The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

中文翻译：

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用于人群标记的无线网络中的联合注释器和频谱分配

物联网产生的海量传感数据将为无处不在的人工智能 (AI) 提供燃料，使我们社会从交通运输到医疗保健的运营自动化。然而，无处不在的人工智能的实施需要在通过监督学习训练人工智能模型之前标记大量数据。为了应对这一挑战，我们探索了一个称为无线人群标记的新方向，它涉及将数据下载到许多不完善的移动注释器以进行重复标记，目的是利用无线网络中的多播。在这个跨学科领域，率失真理论和重复标记以提高准确性的原则共同在标记准确性的约束下产生了无线电和注释器资源之间的新权衡。在权衡的基础上，以最大化标记吞吐量为目标，这项工作侧重于编码率、注释器聚类和子信道分配的联合优化，这导致了一个 NP 难整数规划问题。为了设计一种有效的解决方案，我们基于信噪比递减的顺序建立了一个最佳的顺序注释器聚类方案，从而允许通过有效的树搜索找到最佳解决方案。当通道对称时，可以进一步简化此解决方案。或者，优化问题可以被认为是一个背包问题，它可以通过动态规划在伪多项式时间内有效地解决。此外，针对注释者约束和频谱约束情况导出了最优策略。最后的，