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      Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

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          Abstract

          One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

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          Most cited references43

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          On variants of shortest-path betweenness centrality and their generic computation

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            Understanding Road Usage Patterns in Urban Areas

            In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.
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              Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                02 February 2018
                February 2018
                : 18
                : 2
                : 435
                Affiliations
                Departamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain; diego.rivera@ 123456uah.es (D.R.); susel.fernandez@ 123456uah.es (S.F.); ivan.marsa@ 123456uah.es (I.M.-M.)
                Author notes
                [* ]Correspondence: luis.cruz@ 123456uah.es ; Tel.: +34-918-856-644
                Author information
                https://orcid.org/0000-0002-9570-2851
                https://orcid.org/0000-0002-7076-9048
                https://orcid.org/0000-0002-1576-4340
                https://orcid.org/0000-0002-5529-2851
                Article
                sensors-18-00435
                10.3390/s18020435
                5856164
                29393884
                72fb9422-905f-40ab-b747-6c1164709af4
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 December 2017
                : 31 January 2018
                Categories
                Article

                Biomedical engineering
                sensor networks,optimized sensor deployment,multi-agents system,intelligent transportation system,smart cities,traffic simulations,traffic light management

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