Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/328019
Type: Artigo
Title: Performance Evaluation Of Unmanned Aerial Vehicles In Automatic Power Meter Readings
Author: Neto
Jose R. T.; Boukerche
Azzedine; Yokoyama
Roberto S.; Guidoni
Daniel L.; Meneguette
Rodolfo I.; Ueyama
Jo; Villas
Leandro A.
Abstract: Typically, the electric power companies employ a group of power meter readers to collect data on the customers energy consumption. This task is usually carried out manually, which can lead to high cost and errors, causing financial losses. Some approaches have tried to minimize these problems, using strategies such as discovering the minimal route or relying on vehicles to perform the readings. However, errors in the manual readings can occur and vehicles suffer from congestion and high fuel and maintenance costs. In this work, we go further and propose an architecture to the Automatic Meter Reading (AMR) system using Unmanned Aerial Vehicles (UAV). The main challenge of the solution is to design a robust and lightweight protocol that is capable of dealing with wireless communication collisions. Therefore, the main contribution of this work is the design of a new protocol to ensure wireless communication from UAV to the power meters. We validated and evaluated the architecture in an urban scenario, with results showing a decrease of time and distance when compared to other approaches. We also evaluated the system proposed with Linear Flight Plan, the Ant Colony Optimization and Guided Local Search meta heuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.(C) 2017 Elsevier B.V. All rights reserved.
Subject: Automatic Meter Reading
Unmanned Aerial Vehicles
Wireless Sensors Networks
Message Collision
Editor: Elsevier Science BV
Amsterdam
Citation: Ad Hoc Networks. Elsevier Science Bv, v. 60, p. 11 - 25, 2017.
Rights: fechado
Identifier DOI: 10.1016/j.adhoc.2017.03.003
Address: http://www.sciencedirect.com/science/article/pii/S1570870517300471
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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