A unified solving approach for two and three dimensional coverage problems in sensor networks

Sterle, Claudio; Sforza, Antonio; Amideo, Annunziata Esposito; Piccolo, Carmela

doi:10.1007/s11590-016-1014-8

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Some AGP-derived problem formulations attempt to use a two-dimensional floor plan and two-dimensional camera models [ 9 , 18 , 19 , 20 , 21 , 22 ]. These models benefitted from their simplicity as camera FoV could be treated as a simple, static two-dimensional shape which enabled many optimization techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Several works expand the two-dimensional world view of the AGP into a more realistic three-dimensional case [ 7 , 16 , 20 , 23 , 24 , 25 , 26 , 27 , 28 ]. However, while many of these models can be simplified when you care about a planar scene, these models require extra computation to be used for occlusion checking which is unnecessary when dealing with a planar scene.…”

Section: Related Workmentioning

confidence: 99%

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Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Watras

Kim

Liu

et al. 2018

Sensors

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An optimal camera placement problem is investigated. The objective is to maximize the area of the field of view (FoV) of a stitched video obtained by stitching video streams from an array of cameras. The positions and poses of these cameras are restricted to a given set of selections. The camera array is designed to be placed inside the abdomen to support minimally invasive laparoscopic surgery. Hence, a few non-traditional requirements/constraints are imposed: Adjacent views are required to overlap to support image registration for seamless video stitching. The resulting effective FoV should be a contiguous region without any holes and should be a convex polygon. With these requirements, traditional camera placement algorithms cannot be directly applied to solve this problem. In this work, we show the complexity of this problem grows exponentially as a function of the problem size, and then present a greedy polynomial time heuristic solution that approximates well to the globally optimal solution. We present a new approach to directly evaluate the combined coverage area (area of FoV) as the union of a set of quadrilaterals. We also propose a graph-based approach to ensure the stitching requirement (overlap between adjacent views) is satisfied. We present a method to find a convex polygon with maximum area from a given polygon. Several design examples show that the proposed algorithm can achieve larger FoV area while using much less computing time.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Watras

Kim

Liu

et al. 2018

Sensors

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“…MILP. Prior work has surveyed and combined MILP/ILP approaches to solve the sensor placement problem [31]. Our MILP formulation extends this with connectivity constraints.…”

Section: Related Workmentioning

confidence: 99%

Rapid Top-Down Synthesis of Large-Scale IoT Networks

Ghosh¹,

Bunton²,

Pylorof³

et al. 2020

Preprint

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Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verification and synthesis. Motivated by this, we consider the top-down synthesis of ad-hoc IoT networks for disaster response and search and rescue operations. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks, and, to scale to problem sizes beyond what these frameworks are capable of, develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.

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“…In [28], a distributed communication-aware coverage control method was proposed to maximize area coverage by a mobile robot network. A unified omni-directional and directional coverage control approach for 2D or 3D coverage problems was presented in [29], which was based on schematizing the region of interest by a grid of points and describing perceptual area by a circle or sector. An autonomous optimal deployment method with directional sensing models was suggested in [30], including concurrent rotation control and staged rotation control.…”

Section: And Liu Proposed a Sectormentioning

confidence: 99%

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Jiao

Zhang

et al. 2019

IEEE Access

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Wireless multimedia sensor networks (WMSNs) are widely used in various fields where coverage control is a critical difficulty because multiple requirements need to be considered such as service quality and energy consumption. In this paper, we focus on the issues existing in coverage model and coverage control method, and propose an adaptive particle swarm optimization algorithm for solving the coverage control problem in the WMSNs. First of all, a 3D coverage model is developed with introducing the perceptual radius of sensors, while the optimal projection area of a single sensor is deduced. Hereafter, an adaptive particle swarm optimization is suggested to optimize the location information of sensors for reducing both perceptual overlapping areas and perceptual blind areas in the monitoring area. At last, a redundant node sleeping strategy is presented to reduce the number of working sensors. Simulation results have show that we can ensure better coverage as well as fewer sensors compared to the state-of-the-art frameworks.

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A unified solving approach for two and three dimensional coverage problems in sensor networks

Cited by 7 publications

References 22 publications

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Rapid Top-Down Synthesis of Large-Scale IoT Networks

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

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