Efficient and robust Sensor Placement in Complex Environments
Lukas Taus Yen-Hsi Richard Tsai
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.