSBAF. We also implement a prototype of QSBAF to evaluate the performance of its feasibility in real application scenarios.

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We present MiDebug, a web-based Integrated Development Environment (IDE) for embedded system programming with in-browser debugging capabilities. This web application greatly reduces the time and effort required for rapid prototyping of microcontroller based devices.