Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks

TL;DR

This paper proposes a novel hybrid service trading framework for mobile crowdsensing networks, utilizing stable matching mechanisms in both futures and spot trading modes to optimize task assignment and ensure service quality.

Contribution

It introduces a hybrid trading paradigm with new matching mechanisms (OIA3M, O3M, OMOM) for dynamic task assignment, addressing long-term and real-time resource fluctuations.

Findings

Mechanisms satisfy stability, rationality, fairness, efficiency

Effective in practical network settings with good performance

Handles resource fluctuations and service quality violations

Abstract

By opportunistically engaging mobile users (workers), mobile crowdsensing (MCS) networks have emerged as important approach to facilitate sharing of sensed/gathered data of heterogeneous mobile devices. To assign tasks among workers and ensure low overheads, a series of stable matching mechanisms is introduced in this paper, which are integrated into a novel hybrid service trading paradigm consisting of futures trading mode and spot trading mode to ensure seamless MCS service provisioning. In the futures trading mode, we determine a set of long-term workers for each task through an overbooking-enabled in-advance many-to-many matching (OIA3M) mechanism, while characterizing the associated risks under statistical analysis. In the spot trading mode, we investigate the impact of fluctuations in long-term workers' resources on the violation of service quality requirements of tasks, and formalize a spot trading mode for tasks with violated service quality requirements under practical budget constraints, where the task-worker mapping is carried out via onsite many-to-many matching (O3M) and onsite many-to-one matching (OMOM). We theoretically show that our proposed matching mechanisms satisfy stability, individual rationality, fairness and computational efficiency. Comprehensive evaluations also verify the satisfaction of these properties under practical network settings, while revealing commendable performance on running time, participators' interactions, and service quality.