A Collaborative UAV-WSN Network for Monitoring Large Areas

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and...

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Main Authors: Dan Popescu, Cristian Dragana, Florin Stoican, Loretta Ichim, Grigore Stamatescu
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/18/12/4202
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author Dan Popescu
Cristian Dragana
Florin Stoican
Loretta Ichim
Grigore Stamatescu
author_facet Dan Popescu
Cristian Dragana
Florin Stoican
Loretta Ichim
Grigore Stamatescu
author_sort Dan Popescu
collection DOAJ
description Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.
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spelling doaj.art-0be45f72cc4245b88c7b2964f7dc839b2022-12-22T04:22:54ZengMDPI AGSensors1424-82202018-11-011812420210.3390/s18124202s18124202A Collaborative UAV-WSN Network for Monitoring Large AreasDan Popescu0Cristian Dragana1Florin Stoican2Loretta Ichim3Grigore Stamatescu4Department of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, 060042 București, RomaniaDepartment of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, 060042 București, RomaniaDepartment of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, 060042 București, RomaniaDepartment of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, 060042 București, RomaniaDepartment of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, 060042 București, RomaniaLarge-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.https://www.mdpi.com/1424-8220/18/12/4202large area monitoringwireless sensor networkunmanned aerial vehicleoptimal trajectory designclustering
spellingShingle Dan Popescu
Cristian Dragana
Florin Stoican
Loretta Ichim
Grigore Stamatescu
A Collaborative UAV-WSN Network for Monitoring Large Areas
Sensors
large area monitoring
wireless sensor network
unmanned aerial vehicle
optimal trajectory design
clustering
title A Collaborative UAV-WSN Network for Monitoring Large Areas
title_full A Collaborative UAV-WSN Network for Monitoring Large Areas
title_fullStr A Collaborative UAV-WSN Network for Monitoring Large Areas
title_full_unstemmed A Collaborative UAV-WSN Network for Monitoring Large Areas
title_short A Collaborative UAV-WSN Network for Monitoring Large Areas
title_sort collaborative uav wsn network for monitoring large areas
topic large area monitoring
wireless sensor network
unmanned aerial vehicle
optimal trajectory design
clustering
url https://www.mdpi.com/1424-8220/18/12/4202
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