Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. This trend motivated several research efforts to employ drones as standalone systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain view points; 3) Occlusion by elements in the environment, including other targets. In this paper, we present Argus, a system that provides coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Furthermore, we present a sampling heuristic that yields a comparable performance, yet is up to 100x faster than the approximation algorithm. We build a prototype of Argus to demonstrate and evaluate the proposed coverage algorithms as part of a complete autonomous surveillance system. We evaluate the proposed algorithms using simulations to compare their performance at scale under various conditions. Finally, we present extensions and discuss open problems related to the studied problem.
Full-view coverage is a variant of area coverage with an extra objective of ensuring that any target is covered from all angles @cite_45 . @cite_43 studies the necessary conditions for full-view coverage for static camera deployments and @cite_26 studies full-view coverage for heterogeneous mobile camera scenarios. The related problem of barrier coverage was studied in @cite_55 , where the objective is to detect any targets crossing the barrier into an area of interest. Full view barrier coverage was then introduced @cite_48 and further extended to accommodate stochastic deployments in @cite_14 .
@cite_26 @cite_14 @cite_48 @cite_55 @cite_43 @cite_45
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In camera sensor networks (CSNs), full view cov- erage, in which any direction of any point in the operational region is covered by at least one camera sensor, is of great significance since image shot at the frontal viewpoint considerably increases the possibility to recognize the object. However, finding the critical condition to achieve full view coverage in mobile heterogeneous CSNs remains an open question. In this paper, we analyze both the static and mobile random deployed camera sensor networks. A centralized parameter - equivalent sensing radius (ESR) - is defined to evaluate the critical requirement for asymptotic full view coverage in heterogeneous CSNs. We derive the critical sensing range for full view coverage under static model, 2-dimensional random walk mobility model, 1- dimensional random walk mobility model and random rotating model. We then discuss the impact of various mobility patterns on sensing energy consumption and study the relationship between ESR and percentage of full view coverage, and show that random walk mobility model can decrease the sensing energy consumption under certain delay tolerance. To our knowledge, our work is the very first that derive the critical condition to achieve full view coverage in mobile heterogeneous CSNs. Barrier coverage in visual camera sensor networks (visual barrier coverage) has important real-world applications like battlefield surveillance, environmental monitoring, and protection of government property. Cost-effective deployment, a fundamental issue of visual barrier coverage, considers how to deploy the fewest camera sensors along the barrier to detect intruders (e.g., capture faces) with desirable performance. Existing visual barrier coverage approaches like full-view coverage require numerous camera sensors for capturing intruders' faces deterministically for any trajectory and facing angle. However, intruders' trajectories and facing angles are bounded and deterministic intruder detection requires many camera sensors for rare intrusion cases. Certain practical applications can tolerate limited intrusion mis-detection given budget limitations. This paper proposes local face-view barrier coverage, a novel concept that achieves statistical barrier coverage in camera sensor networks leveraging intruders' trajectory lengths l along the barrier and head rotation angles δ. Using (l, δ) and other parameters, we derive a rigorous probability bound for intruder detection for local face-view barrier coverage via a feasible deployment pattern. Our detection probability bound and deployment pattern can guide practical camera sensor network deployments with camera sensor budgets. Extensive evaluations show that local face-view barrier coverage requires up to 50 fewer camera sensors than full-view barrier coverage. When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least k distinct sensors before it crosses the barrier of wireless sensors, we say the network provides k-barrier coverage. In this paper, we develop theoretical foundations for k-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for k-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak k-barrier coverage. Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called full-view coverage. An object is considered to be full-view covered if for any direction from 0 to 2π (object's facing direction), there is always a sensor such that the object is within the sensor's range and more importantly the sensor's viewing direction is sufficiently close to the object's facing direction. With this model, we propose an efficient method for full-view coverage detection in any given camera sensor networks. We also derive a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment. Finally, we show a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice based deployment.