  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.
