[Paper Review] An Incentive Mechanism for Crowd Sensing with Colluding Agents

Susu Xu, Weiguang Mao+3 more|arXiv (Cornell University)|Sep 13, 2018

Mobile Crowdsensing and Crowdsourcing26 references3 citations

TL;DR

This paper proposes an incentive mechanism called InSensitive Mechanism for crowd sensing that remains effective even when agents collude, ensuring optimal data collection under budget constraints. It achieves up to 30% higher data collection than prior methods in both synthetic and real-world experiments, maintaining strong theoretical guarantees in both homogeneous and heterogeneous settings.

ABSTRACT

Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize drivers (agents) to collect data, with the overall goal of obtaining the maximum amount of data (across multiple vehicles) for a given budget. Past works on this problem consider a setting where each agent operates in isolation---an assumption which is frequently violated in practice. In this paper, we design an incentive mechanism to incentivize agents who can engage in arbitrary collusions. We then show that in a "homogeneous" setting, our mechanism is optimal, and can do as well as any mechanism which knows the agents' preferences a priori. Moreover, if the agents are non-colluding, then our mechanism automatically does as well as any other non-colluding mechanism. We also show that our proposed mechanism has strong (and asymptotically optimal) guarantees for a more general "heterogeneous" setting. Experiments based on synthesized data and real-world data reveal gains of over 30\% attained by our mechanism compared to past literature.

Motivation & Objective

• To address the critical gap in existing crowd sensing mechanisms that assume non-colluding agents, a common but often violated assumption in practice.
• To design an incentive mechanism that maintains high data collection efficiency even when agents can collude strategically.
• To achieve optimal performance in homogeneous settings where all agents have the same threshold, matching the theoretical upper bound of any mechanism with prior knowledge of preferences.
• To provide strong, asymptotically optimal guarantees in heterogeneous settings where agents have varying thresholds and truth-telling cannot be enforced.
• To empirically validate the mechanism’s superiority over state-of-the-art approaches using both synthesized and real-world data.

Proposed method

• Proposes the InSensitive Mechanism, an incentive design that is insensitive to agent collusion, ensuring truthful bidding remains optimal under specific conditions.
• Uses a budget-constrained, multi-round auction framework where agents bid their private thresholds, and the crowdsourcer allocates payments to maximize data collection.
• Employs a priority-based selection rule that ensures agents with lower thresholds are favored when budgets are tight, promoting efficiency.
• Derives theoretical bounds on regret and performance loss in heterogeneous settings, showing the mechanism remains near-optimal even when truth-telling is impossible.
• Applies a novel analysis of agent behavior under collusion, proving that the mechanism maintains strong performance guarantees without requiring ground-truth data or prior knowledge of thresholds.
• Employs numerical evaluation using both synthetic and real-world taxi trajectory data to compare performance against prior mechanisms under identical budget constraints.

Experimental results

Research questions

• RQ1 Can an incentive mechanism be designed to maintain high data collection efficiency in crowd sensing when agents are allowed to collude?
• RQ2 Is it possible to achieve optimal performance in a homogeneous threshold setting where all agents have identical willingness-to-participate thresholds?
• RQ3 How does the mechanism perform in a more realistic heterogeneous threshold setting where agents have varying private thresholds?
• RQ4 What theoretical guarantees can be provided for the mechanism’s performance, especially in terms of regret and budget utilization?
• RQ5 To what extent does the mechanism outperform existing state-of-the-art mechanisms in practice?

Key findings

• The InSensitive Mechanism achieves optimal performance in the homogeneous threshold setting, matching the theoretical upper bound of any mechanism with prior knowledge of agent preferences.
• In the heterogeneous setting, the mechanism provides strong, asymptotically optimal guarantees despite the impossibility of ensuring truthful bidding.
• The mechanism consistently outperforms prior state-of-the-art mechanisms, achieving over 30% higher data collection under the same budget in both synthetic and real-world experiments.
• In cases where the budget is insufficient to pay all agents, the mechanism ensures that at least iN tasks are completed when i agents have thresholds below the budget-per-round limit, with minimal regret.
• The mechanism maintains strong performance even when agents collude, as it is designed to be insensitive to strategic behavior, including coordinated bidding.
• Empirical results show that the mechanism’s performance gain is robust across different data distributions and budget levels, confirming its practical viability.