Efficient and robust Sensor Placement in Complex Environments

TL;DR

This paper presents a method combining greedy algorithms and deep learning to optimize sensor placement for efficient, robust coverage in complex environments, considering multi-coverage constraints and adversarial robustness.

Contribution

It introduces a novel approach integrating deep learning with greedy algorithms to accelerate sensor placement optimization with robustness considerations.

Findings

Deep learning accelerates evaluation of the placement objective.

Geometric data properties influence neural network performance.

Different data generation strategies affect robustness and efficiency.

Abstract

We address the problem of efficient and unobstructed surveillance or communication in complex environments. On one hand, one wishes to use a minimal number of sensors to cover the environment. On the other hand, it is often important to consider solutions that are robust against sensor failure or adversarial attacks. This paper addresses these challenges of designing minimal sensor sets that achieve multi-coverage constraints -- every point in the environment is covered by a prescribed number of sensors. We propose a greedy algorithm to achieve the objective. Further, we explore deep learning techniques to accelerate the evaluation of the objective function formulated in the greedy algorithm. The training of the neural network reveals that the geometric properties of the data significantly impact the network's performance, particularly at the end stage. By taking into account these properties, we discuss the differences in using greedy and $\epsilon$-greedy algorithms to generate data and their impact on the robustness of the network.