Sensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity

• URL: http://arxiv.org/abs/2407.19736v1
• Date: Mon, 29 Jul 2024 06:56:57 GMT
• Title: Sensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity
• Authors: Spilios Evmorfos, Zhaoyi Xu, Athina Petropulu,
• Abstract summary: This work addresses the challenge of selecting $k$ sensor elements from a set of $m$ to optimize a generic Quality-of-Service metric. evaluating all $binommk$ possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches.
• Score: 3.255315368019651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of sensor arrays in sensing and wireless communications improves with more elements, but this comes at the cost of increased energy consumption and hardware expense. This work addresses the challenge of selecting $k$ sensor elements from a set of $m$ to optimize a generic Quality-of-Service metric. Evaluating all $\binom{m}{k}$ possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches. The current paper proposes a new framework that employs deep generative modeling, treating sensor selection as a deterministic Markov Decision Process where sensor subsets of size $k$ arise as terminal states. Generative Flow Networks (GFlowNets) are employed to model an action distribution conditioned on the state. Sampling actions from the aforementioned distribution ensures that the probability of arriving at a terminal state is proportional to the performance of the corresponding subset. Applied to a standard sensor selection scenario, the developed approach outperforms popular methods which are based on convex optimization and greedy algorithms. Finally, a multiobjective formulation of the proposed approach is adopted and applied on the sparse antenna array design for Integrated Sensing and Communication (ISAC) systems. The multiobjective variation is shown to perform well in managing the trade-off between radar and communication performance.

Related papers

• Deep Optimal Sensor Placement for Black Box Stochastic Simulations [1.638332186726632]
  We propose a novel approach, the joint distribution over input parameters and solution with a joint energy-based model. We demonstrate the validity of our framework on a variety of problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches.
  arXiv  Detail & Related papers  (2024-10-15T20:10:25Z)
• Conditional Gumbel-Softmax for constrained feature selection with application to node selection in wireless sensor networks [48.536059764418454]
  We introduce Conditional Gumbel-Softmax as a method to perform end-to-end learning of the optimal subset for a given task. We demonstrate how this approach can be used to select the task-optimal nodes composing a wireless sensor network.
  arXiv  Detail & Related papers  (2024-06-03T09:55:56Z)
• A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks [53.022158485867536]
  We propose a dynamic sensor selection approach for deep neural networks (DNNs) It is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. We show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
  arXiv  Detail & Related papers  (2023-08-16T14:04:50Z)
• Task-Oriented Over-the-Air Computation for Multi-Device Edge AI [57.50247872182593]
  6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system.
  arXiv  Detail & Related papers  (2022-11-02T16:35:14Z)
• Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method [4.031433260365659]
  We propose an optimal deployment method for practical wireless sensor networks (WSNs) We develop a novel multi-objective optimization algorithm known as the competitive multi-objective optimization algorithm (CMOMPA) The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability, and network reliability.
  arXiv  Detail & Related papers  (2022-07-19T05:53:55Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• Learning a Probabilistic Strategy for Computational Imaging Sensor Selection [16.553234762932938]
  We propose a physics-constrained, fully differentiable, autoencoder that learns a probabilistic sensor-sampling strategy for optimized sensor design. The proposed method learns a system's preferred sampling distribution that characterizes the correlations between different sensor selections as a binary, fully-connected Ising model.
  arXiv  Detail & Related papers  (2020-03-23T17:52:17Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.