Solving priority-based target coverage problem in directional sensor networks with adjustable sensing ranges

Abstract

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have recently attracted a great deal of attention. DSNs primarily operate based on simultaneously observing a group of events (targets) occurring in a set area and maximizing network lifetime, as there are limitations to the directional sensors’ sensing angle and battery power. The higher the number of sensing ranges of the sensors and the more different the coverage requirements for the targets, the more complex this issue will be. Also known as priority-based target coverage with adjustable sensing ranges (PTCASR), this issue, which has not yet been investigated in the field of study, is the highlight of this research. A potential solution to this problem, based on the fact that sensors are frequently densely deployed, would be to organize the sensors into a few cover sets. After that the cover sets needs to be successively activated—this process is referred to as the scheduling technique. This paper aims to resolve the issue of PTCASR with the proposal of two scheduling algorithms i.e. greedy-based and learning automata-based algorithms. These proposed algorithms were assessed for their performance via a number of experiments. Additionally, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. Both algorithms were successful in solving the problem; however, the learning automata-based scheduling algorithm proved relatively superior to the greedy-based algorithm when it came to extending network lifetime.