  In the literature, scaling laws for wireless mobile networks have been
characterized under various models of node mobility and several assumptions on
how communication occurs between nodes. To improve the realism in the analysis
of scaling laws, we propose a new analytical framework. The framework is the
first to consider a L\'{e}vy flight mobility pattern, which is known to closely
mimic human mobility patterns. Also, this is the first work that allows nodes
to communicate while being mobile. Under this framework, delays ($\bar{D}$) to
obtain various levels of per-node throughput $(\lambda)$ for L\'evy flight are
suggested as $\bar{D}(\lambda) = O(\sqrt{\min (n^{1+\alpha} \lambda, n^2)})$,
where L\'evy flight is a random walk of a power-law flight distribution with an
exponent $\alpha \in (0,2]$. The same framework presents a new tighter tradeoff
$\bar{D}(\lambda) = O(\sqrt{\max (1,n\lambda^3)})$ for \textit{i.i.d.}
mobility, whose delays are lower than existing results for the same levels of
per-node throughput.
