Optimizing wireless unicast and multicast sensor networks on the basis of evolutionary game theory

Chen, Zhide; Qiao, Cheng; Xu, Li; Wu, Wei

doi:10.1002/cpe.3060

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The authors in [35] proposed a proactive defense model based on the evolutionary game, in which SNs have a limited ability to learn the evolution of rationality from different attack strategies of the attacker and can dynamically adjust their strategies to achieve the most effective defense. They also employ the evolutionary game to present two routing games, that is, a unicast and a multicast routing game [36]. Liu et al [37] proposed an evolutionary game-theoretic optimal framework for maximizing the coverage capacity in mobile sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

et al. 2016

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A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select actionTrustorMistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select actionTrust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose actionTrustas their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism.

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Section: Related Workmentioning

confidence: 99%

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

et al. 2016

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“…In the context of broadcast-type forwarding, a number of studies (e.g., [37], [38], [39], and [40]) exploit some classical game settings such as Diekman's "volunteer's dilemma" [41] or Arthur's "Santa Fe bar problem (SFBP)" [42] to strike coordination between the nodes' rebroadcasting decisions. The "volunteer's dilemma" models a situation in public economics where each player faces the decision of either making a small sacrifice from which all will benefit, or freeriding.…”

Section: Game-theoretic and Learning-based Forwarding: A Review Of Thmentioning

confidence: 99%

“…The NE's parameters, however, are assumed to be either a priori-known or be derived from simulation experiments, which limits its practicability. Similar FDG-like systems have been introduced in [38] and [39] for VANETs and wireless sensor networks (WSNs), respectively. The SFBP, on the other hand, typifies scenarios where a congested resource, a bar in the seminal article [42], is shared by a set of agents, i.e., the bar customers.…”

Section: Game-theoretic and Learning-based Forwarding: A Review Of Thmentioning

confidence: 99%

Cognitive forwarding control in wireless ad-hoc networks with slow fading channels

Hakami

Dehghan

2015

Wireless Netw

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We propose a decentralized stochastic control solution for the broadcast message dissemination problem in wireless ad-hoc networks (WANETs) with slow fading channels. We formulate the control problem as a dynamic robust game which is well-justified by two key observations: first, the shared nature of the wireless medium which inevitably cross-couples the nodes' forwarding decisions, thus binding them together as strategic players; second, the stochastic dynamics associated with the link qualities which renders the transmission costs noisy, thus motivating a robust formulation. Given the non-stationarity induced by the fading process, an online solution for the formulated game would then require an adaptive procedure capable of both convergence to and tracking strategic equilibria as the environment changes. To this end, we deploy the strategic and non-stationary learning algorithm of regret-tracking, the temporally-adaptive variant of the celebrated regret-matching algorithm, to guarantee the emergence and active tracking of the correlated equilibria (CE) in the dynamic robust forwarding game. We also make provision for exploiting the channel state information (CSI), when available, to enhance the convergence speed of the learning algorithm by conducting an accurate (transmission) cost estimation. This cost estimate can basically serve as a model which spares the algorithm from extra action exploration, thus rendering the learning process more sample-efficient.Simulation results reveal that our proposed solution excels in terms of both the number of transmissions and load distribution while also maintaining near perfect delivery ratio, especially in dense crowded environments.

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“…The first five papers are devoted to intelligent secure data analysis, encryption, and authentication techniques. The next three papers present intelligent optimization and data processing techniques, including genetic algorithms, independent component analysis, and evolutionary game theory. The following three papers deal with data modeling and analysis in wireless, mobile, and Web‐based computing systems.…”

mentioning

confidence: 99%

Advances in secure and intelligent data processing

Xhafa

Chen

Huang

et al. 2013

Concurrency and Computation

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EDITORIALAdvances in secure and intelligent data processingWith the fast proliferation of various computational paradigms, from Web-based to wireless and mobile computing, intelligent data processing and security have become cross-cutting features of large scale Internet-based system. On the one hand, there is data being generated everywhere, from various sources and heterogenous formats (such as document repositories, business process data, network data, mobile communications data, server logs data, etc.). On the other hand, the nature of the data, the amount, and data sensitivity, among other features, are promoting the combination of intelligent processing with security intelligence to deliver next generation secure and intelligent systems and applications.This special issue brings together high quality contributions in the interleaving research areas of intelligent data processing and intelligent techniques in security data analysis. The papers of the special issues have also identified and discussed the challenges in the field that will lead to new avenues in intelligent processing for secure and intelligent systems and applications.The special issue consists of 13 papers organized as follows. The first five papers [1][2][3][4][5] are devoted to intelligent secure data analysis, encryption, and authentication techniques. The next three papers [6][7][8] present intelligent optimization and data processing techniques, including genetic algorithms, independent component analysis, and evolutionary game theory. The following three papers [9][10][11] deal with data modeling and analysis in wireless, mobile, and Web-based computing systems. The final two papers [12,13] bring intelligent techniques from image processing field.

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Optimizing wireless unicast and multicast sensor networks on the basis of evolutionary game theory

Cited by 4 publications

References 25 publications

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

Cognitive forwarding control in wireless ad-hoc networks with slow fading channels

Advances in secure and intelligent data processing

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