Back to Search
Start Over
Performance evaluation of unmanned aerial vehicles in automatic power meter readings
- Source :
- Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
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 metaheuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.
- Subjects :
- Computer Networks and Communications
Computer science
Ant colony optimization algorithms
Real-time computing
020206 networking & telecommunications
02 engineering and technology
Energy consumption
SISTEMAS DISTRIBUÍDOS
Hardware and Architecture
Electricity meter
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Lightweight protocol
Electric power
Software
Simulation
Automatic meter reading
Subjects
Details
- ISSN :
- 15708705
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- Ad Hoc Networks
- Accession number :
- edsair.doi.dedup.....67a199059b3897573c1580b8b6c0a22b
- Full Text :
- https://doi.org/10.1016/j.adhoc.2017.03.003