English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The massive sensing data generated by Internet-of-Things will provide fuelfor ubiquitous artificial intelligence (AI), automating the operations of oursociety ranging from transportation to healthcare. The realistic adoption ofthis technique however entails labelling of the enormous data prior to thetraining of AI models via supervised learning. To tackle this challenge, weexplore a new perspective of wireless crowd labelling that is capable ofdownloading data to many imperfect mobile annotators for repetition labellingby exploiting multicasting in wireless networks. In this cross-disciplinaryarea, the integration of the rate-distortion theory and the principle ofrepetition labelling for accuracy improvement gives rise to a new tradeoffbetween 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, annotatorclustering, and sub-channel allocation, which results in an NP-hard integerprogramming problem. To devise an efficient solution approach, we establish anoptimal sequential annotator-clustering scheme based on the order of decreasingsignal-to-noise ratios. Thereby, the optimal solution can be found by anefficient tree search. Next, the solution is simplified by applying truncatedchannel inversion. Alternatively, the optimization problem can be recognized asa knapsack problem, which can be efficiently solved in pseudo-polynomial timeby means of dynamic programming. In addition, exact polices are derived for theannotators constrained and spectrum constrained cases. Last, simulation resultsdemonstrate the significant throughput gains based on the optimal solutioncompared with decoupled allocation of the two types of resources.

Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling