A Distributed Sensor Relocatlon Scheme for Environmental Control

Garetto, Michele; Gribaudo, Marco; Chiasserini, Carla-Fabiana; Leonardi, E.

doi:10.1109/mobhoc.2007.4428663

Cited by 62 publications

(111 citation statements)

References 11 publications

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“…The results indicate that the VFA-DT converged faster than the VFA-LJ and the final distribution of the VFA-DT was more approximate to a perfect hexagonal topology. www.intechopen.com indicate that VFA-DT converged faster than VFA-LJ and that the final distribution of VFA-DT was more approximate to a perfect hexagonal topology, which also means that our algorithm can obtain a better convergence time and uniformity compared with the general VFA of Garetto et al [10]. In Figure 2, we plot the Delaunay triangulation and the dual graph Voronoi diagram for the final network configuration.…”

Section: The Optimized Vfa Implementation Routinementioning

confidence: 87%

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Optimized Node Deployment Algorithm and Parameter Investigation in a Mobile Sensor Network for Robotic Systems

Tang

Nie

Qian

et al. 2015

International Journal of Advanced Robotic Systems

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Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one of the most popular deployment methods, in recent years the virtual force algorithm has been studied in detail by many scientists. In this paper, we focus on the virtual force algorithm and present a corresponding parameter investigation for mobile sensor deployment. We introduce an optimized virtual force algorithm based on the exchange force, in which a new shielding rule grounded in Delaunay triangulation is adopted. The algorithm employs a new performance metric called 'pair-correlation diversion', designed to evaluate the uniformity and topology of the sensor distribution. We also discuss the implementation of the algorithm's computation and analyse the influence of experimental parameters on the algorithm. Our results indicate that the area ratio, φ s , and the exchange force constant, G, influence the final performance of the sensor deployment in terms of the coverage rate, the convergence time and topology uniformity. Using simulations, we were able to verify the effectiveness of our algorithm and we obtained an optimal region for the (φ s , G)-parameter space which, in the future, could be utilized as an aid for experiments in robotic sensor deployment.

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Section: The Optimized Vfa Implementation Routinementioning

confidence: 87%

“…When the equilibrium distance D m = 3R s , the sensor network ideally forms a triangular tessellation and can yield the optimal hexagonal coverage [10,15]. Garetto [10].…”

Section: Optimization Of the Shielding Rulementioning

confidence: 99%

Optimized Node Deployment Algorithm and Parameter Investigation in a Mobile Sensor Network for Robotic Systems

Tang

Nie

Qian

et al. 2015

International Journal of Advanced Robotic Systems

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“…The works closest to our approach are: the work in [Garetto et al 2007] and the Fcoverage formation problem discussed in . The model in ] covers a specific focus area around a known POI.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [Garetto et al 2007] studies the sensor deployment and relocation issue through the virtual forces paradigm proposed in [Zou and Chakrabarty 2003]. Specifically, the work in [Garetto et al 2007] makes some important assumptions on the characteristics of the MSN.…”

Section: Related Workmentioning

confidence: 99%

Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor Networks

Anagnostopoulos

Hadjiefthymiades

Kolomvatsos

2016

ACM Trans. Sen. Netw.

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We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to 'opportunistically' modify the previous placement of nodes to attain high quality phenomena monitoring. The required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the paper (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.

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“…Algorithms that belong to this category include [5], [8], [11], [12], just to name a few. The basic idea is: each node computes movement vectors for its neighbors in rounds using their relative position and then moves according to the vector summation.…”

Section: Related Workmentioning

confidence: 99%

Focused-coverage by mobile sensor networks

Frey

Santoro

2009

2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems

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Abstract-We pinpoint a new sensor self-deployment problem, constructing focused coverage around a Point of Interest (POI), and introduce an evaluation metric, coverage radius. We propose two solutions, Greedy Advance (GA) and Greedy-RotationGreedy (GRG), which are to our knowledge the first sensor selfdeployment algorithms that operate in a purely localized manner and yet provide coverage guarantee. The two algorithms drive sensors to move along a locally-computed equilateral triangle tessellation (TT) to surround POI. In GA, nodes greedily proceed as close to POI as they can; in GRG, when their greedy advance is blocked, nodes rotate around POI to a TT vertex where greedy advance can resume. They both yield a connected network of TT layout with hole-free coverage; GRG furthermore assures a hexagon coverage shape centered at POI. We prove their correctness and analyze their coverage radius property. Our study shows that GRG guarantees optimal hexagonal coverage radius and near optimal circular coverage radius. Through extensive simulation we as well evaluate their performance on convergence time, energy consumption, and node collision.

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A Distributed Sensor Relocatlon Scheme for Environmental Control

Cited by 62 publications

References 11 publications

Optimized Node Deployment Algorithm and Parameter Investigation in a Mobile Sensor Network for Robotic Systems

Optimized Node Deployment Algorithm and Parameter Investigation in a Mobile Sensor Network for Robotic Systems

Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor Networks

Focused-coverage by mobile sensor networks

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