An Emotion-driven Negotiation Mechanism of Selfish Nodes in the MANETs

Ad-hoc networks are attractive, since they can provide a high level of connectivity without the need of a fixed infrastructure. Nodes that are not within the same transmission range communicate through multi-hops, where intermediate nodes act as relays. Mutual cooperation of all the participating nodes is necessary for proper operation of MANET. However, nodes in MANET being battery-constrained, they tend to behave selfishly while forwarding packets. In this paper, we have investigated the security of MANET AODV routing protocol by identifying the impact of selfish nodes on it. It was observed that due to the presence of selfish nodes, packet loss in the network increases and the performance of MANET degrades significantly. Finally a game theoretic approach is used to mitigate the selfishness attack. All the nodes in MANET should cooperate among themselves to thwart the selfish behavior of attacker nodes.

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An incentive-based fairness mechanism for multi-hop wireless backhaul networks with selfish nodes

IEEE Transactions on Wireless Communications ◽

10.1109/twc.2008.060631 ◽

2008 ◽

Vol 7 (2) ◽

pp. 697-704 ◽

Cited By ~ 20

Author(s):

Jeng Farn Lee ◽

Wanjiun Liao ◽

Meng Chang Chen

Keyword(s):

Selfish Nodes ◽

Wireless Backhaul ◽

Backhaul Networks

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Transmission of multimedia contents in opportunistic networks with social selfish nodes

Multimedia Systems ◽

10.1007/s00530-013-0343-z ◽

2013 ◽

Vol 21 (3) ◽

pp. 277-288 ◽

Cited By ~ 5

Author(s):

Daru Pan ◽

Hui Zhang ◽

WeiJing Chen ◽

Ke Lu

Keyword(s):

Opportunistic Networks ◽

Selfish Nodes ◽

Multimedia Contents

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A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

SN Computer Science ◽

10.1007/s42979-021-00650-4 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

Farzaneh Farhadi ◽

Nicholas R. Jennings

Keyword(s):

Information Leakage ◽

Optimal Strategies ◽

Privacy Preserving ◽

Limited Attention ◽

Limited Information ◽

Multi Agent Systems ◽

Negotiation Strategies ◽

Multi Agent ◽

Theoretical And Numerical Analysis ◽

Negotiation Mechanism

AbstractDistributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.

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