Effective Coverage of Two-Node Team with Radial Attenuation Models

The Effective Coverage of Homogeneous Teams with Radial Attenuation Models

“…We only discuss the situations where the nodes are equipped with the unbounded convex model as an example. For the truncated convex models and concave models, the methods are similar to the unbounded models in the overlapping region centered at , and the same with their corresponding 2-node cases [ 1 ] out of that overlapping region.…”

“…Many static devices and autonomous robots (collectively named “nodes”) can collaboratively sense or influence their surroundings. Coverage (area coverage) and effective coverage [ 1 ] are important metrics that reflect the sensing or influencing ability, or the performance of a node or a node team, such as a light array, air-conditioner team, wireless sensor network, etc. The analysis of a team’s effective coverage depends on the problems to be solved and their adopted models of coverage.…”

“…However, it is useful in and can be applied to many dynamic problems involving moving robots, such as mobile WSNs, cooperative illumination, animal husbandry, and agriculture (for birds detection/driving), etc. [ 1 , 8 ]. Hence, maximizing the effective coverage of a node team and maintaining the connectedness of its effectively covered region are desirable in such practical applications, which motivates our study in this article.…”

“…This model was also adopted in several later works [ 19 , 20 ]. Yáng et al proposed the concept of effective coverage based on the coverage intensity to characterize the coverage behavior of practical devices equipped with attenuation models [ 1 ]. Their model follows the superposition principle, which focuses on influencing activities (output coverage) and can approximate the behavior of sensing activities.…”

“…Optimal separation distances that maximize a team’s effective coverage are analyzed and simulated, respectively, for two types of models of coverage. The results can be directly applied to practical applications such as mobile air-conditioner teams [ 22 ] and birds driving robots, for output applications [ 1 ]; and for input applications, e.g., mobile wireless sensor networks, the approximation using our models and analysis is much more accurate and practical than the existing binary models. To the best of our knowledge, this is the first work to study the effective coverage optimization of a team containing a given number (≥3) of nodes equipped with a non-constant model of coverage.…”