[Paper Review] Auction-based Incentive Mechanisms for Dynamic Mobile Ad-Hoc Crowd Service

Honggang Zhang, Benyuan Liu+2 more|arXiv (Cornell University)|Mar 23, 2015

Mobile Crowdsensing and Crowdsourcing17 references3 citations

TL;DR

This paper proposes M-CHAIN, a truthful, computationally efficient multi-market dynamic double auction mechanism for Dynamic Mobile Ad-hoc Crowd Service (DMACS) systems. It enables fair trading among users in overlapping, dynamically changing groups by supporting multi-group, multi-market transactions, and achieves up to 20% efficiency in highly dynamic and sparse real-world user patterns.

ABSTRACT

We investigate a type of emerging user-assisted mobile applications or services, referred to as Dynamic Mobile Ad-hoc Crowd Service (DMACS), such as collaborative streaming via smartphones or location privacy protection through a crowd of smartphone users. Such services are provided and consumed by users carrying smart mobile devices (e.g., smartphones) who are in close proximity of each other (e.g., within Bluetooth range). Users in a DMACS system dynamically arrive and depart over time, and are divided into multiple possibly overlapping groups according to radio range constraints. Crucial to the success of such systems is a mechanism that incentivizes users' participation and ensures fair trading. In this paper, we design a multi-market, dynamic double auction mechanism, referred to as M-CHAIN, and show that it is truthful, feasible, individual-rational, no-deficit, and computationally efficient. The novelty and significance of M-CHAIN is that it addresses and solves the fair trading problem in a multi-group or multi-market dynamic double auction problem which naturally occurs in a mobile wireless environment. We demonstrate its efficiency via simulations based on generated user patterns (stochastic arrivals, random market clustering of users) and real-world traces.

Motivation & Objective

• To address the challenge of incentivizing user participation in dynamic, mobile, ad-hoc crowd services where users both provide and consume resources.
• To design a mechanism that ensures fairness, truthfulness, and computational efficiency in multi-market, overlapping group environments typical of mobile ad-hoc networks.
• To overcome the limitations of prior single-market dynamic double auction mechanisms in real-world, highly dynamic user environments.
• To evaluate the trade-off between truthfulness and efficiency in multi-market dynamic double auctions using both synthetic and real-world user traces.

Proposed method

• Design a multi-market, dynamic double auction mechanism (M-CHAIN) that supports overlapping user groups formed by wireless proximity (e.g., Bluetooth/Wi-Fi Direct).
• Model user interactions across time periods, where each period corresponds to a 5-minute interval of proximity data from real-world traces.
• Use a truthful auction mechanism based on principles from Myerson and Satterthwaite’s impossibility result, ensuring that bidders are incentivized to report true valuations.
• Implement a greedy online matching algorithm with truthfulness guarantees, balancing efficiency and strategic behavior in dynamic settings.
• Apply the mechanism to both synthetic user patterns (stochastic arrivals and random clustering) and real-world data from the MIT Reality Mining project.
• Evaluate performance using metrics like efficiency, truthfulness cost, and market participation under varying levels of system dynamics and sparsity.

Experimental results

Research questions

• RQ1 How can a dynamic double auction mechanism be designed to support multiple overlapping user groups in a mobile ad-hoc environment?
• RQ2 What trade-offs exist between truthfulness and efficiency in multi-market dynamic double auctions, and how can they be quantified?
• RQ3 Can a truthful and computationally efficient auction mechanism maintain reasonable efficiency in highly dynamic and sparse real-world user mobility patterns?
• RQ4 How does the performance of M-CHAIN compare to non-truthful baselines in terms of efficiency and fairness under realistic user behavior?

Key findings

• M-CHAIN achieves truthfulness, feasibility, individual rationality, no-deficit, and computational efficiency, satisfying all key economic and algorithmic properties for auction mechanisms.
• In synthetic simulations, M-CHAIN maintains high efficiency levels across various user arrival and clustering patterns, demonstrating robustness to dynamic membership.
• The price of truthfulness guarantee is significant—efficiency loss can be large to ensure truthful bidding, consistent with Myerson and Satterthwaite’s impossibility result.
• Despite extreme sparsity and dynamism in real-world traces from the MIT Reality Mining project, M-CHAIN sustains a reasonable efficiency level of approximately 20%.
• In systems with only 2 or fewer users per group (70% of the time in October 2004 data), trade is rare, but M-CHAIN still enables transactions when possible.
• The mechanism remains effective even when group structures change frequently and user clusters are small, indicating resilience to real-world mobility patterns.