An HMM-Based Reputation Model

ElSalamouny, Ehab; Sassone, Vladimiro

doi:10.1007/978-3-642-40597-6_9

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The transactions could be collected and packaged into new blocks according to the chain structure. Driven by a consensus mechanism, the transaction records are saved in the nodes until consensus is reached [30,31]. In blockchain, the block hash is the key point of generating secure blocks.…”

Section: Blockchainmentioning

confidence: 99%

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

Guo

Chang

et al. 2022

Applied Sciences

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The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.

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Section: Blockchainmentioning

confidence: 99%

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

Guo

Chang

et al. 2022

Applied Sciences

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“…However, for unstable systems (i.e., where behaviour changes frequently) this can lead to error in the prediction of the true system behaviour [12]. Alternatively, a finite state Hidden Markov Model (HMM) allows capturing dynamic behaviour of systems with lower error than Beta distribution-based trust models [10,13]. Other approaches do not require a direct interaction with the trustee to form an opinion.…”

Section: Related Work a Trustmentioning

confidence: 99%

Trust Trackers for Computation Offloading in Edge-Based IoT Networks

Bradbury

Jhumka

Watson

2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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Interaction Dynamics in a Social Network Using Hidden Markov Model

Ntwiga¹,

Ogutu²

2018

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Agents interactions in a social network are dynamic and stochastic. We model the dynamic interactions using the hidden Markov model, a probability model which has a wide array of applications. The transition matrix with three states, forgetting, reinforcement and exploration is estimated using simulation. Singular value decomposition estimates the observation matrix for emission of low, medium and high interaction rates. This is achieved when the rank approximation is applied to the transition matrix. The initial state probabilities are then estimated with rank approximation of the observation matrix. The transition and the observation matrices estimate the state and observed symbols in the model. Agents interactions in a social network account for between 20% and 50% of all the activities in the network. Noise contributes to the other portion due to interaction dynamics and rapid changes observable from the agents transitions in the network. In the model, the interaction proportions are low with 11%, medium with 56% and high with 33%. Hidden Markov model has a strong statistical and mathematical structure to model interactions in a social network.

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An HMM-Based Reputation Model

Cited by 3 publications

References 17 publications

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

Trust Trackers for Computation Offloading in Edge-Based IoT Networks

Interaction Dynamics in a Social Network Using Hidden Markov Model

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