[Paper Review] Fair Airtime Allocation for Content Dissemination in WiFi-Direct-Based Mobile Social Networks

Zhifei Mao, Yuming Jiang|arXiv (Cornell University)|Jun 12, 2017

Cooperative Communication and Network Coding3 citations

TL;DR

This paper proposes a fair airtime allocation scheme for content dissemination in WiFi-Direct-based mobile social networks using a generalized Nash bargaining game to ensure fairness in content dissemination rates. The approach models node cooperation and asymmetric contributions, achieving a unique optimal solution via a low-complexity algorithm and a time-slotted scheduling protocol for implementation.

ABSTRACT

The vast penetration of smart mobile devices provides a unique opportunity to make mobile social networking pervasive by leveraging the feature of short-range wireless communication technologies (e.g. WiFi Direct). In this paper, we study local content dissemination in WiFi-Direct-based mobile social networks (MSNs). We propose a simple GO-coordinated dissemination strategy, as WiFi Direct does not originally support content dissemination. Due to mobility and the short transmission range, the duration of nodes in contact tends to be limited and consequently they compete for the limited airtime to disseminate their own data. Therefore, fair allocation of the limited airtime among the nodes is required. We focus on fairness in content dissemination rate, which is a key application-layer metric, rather than fairness in throughput or airtime and formulate the allocation problem as a generalized Nash bargaining game wherein the nodes bargain for a share of the limited airtime. The game is proved to have a unique optimal solution, and an algorithm with low complexity is designed to find the optimal solution. Furthermore, we propose a detailed scheduling approach to implement the optimal solution. We also present numerical results to evaluate the Nash bargaining based allocation and scheduling.

Motivation & Objective

• To address the challenge of fair airtime allocation among mobile nodes in WiFi-Direct-based mobile social networks due to limited contact durations and mobility.
• To model content dissemination fairness not in terms of throughput or airtime, but in terms of dissemination rate—the key application-layer metric.
• To account for asymmetric node contributions, particularly the Group Owner (GO) that forwards data for others, which incurs higher resource costs.
• To design a game-theoretic solution that guarantees fairness while maximizing social welfare in content dissemination.
• To implement the optimal allocation through a time-slotted scheduling mechanism enabling efficient, round-robin data transmission.

Proposed method

• The authors model the airtime allocation problem as a generalized Nash bargaining game, where nodes negotiate for shares of limited airtime to maximize fairness in content dissemination rates.
• The game incorporates individual node constraints (e.g., data volume, transmission rate) and the GO’s role in relaying data, reflecting asymmetric contributions.
• A key equation (15) defines the optimal allocation as the solution to a system of equations involving inverse latency functions and cumulative time constraints.
• The solution is proven to be unique and computable via a recursive algorithm (equation 4), which iteratively computes each node’s allocated time based on remaining available airtime.
• A time-slotted scheduling protocol is designed to implement the optimal allocation, dividing time into small slots and enabling round-robin transmission to ensure fairness.
• The algorithm is shown to have low computational complexity, making it suitable for real-time deployment on mobile devices.

Experimental results

Research questions

• RQ1 How can fair airtime allocation be achieved in WiFi-Direct-based mobile social networks where contact durations are short and nodes have asymmetric roles?
• RQ2 What game-theoretic framework can effectively model fairness in content dissemination rate rather than throughput or airtime?
• RQ3 Can a generalized Nash bargaining game yield a unique and socially optimal solution that accounts for the GO’s increased resource burden?
• RQ4 How can the optimal solution be computed efficiently with low computational complexity for mobile deployment?
• RQ5 What scheduling mechanism can reliably implement the optimal airtime allocation in practice?

Key findings

• The generalized Nash bargaining game formulation ensures a unique optimal solution for fair airtime allocation, proven through mathematical analysis of the system of equations.
• The proposed algorithm computes the optimal allocation in a recursive, low-complexity manner, making it suitable for mobile devices with limited processing power.
• The solution accounts for the GO’s higher resource usage by incorporating the factor (1+βi) to reflect the cost of relaying data for other nodes.
• Numerical results demonstrate that the Nash bargaining-based allocation achieves higher fairness in dissemination rates compared to proportional or max-min fairness schemes.
• The time-slotted scheduling approach successfully implements the optimal allocation, enabling fair and efficient data dissemination in a round-robin fashion across nodes.
• The analysis confirms that equal throughput or airtime does not lead to equal dissemination rates due to the GO’s forwarding role, validating the need for rate-based fairness.