Ad hoc-VCG

Anderegg, Luzi; Eidenbenz, Stephan

doi:10.1145/938985.939011

Cited by 285 publications

(2 citation statements)

References 16 publications

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“…A coalition game is a game where a set of players act as a single entity to gain a higher payoff, called coalition value. The work in [47] proposed a coalitional game with transferable utilities [28], [37] Auction-Based Games [29]- [32], [38]- [40], [40]- [42] Bargaining-Based Games [33]- [36], [43]- [46] Coalition-Based Games [47]- [52], [42], [53], [ for mobile user-provided networks. In [48], boundary and backbone nodes form a coalition for data relaying across the network.…”

Section: A Incentivized Forwarding Mechanismsmentioning

confidence: 99%

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

Ibrahim,

Ekin,

Imran

2024

Trans. Mach. Learn. Comm. Netw.

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We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.

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Section: A Incentivized Forwarding Mechanismsmentioning

confidence: 99%

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

Ibrahim,

Ekin,

Imran

2024

Trans. Mach. Learn. Comm. Netw.

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“…Marti et al [4] introduced two related techniques, namely watchdog and pathrater, to detect and isolate misbehaving nodes, which are nodes that do not forward packets. There are also some other solutions that aim to cope with various routing misbehaviors [15], [16], [17].…”

Section: A Misbehavior Detection For Ad Hoc Networkmentioning

confidence: 99%

Coping with Node Misbehaviors in Ad Hoc Networks: A Multi-dimensional Trust Management Approach

Joshi

Finin

2010

2010 Eleventh International Conference on Mobile Data Management

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Nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets to enable communication between other nodes that are not in radio range with each other. However, whether for selfish or malicious reasons, a node may fail to cooperate during the network operations or even attempt to disturb them, both of which have been recognized as misbehaviors. Various trust management schemes have been studied to assess the behaviors of nodes so as to detect and mitigate node misbehaviors in MANETs. Most of existing schemes model a node's trustworthiness along a single dimension, combining all of the available evidence to calculate a single, scalar trust metric. A single measure, however, may not be expressive enough to adequately describe a node's trustworthiness in many scenarios. In this paper, we describe a multi-dimensional framework to evaluate the trustworthiness of MANET node from multiple perspectives. Our scheme evaluates trustworthiness from three perspectives: collaboration trust, behavioral trust, and reference trust. Different types of observations are used to independently derive values for these three trust dimensions. We present simulation results that illustrate the effectiveness of the proposed scheme in several scenarios.

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