  We revisit the online Unit Clustering and Unit Covering problems in higher
dimensions: Given a set of $n$ points in a metric space, that arrive one by
one, Unit Clustering asks to partition the points into the minimum number of
clusters (subsets) of diameter at most one; while Unit Covering asks to cover
all points by the minimum number of balls of unit radius. In this paper, we
work in $\mathbb{R}^d$ using the $L_\infty$ norm.
  We show that the competitive ratio of any online algorithm (deterministic or
randomized) for Unit Clustering must depend on the dimension $d$. We also give
a randomized online algorithm with competitive ratio $O(d^2)$ for Unit
Clustering of integer points (i.e., points in $\mathbb{Z}^d$, $d\in
\mathbb{N}$, under $L_{\infty}$ norm). We show that the competitive ratio of
any deterministic online algorithm for Unit Covering is at least $2^d$. This
ratio is the best possible, as it can be attained by a simple deterministic
algorithm that assigns points to a predefined set of unit cubes. We complement
these results with some additional lower bounds for related problems in higher
dimensions.
