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Although blood hemoglobin (Hgb) testing is a routine procedure in a variety of clinical situations, noninvasive, continuous, and real-time blood Hgb measurements are still challenging. Optical spectroscopy can offer noninvasive blood Hgb quantification, but requires bulky optical components that intrinsically limit the development of mobile health (mHealth) technologies. Here, we report spectral super-resolution (SSR) spectroscopy that virtually transforms the built-in camera (RGB sensor) of a smartphone into a hyperspectral imager for accurate and precise blood Hgb analyses. Statistical learning of SSR enables us to reconstruct detailed spectra from three color RGB data. Peripheral tissue imaging with a mobile application is further combined to compute exact blood Hgb content without a priori personalized calibration. Measurements over a wide range of blood Hgb values show reliable performance of SSR blood Hgb quantification. Given that SSR does not require additional hardware accessories, the mobility, simplicity, and affordability of conventional smartphones support the idea that SSR blood Hgb measurements can be used as an mHealth method.

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Mutual collaboration plays a vital role in sharing of resources in an ad hoc network of handheld devices in a pervasive computing environment. Effective sharing of resources is facilitating tiny pervasive devices to benefit from situations which otherwise would not have been possible due to several limitations (such as poor storage and computational capability). An unavoidable consequence of this aspect is opening the door for security breaches. Trust is the weapon which is used to fight against security violations by restricting malicious nodes from participating in any interaction. Here, we present a context specific and reputation based trust model along with a brief survey of current trust models. To the best of our knowledge, our model is the first formal omnipresent trust model for pervasive computing, which can be used universally. This paper presents a recommendation protocol that provides a multi-hop recommendation capability and a flexible behavioral model to handle interactions. This paper also illustrates the implementation and evaluation of the omnipresent formal trust model.

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