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This paper presents a novel unsupervised multi-source domain adaptation approach, named as Coupled Local-Global Adaptation (CLGA). At the global level, in order to maximize the adaptation ability, CLGA regards multiple domains as a unity, and jointly mitigates the gaps of both marginal and conditional distributions between source and target dataset. At the local level, with the intention of maximizing the discriminative ability, CLGA investigates the relationship among distinctive domains, and exploits both class and domain manifold structures embedded in data samples. We formulate both local and global adaptation in a concise optimization problem, and further derive an analytic solution for the objective function. Extensive evaluations verify that CLGA performs better than several existing methods not only in multi-source adaptation tasks but also in single source scenarios.

Mobile crowdsensing is an emerging approach to data collection by exploiting the sensing abilities offered by smart phones and users’ mobility. Data collection can be implemented by exploiting the forwarding opportunities given by the contacts between nodes. However, as cell phones are still resource constrained, most people are socially selfish so that they may not always cooperate with each other in data collection. In this paper, we propose a routing protocol, called Accept aNd Tolerate (ANT), which is tailored for data collection in a social environment with selfish individuals. ANT works by accepting and tolerating social selfishness as an unavoidable human characteristic. It makes relay selection based on nodes’ contacts and their willingness to cooperate. The cooperative willingness of selfish nodes is measured rationally according to the reciprocity relationship between nodes and their resource constraints. Through assessing the worthiness of carrying and forwarding a packet, ANT proposes a buffer management scheme and makes forwarding decisions. Simulations based on real traces show that ANT achieves better performance under resource-constrained circumstances than other comparable approaches.

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