A Distributed Satisfactory Content Delivery Scheme for QoS Provisioning in Delay Tolerant Networks

TL;DR

This paper proposes a distributed content delivery scheme in Delay Tolerant Networks using a satisfaction game model, incentive mechanisms, and learning algorithms to ensure QoS with energy-efficient cooperation.

Contribution

It introduces a novel satisfaction equilibrium framework and learning algorithms for incentivized content forwarding in DTNs, addressing cooperation dilemmas.

Findings

The satisfaction equilibrium ensures delivery probability thresholds.

The proposed algorithms effectively reach equilibrium strategies.

Numerical results demonstrate improved QoS and energy efficiency.

Abstract

We deal in this paper with the content forwarding problem in Delay Tolerant Networks (DTNs). We first formulate the content delivery interaction as a non-cooperative satisfaction game. On one hand, the source node seeks to ensure a delivery probability above some given threshold. On the other hand, the relay nodes seek to maximize their own payoffs. The source node offers a reward (virtual coins) to the relay which caches and forwards the file to the final destination. Each relay faces the dilemma of accepting/rejecting to cache the source's file. Cooperation incurs energy cost due to caching, carrying and forwarding the source's file. Yet, when a relay accepts to cooperate, it may receive some reward if it succeeds to be the first relay to forward the content to the destination. Otherwise, the relay may receive some penalty in the form of a constant regret; the latter parameter is introduced to make incentive for cooperation. Next, we introduce the concept of Satisfaction Equilibrium (SE) as a solution concept to the induced game. Now, the source node is solely interested in reaching a file delivery probability greater than some given threshold, while the relays behave rationally to maximize their respective payoffs. Full characterizations of the SEs for both pure and mixed strategies are derived. Furthermore, we propose two learning algorithms allowing the players (source/relays) to reach the SE strategies. Finally, extensive numerical investigations and some learning simulations are carried out to illustrate the behaviour of the interacting nodes.