# Multi-Perspective Trust Management Framework for Crowdsourced IoT Services

arXiv: https://arxiv.org/abs/2101.04244 · HTML: https://mlsanity.com/paper/2101.04244/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2021.

## Built on by (most cited first, 3 of 3)

- [Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks](https://mlsanity.com/paper/2208.01221/) (2022)
- [Layer-based Composite Reputation Bootstrapping](https://mlsanity.com/paper/2102.09951/) (2021)
- [A biologically Inspired Trust Model for Open Multi-Agent Systems that is Resilient to Rapid Performance Fluctuations](https://mlsanity.com/paper/2504.15301/) (2025)

## Abstract (arXiv)

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.