Learnable WSN Deployment of Evidential Collaborative Sensing Model

• URL: http://arxiv.org/abs/2403.15728v1
• Date: Sat, 23 Mar 2024 05:29:09 GMT
• Title: Learnable WSN Deployment of Evidential Collaborative Sensing Model
• Authors: Ruijie Liu, Tianxiang Zhan, Zhen Li, Yong Deng,
• Abstract summary: In wireless sensor networks (WSNs), coverage and deployment are two most crucial issues when conducting detection tasks. We develop a collaborative sensing model of sensors to enhance detection capabilities of WSNs. A learnable sensor deployment network (LSDNet) is proposed for achieving the optimal deployment of WSNs.
• Score: 11.389924009815795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In wireless sensor networks (WSNs), coverage and deployment are two most crucial issues when conducting detection tasks. However, the detection information collected from sensors is oftentimes not fully utilized and efficiently integrated. Such sensing model and deployment strategy, thereby, cannot reach the maximum quality of coverage, particularly when the amount of sensors within WSNs expands significantly. In this article, we aim at achieving the optimal coverage quality of WSN deployment. We develop a collaborative sensing model of sensors to enhance detection capabilities of WSNs, by leveraging the collaborative information derived from the combination rule under the framework of evidence theory. In this model, the performance evaluation of evidential fusion systems is adopted as the criterion of the sensor selection. A learnable sensor deployment network (LSDNet) considering both sensor contribution and detection capability, is proposed for achieving the optimal deployment of WSNs. Moreover, we deeply investigate the algorithm for finding the requisite minimum number of sensors that realizes the full coverage of WSNs. A series of numerical examples, along with an application of forest area monitoring, are employed to demonstrate the effectiveness and the robustness of the proposed algorithms.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Analysis and Optimization of Seismic Monitoring Networks with Bayesian Optimal Experiment Design [0.0]
  Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network's ability to locate seismic events.
  arXiv  Detail & Related papers  (2024-09-27T04:45:27Z)
• Optimizing Sensor Network Design for Multiple Coverage [0.9668407688201359]
  We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks. We also introduce a Deep Learning model to accelerate the algorithm for near real-time computations.
  arXiv  Detail & Related papers  (2024-05-15T05:13:20Z)
• MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs [3.887356044145916]
  Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. We propose an innovative intrusion detection approach that integrates Machine Learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs.
  arXiv  Detail & Related papers  (2024-02-17T18:04:08Z)
• 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)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks [3.6748639131154315]
  Wireless Sensor Networks (WSNs) can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention.
  arXiv  Detail & Related papers  (2022-08-25T06:39:29Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Data-aided Sensing for Distributed Detection [48.523643863141466]
  We derive a node selection criterion based on the J-divergence in DAS for reliable decision subject to a decision delay constraint. Based on the proposed J-divergence based DAS, the nodes can be selected to rapidly increase the log-likelihood ratio (LLR) It is confirmed that the J-divergence based DAS can provide a reliable decision with a smaller number of sensors compared to other approaches.
  arXiv  Detail & Related papers  (2020-11-17T03:15:44Z)

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.