Binomial Distribution Based Reputation for WSNs: A Comprehensive Survey

doi:10.3837/tiis.2021.10.017

Cited by 1 publication

(1 citation statement)

References 60 publications

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“…Wei and Yu [124] investigated reputation-based incentives for WSN by analyzing which functional components of binominal reputation the incentives apply. The functional components the authors analyzed include direct/indirect reputation, reputation aging, energy reputation, communication reputation, data reputation, reputation redemption, penalty & reward consideration, computational complexity, adaptive reputation threshold, adaptive forgetting factor, and energy consideration.…”

Section: Overview Of Prior Literature Reviewsmentioning

confidence: 99%

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

et al. 2023

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Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze eleven literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.

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Section: Overview Of Prior Literature Reviewsmentioning

confidence: 99%