Minimizing Uncertainty through Sensor Placement with Angle Constraints

We study the problem of sensor placement in environments in which localization is a necessity, such as ad-hoc wireless sensor networks that allow the placement of a few anchors that know their location or sensor arrays that are tracking a target. In most of these situations, the quality of localization depends on the relative angle between the target and the pair of sensors observing it. In this paper, we consider placing a small number of sensors which ensure good angular $α$-coverage: given $α$ in $[0,π/2]$, for each target location $t$, there must be at least two sensors $s_1$ and $s_2$ such that the $\angle(s_1 t s_2)$ is in the interval $[α, π-α]$. One of the main difficulties encountered in such problems is that since the constraints depend on at least two sensors, building a solution must account for the inherent dependency between selected sensors, a feature that generic Set Cover techniques do not account for. We introduce a general framework that guarantees an angular coverage that is arbitrarily close to $α$ for any $α<= π/3$ and apply it to a variety of problems to get bi-criteria approximations. When the angular coverage is required to be at least a constant fraction of $α$, we obtain results that are strictly better than what standard geometric Set Cover methods give. When the angular coverage is required to be at least $(1-1/δ)\cdotα$, we obtain a $\mathcal{O}(\log δ)$- approximation for sensor placement with $α$-coverage on the plane. In the presence of additional distance or visibility constraints, the framework gives a $\mathcal{O}(\logδ\cdot\log k_{OPT})$-approximation, where $k_{OPT}$ is the size of the optimal solution. We also use our framework to give a $\mathcal{O}(\log δ)$-approximation that ensures $(1-1/δ)\cdot α$-coverage and covers every target within distance $3R$.

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