An Efficient Approach to the k-Strong Barrier Coverage Problem Under the Probabilistic Sensing Model in Wireless Multimedia Sensor Networks

Barrier coverage (BC) is a potential coverage model in wireless multimedia sensor networks (WMSNs) for applications such as intrusion detection and border surveillance. This model necessitates a chain of sensors positioned across the deployment region with overlapping sensing fields. However, achieving k-strong barrier coverage following the initial random sensor deployment poses significant challenges. BC holes frequently emerge within the sensing fields, even in high-density sensor. Previous research primarily focused on addressing the problem of constructing k-strong barrier coverage under a Boolean disk or a sector coverage model. This approach leads to inaccurate assessments of barrier coverage quality. To address the limitation, this paper presents an efficient scheme for achieving k-strong barrier coverage in heterogeneous WMSNs (HeWMSNs) using the minimum number of mobile sensors, while employing a probabilistic sector coverage model. By leveraging the proposed probabilistic sector sensing coverage model, we formulate the problem of attaining k-strong barrier coverage in HeWMSNs as a combinatorial optimization problem called KSB-HeWMSN. Subsequently, an efficient evolutionary algorithm is developed to tackle this problem. Through both analytical analysis and experimental evaluations conducted on multiple instances, the proposed algorithm demonstrates its suitability for the KSB-HeWMSN problem and its superior solution quality compared to previous approaches.