# Learnable WSN Deployment of Evidential Collaborative Sensing Model

arXiv: https://arxiv.org/abs/2403.15728 · HTML: https://mlsanity.com/paper/2403.15728/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 63 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.10453/) (2020)
- [EDGF: Empirical dataset generation framework for wireless network networks](https://mlsanity.com/paper/2101.10758/) (2021)
- [A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks](https://mlsanity.com/paper/2208.11887/) (2022)

## Built on by (most cited first, 1 of 1)

- [Random Walk in Random Permutation Set Theory](https://mlsanity.com/paper/2404.03978/) (2024)

## Abstract (arXiv)

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.