Self-Rating in a Community of Peers

Abstract : Consider a community of agents, all performing a predefined task, but with different abilities. Each agent may be interested in knowing how well it performs in comparison with her peers. This general scenario is relevant, e.g., in Wireless Sensor Networks (WSNs), or in the context of crowd sensing applications, where devices with embedded sensing capabilities collaboratively collect data to characterize the surrounding environment, but the performance is very sensitive to the accuracy of the gathered measurements. In this paper we present a distributed algorithm allowing each agent to self-rate her level of expertise/performance at the task, as a consequence of pairwise interactions with the peers. The dynamics of the proportions of agents with similar beliefs in their expertise are described using continuous-time state equations. The existence of an equilibrium is shown. Closedform expressions for the various proportions of agents with similar belief in their expertise is provided at equilibrium. Simulation results match well theoretical results in the context of agents equipped with sensors aiming at determining the performance of their sensors.
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Wenjie Li, Francesca Bassi, Laura Galluccio, Michel Kieffer. Self-Rating in a Community of Peers. 55th IEEE Conference on Decision and Control (CDC 2016), Dec 2016, Las Vegas, United States. pp.5888-5893, ⟨10.1109/cdc.2016.7799175 ⟩. ⟨hal-01327792v2⟩

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