Cooperation Enforcement for Packet Forwarding Optimization in Multi-hop Ad-hoc Networks

TL;DR

This paper introduces a self-learning game-based framework inspired by a TV game show to motivate cooperation among nodes in ad-hoc networks, improving packet forwarding efficiency.

Contribution

It proposes a novel repeated game framework that enforces cooperation in ad-hoc networks, outperforming existing self-learning game methods.

Findings

The framework effectively enforces cooperation among nodes.

Simulation results show improved packet forwarding efficiency.

The approach outperforms two existing self-learning game frameworks.

Abstract

Ad-hoc networks are independent of any infrastructure. The nodes are autonomous and make their own decisions. They also have limited energy resources. Thus, a node tends to behave selfishly when it is asked to forward the packets of other nodes. Indeed, it would rather choose to reject a forwarding request in order to save its energy. To overcome this problem, the nodes need to be motivated to cooperate. To this end, we propose a self-learning repeated game framework to enforce cooperation between the nodes of a network. This framework is inspired by the concept of "The Weakest Link" TV game. Each node has a utility function whose value depends on its cooperation in forwarding packets on a route as well as the cooperation of all the nodes that form this same route. The more these nodes cooperate the higher is their utility value. This would establish a cooperative spirit within the nodes of the networks. All the nodes will then more or less equally participate to the forwarding tasks which would then eventually guarantee a more efficient packets forwarding from sources to respective destinations. Simulations are run and the results show that the proposed framework efficiently enforces nodes to cooperate and outperforms two other self-learning repeated game frameworks which we are interested in.