Learnable WSN Deployment of Evidential Collaborative Sensing Model
This work addresses coverage and deployment inefficiencies in WSNs, particularly for detection tasks like environmental monitoring, but it appears incremental as it builds on existing evidence theory and optimization methods.
The paper tackles the problem of achieving optimal coverage quality in wireless sensor network (WSN) deployment by developing a collaborative sensing model based on evidence theory and a learnable sensor deployment network (LSDNet), resulting in improved detection capabilities and efficient sensor usage, as demonstrated through numerical examples and a forest monitoring application.
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.