  Consider $n$ sensors placed randomly and independently with the uniform
distribution in a $d-$dimensional unit cube ($d\ge 2$). The sensors have
identical sensing range equal to $r$, for some $r >0$. We are interested in
moving the sensors from their initial positions to new positions so as to
ensure that the $d-$dimensional unit cube is completely covered, i.e., every
point in the $d-$dimensional cube is within the range of a sensor. If the
$i$-th sensor is displaced a distance $d_i$, what is a displacement of minimum
cost? As cost measure for the displacement of the team of sensors we consider
the $a$-total movement defined as the sum $M_a:= \sum_{i=1}^n d_i^a$, for some
constant $a>0$. We assume that $r$ and $n$ are chosen so as to allow full
coverage of the $d-$dimensional unit cube and $a > 0$.
  The main contribution of the paper is to show the existence of a tradeoff
between the $d-$dimensional cube, sensing radius and $a$-total movement. The
main results can be summarized as follows for the case of the $d-$dimensional
cube.
  If the $d-$dimensional cube sensing radius is $\frac{1}{2n^{1/d}}$ and
$n=m^d$, for some $m\in N$, then we present an algorithm that uses
$O\left(n^{1-\frac{a}{2d}}\right)$ total expected movement (see Algorithm 2 and
Theorem 5).
  If the $d-$dimensional cube sensing radius is greater than
$\frac{3^{3/d}}{(3^{1/d}-1)(3^{1/d}-1)}\frac{1}{2n^{1/d}}$ and $n$ is a natural
number then the total expected movement is
$O\left(n^{1-\frac{a}{2d}}\left(\frac{\ln n}{n}\right)^{\frac{a}{2d}}\right)$
(see Algorithm 3 and Theorem 7).
  In addition, we simulate Algorithm 2 and discuss the results of our
simulations.
