An average case analysis of the minimum spanning tree heuristic for the range assignment problem

We present an average case analysis of the minimum spanning tree heuristic for the range assignment problem on a graph with power weighted edges. It is well-known that the worst-case approximation ratio of this heuristic is 2. Our analysis yields the following results: (1) In the one dimensional case ($d = 1$), where the weights of the edges are 1 with probability $p$ and 0 otherwise, the average-case approximation ratio is bounded from above by $2-p$. (2) When $d =1$ and the distance between neighboring vertices is drawn from a uniform $[0,1]$-distribution, the average approximation ratio is bounded from above by $2-2^{-\alpha}$ where $\alpha$ denotes the distance power radient. (3) In Euclidean 2-dimensional space, with distance power gradient $\alpha = 2$, the average performance ratio is bounded from above by $1 + \log 2$

Interference Minimization in Asymmetric Sensor Networks

A fundamental problem in wireless sensor networks is to connect a given set of sensors while minimizing the \emph{receiver interference}. This is modeled as follows: each sensor node corresponds to a point in $\mathbb{R}^d$ and each \emph{transmission range} corresponds to a ball. The receiver interference of a sensor node is defined as the number of transmission ranges it lies in. Our goal is to choose transmission radii that minimize the maximum interference while maintaining a strongly connected asymmetric communication graph. For the two-dimensional case, we show that it is NP-complete to decide whether one can achieve a receiver interference of at most $5$. In the one-dimensional case, we prove that there are optimal solutions with nontrivial structural properties. These properties can be exploited to obtain an exact algorithm that runs in quasi-polynomial time. This generalizes a result by Tan et al. to the asymmetric case.Comment: 15 pages, 5 figure