As the binary sensing model is a coarse approximation
of reality, the probabilistic sensing model has been proposed
as a more realistic model for characterizing the sensing region. A
point is covered by sensor networks under the probabilistic sensing
model if the joint sensing probability from multiple sensors is
larger than a predefined threshold ε. Existing work has focused
on probabilistic point coverage since it is extremely difficult to
verify the coverage of a full continuous area (i.e., probabilistic area
coverage). In this paper, we tackle such a challenging problem. We
first study the sensing probabilities of two points with a distance of
d and obtain the fundamental mathematical relationship between
them. If the sensing probability of one point is larger than a certain
value, the other is covered. Based on such a finding, we transform
probabilistic area coverage into probabilistic point coverage, which
greatly reduces the problem dimension. Then, we design the ε-full
area coverage optimization (FCO) algorithm to select a subset of
sensors to provide probabilistic area coverage dynamically so that
the network lifetime can be prolonged as much as possible. We also
theoretically derive the approximation ratio obtained by FCO to
that by the optimal one. Finally, through extensive simulations, we
demonstrate that FCO outperforms the state-of-the-art solutions
significantly