Efficient and robust Sensor Placement in Complex Environments
This addresses the need for efficient and robust sensor placement in domains like security or monitoring, but it appears incremental as it builds on existing greedy methods with deep learning enhancements.
The paper tackles the problem of designing minimal sensor sets for surveillance or communication in complex environments while ensuring robustness against failures or attacks, achieving multi-coverage constraints through a greedy algorithm and deep learning acceleration.
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 $ε$-greedy algorithms to generate data and their impact on the robustness of the network.