Exploitation of Heterogeneity in Distributed Sensing

TL;DR

This paper introduces a distributed sensing framework that leverages heterogeneity among agents, including active and passive types, to improve environmental estimation accuracy in multi-agent systems.

Contribution

It presents a novel approach that exploits sensor heterogeneity and active-passive agent dynamics to enhance distributed sensing in dynamic environments.

Findings

Agents' states converge to a weighted average of active agents' inputs

The framework adapts weights based on environmental factors

Improves sensing accuracy by exploiting heterogeneity

Abstract

Most distributed sensing methods assume that the expected value of sensed information is same for all agents ignoring differences in sensor capabilities due to, for example, environmental factors and sensors quality and condition. In this paper, we present a distributed sensing framework to exploit heterogeneity in information provided about a dynamic environment using an active-passive networked multiagent systems approach. Specifically, this approach consists of agents subject to exogenous inputs (active agents) and agents without any inputs (passive agents). In addition, if an active agent senses a quantity accurately (resp., not accurately), then it is weighted high (resp., low) in the network such that these weights can be a function of time due to varying environmental factors. The key feature of our approach is that the states of all agents converge to an adjustable neighborhood of the weighted average of the sensed exogenous inputs by the active agents.