  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 realistic adoption of
this technique however entails labelling of the enormous data prior to the
training of AI models via supervised learning. To tackle this challenge, we
explore a new perspective of wireless crowd labelling that is capable of
downloading data to many imperfect mobile annotators for repetition labelling
by exploiting multicasting in wireless networks. In this cross-disciplinary
area, the integration of the rate-distortion theory and the principle of
repetition labelling for accuracy improvement gives 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, the optimal solution can be found by an
efficient tree search. Next, the solution is simplified by applying truncated
channel inversion. 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, exact polices are derived for the
annotators constrained and spectrum constrained cases. Last, simulation results
demonstrate the significant throughput gains based on the optimal solution
compared with decoupled allocation of the two types of resources.
