Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km²) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km²) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

Single cell tracking based on Voronoi partition via stable matching

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as “stable matching problem” and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

Optimized Dislocation of Mobile Sensor Networks on Large Marine Environments Using Voronoi Partitions

The real-time environmental surveillance of large areas requires the ability to dislocate sensor networks. Generally, the probability of the occurrence of a pollution event depends on the burden of possible sources operating in the areas to be monitored. This implies a challenge for devising optimal real-time dislocation of wireless sensor networks. This challenge involves both hardware solutions and algorithms optimizing the displacements of mobile sensor networks in large areas with a vast number of sources of pollutant factors based mainly on diffusion mechanisms. In this paper, we present theoretical and simulated results inherent to a Voronoi partition approach for the optimized dislocation of a set of mobile wireless sensors with circular (radial) sensing power on large areas. The optimal deployment was found to be a variation of the generalized centroidal Voronoi configuration, where the Voronoi configuration is event-driven, and the centroid set of the corresponding generalized Voronoi cells changes as a function of the pollution event. The initial localization of the pollution events is simulated with a Poisson distribution. Our results could improve the possibility of reducing the costs for real-time surveillance of large areas, and other environmental monitoring when wireless sensor networks are involved.

Multi-Leader Election Algorithm Based On VORONOI Partition for Self-Stabilization in MANETs

Electing a leader in Mobile Adhoc Networks (MANETs) is the basic problem in providing the stabilization. Many current research outcomes study multi-leader election concept in the static networks and single leader in dynamic networks. The article mainly focusses on multi leader election in dynamic sensor networks where the nodes were installed randomly. This proposed concept elect an exclusive leader when the topology changes occurred eventually over the network. Furthermore, a basic representation used for the process. Also provided accurate proof for the proposed algorithm and shown the way of satisfying the stability condition through ensured results.

GM-VPC: An Algorithm for Multi-robot Coverage of Known Spaces Using Generalized Voronoi Partition

SUMMARYIn this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.