An Online Reinforcement Learning Approach to Quality-Cost-Aware Task Allocation for Multi-Attribute Social Sensing

TL;DR

This paper introduces an online reinforcement learning method for dynamic task allocation in social sensing, balancing data quality and sensing costs amid complex multi-attribute constraints and nonuniform costs.

Contribution

It proposes the QCO-TA scheme, a novel reinforcement learning approach that addresses online responsiveness, multi-attribute optimization, and nonuniform costs in social sensing.

Findings

QCO-TA outperforms existing methods in accuracy and cost.

The scheme adapts effectively to large dynamics in social sensing data.

Real-world evaluation confirms its practical advantages.

Abstract

Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy (i.e., decide when and where to collect sensing data) to achieve an optimized tradeoff between the data quality and the sensing cost. While recent progress has been made to tackle similar problems, three important challenges have not been well addressed: (i) "online task allocation": the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables in social sensing; (ii) "multi-attribute constrained optimization": minimizing the overall sensing error given the dependencies and constraints of multiple attributes of the measured variables is a non-trivial problem to solve; (iii) "nonuniform task allocation cost": the task allocation cost in social sensing often has a nonuniform distribution which adds additional complexity to the optimized task allocation problem. This paper develops a Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme to address the above challenges using a principled online reinforcement learning framework. We evaluate the QCO-TA scheme through a real-world social sensing application and the results show that our scheme significantly outperforms the state-of-the-art baselines in terms of both sensing accuracy and cost.