RESUMO
Mobile crowdsensing leverages the ubiquitous sensors of smart devices to facilitate various sensing applications. Users who participate in contributing data usually get rewards from the task requester, while there is a potential risk that someone would preempt the task and provide a forged sensing report for seeking revenue with minimal effort. Thus, trust assessment is essential to identify those irregular sensing reports. The existing methods mainly consider users' reputations and estimate the trustworthiness upon the difference from the aggregated result. However, they still face a severe problem when a majority of reports are invalid or low-quality caused by the repeated submission, e.g., a user can switch multiple accounts on a single device to repeatedly submit forged reports. To tackle this problem, we design a trust assessment scheme with an enhanced device fingerprinting algorithm. Briefly, to reduce the influence of the repeated sensing reports, we first compute their unique fingerprints derived from the intrinsic characteristics of sensors and assign an initial trust weight for each report. Then, to improve the accuracy of the assessment, we further compute the similarity of the reports to obtain their final trust values. Extensive evaluations are conducted to justify the effectiveness of our proposed design.
RESUMO
Multilayers of InGaAs nanostructures are grown on GaAs(210) by molecular beam epitaxy. With reducing the thickness of GaAs interlayer spacer, a transition from InGaAs quantum dashes to arrow-like nanostructures is observed by atomic force microscopy. Photoluminescence measurements reveal all the samples of different spacers with good optical properties. By adjusting the InGaAs coverage, both one-dimensional and two-dimensional lateral ordering of InGaAs/GaAs(210) nanostructures are achieved.
