Optimizing Sensor Network Design for Multiple Coverage

TL;DR

This paper presents a novel approach to optimize sensor network design for multiple coverage, focusing on robustness against failures and attacks, using a new objective function, theoretical bounds, and deep learning acceleration.

Contribution

It introduces a new objective function for sensor placement, derives theoretical bounds, and employs deep learning to enable near real-time optimization of robust sensor networks.

Findings

The new objective function improves sensor placement efficiency.

Deep learning accelerates the greedy algorithm significantly.

Parallel algorithms are competitive with more complex methods.

Abstract

Sensor placement optimization methods have been studied extensively. They can be applied to a wide range of applications, including surveillance of known environments, optimal locations for 5G towers, and placement of missile defense systems. However, few works explore the robustness and efficiency of the resulting sensor network concerning sensor failure or adversarial attacks. This paper addresses this issue by optimizing for the least number of sensors to achieve multiple coverage of non-simply connected domains by a prescribed number of sensors. We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks and derive theoretical bounds on the network's optimality. We further introduce a Deep Learning model to accelerate the algorithm for near real-time computations. The Deep Learning model requires the generation of training examples. Correspondingly, we show that understanding the geometric properties of the training data set provides important insights into the performance and training process of deep learning techniques. Finally, we demonstrate that a simple parallel version of the greedy approach using a simpler objective can be highly competitive.