Energy Consumption in Wireless Sensor Networks for ...

Smart Cities: Energy Consumption in Wireless Sensor Networks for Road Traffic Modeling Using Simulator SUMO M. Kabrane, S. Krit, L. Elmaimouni, K. Bendaoud, H. Oudani,, M. Elasikri, K. Karimi and H. El Bousty

[email protected] [email protected] [email protected] [email protected] [email protected]

Laboratory of Engineering Sciences and Energy. Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University Agadir BP/638 Morocco [email protected] [email protected] [email protected] Abstract—In recent years, wireless sensor networks (WSNs) and their applications especially in the urban traffic management, have been the subject of a growing interest on the part of researchers. WSN consist of small nodes with sensing, processing and wireless communications capabilities. A critical issue in wireless sensor networks is to extend the life of the network by preserving the energy of the sensor nodes. This last consume most of his energy during the phase of data transfer. We have previously proposed a promising algorithm which is used to minimize the amount of data transfer in a sensors network, which helps (contributes) to reduce energy consumption. In this paper, we used the SUMO and OpenstreetMap (OSM) tools to analyze urban infrastructure, and simulate urban traffic in a more realistic way. We show how to Simulate traffic using OpenStreetMap-data and SUMO- Simulation for Urban MObility. The simulation results again showed that our proposed algorithm remains an effective technique and confirms our results find previously by the GLD simulator, to reduce the energy consumption of a sensor node

Keywords— Wireless sensor Networks (WSNs), Energy consumption, Urban traffic, OpenstreetMap, SUMO

and used SUMO (Simulation of Urban MObility) [19-23], a well-known microscopic traffic simulator for simulations. In this case, it is an infrastructure consisting of several intersections (complex case) that can be modeled using OpenStreetMap (OSM). First, we are interested in the management of traffic signals in an urban road network, this management is made by the communication between a sensor node and the traffic light controller. We will present a real infrastructure in order to present the objective and the problem to be solved. Next, we describe the simulation tools and how they are used, and how to convert data of OSM to SUMO simulation scenario. The different simulations are proposed to illustrate the interest of choosing this simulator. We then discuss the results of simulations that allow us to value our models and our approach. These results are used as input parameters for our previously presented algorithm. The results obtained confirm and validate our approach of minimizing energy expenditure. This article ends with a general conclusion and some perspectives. II.

I.

INTRODUCTION

Network of wireless sensors (WSN) is undergoing a major revolution, Opening perspectives [1-8] in many areas of application especially the management of urban traffic with the aim of reducing congestion and waiting time[9]. The increase of the autonomy of sensors is the main focus of research [5,10-16] in this domain and this is the one previously developed by our research team [13-15] and simulated in this article. The energy consumed for data transmission is very important to detection and processing of the same amount of data[17,18]. We have selected Casablanca city in Morocco as our study region Figure. 1

ROAD NETWORK: OBJECTIVE AND PROBLEM

The basic idea is: We deal with an infrastructure case with several intersections Figure. 1-a) knowing that the data taken into account will be similar for each of the intersections: that each lane has two sensors Figure. 1-b): The first sensor is placed before the traffic lights, and second is placed after, (d) is the distance between the two sensors. Moreover, a controller is present on the side of the road to collect sensors data. After deployment, the set of nodes form a wireless sensor network to exchange information on passing cars.

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We present details on why the SUMO traffic simulator was chosen are provided, while a brief description of the SUMO software. Then, the motivation for the choice of the modeled zone as well as the details on the creation of the network within SUMO using the OpenStreetMap(OSM)-data A. WHY SIMULATION OF URBAN MOBILITY (SUMO)?

Figure 1-a): Area of study: city of casablanca in morocco -Maps-

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Figure 1-b) Area of study: city of casablanca in morocco -Schema with traffic light-

To make our ideas clear, when a vehicle passes over a sensor, the latter records the variations produced on the magnetic field. These variations allow to detect a vehicle, to know its type, detection time, estimated its speed, its length..., Each detection, the sensor sends a passage to the Controller to manage the traffic in real time, gold the sending recursive of messages generate important consumption energy, we talk about the problem of energy consumption in sensor networks. The aim of this model is to make traffic more fluid by allowing a flow of users to cross a set of intersections by minimizing the waiting time, and so minimize the number of packets sent by each node. this requiring particular coordination between the sensor nodes and the traffic light controllers. The nodes transmit information about the vehicles they have detected along the track. This information is then communicated to the controllers, which in turn processes the information according to these algorithms in order to create a "green wave", making it possible to minimize the number of sent packets

III.

B. SUMO

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There are several microscopic traffic simulators available to choose from. After analyzing our objectives, and in view of the direction of future work previously proposed [13-15]. It was decided to use the SUMO traffic simulator. It exists a number of important features offered by the SUMO [22] simulation environment that took this decision into account (Open Source, Graphical user interface (GUI), Can use OpenStreetMap data, Large Network Support)

OVERVIEW OF SUMO

In the rest of our article, we detail how to create a realistic traffic model to test the algorithm which allows to reduce the number of communication of a sensor [13,14].To create a realistic model, a section of the city-center of Casablanca was modeled in the simulation environment with SUMO.

SUMO (Simulation of Urban Mobility) is an open source, microscopic road traffic simulation package designed to handle large road networks, allowing to model the flow of road traffic, this by treating more realistic and broader cases. As we cited it previously, SUMO has several advantages of being always maintained by its authors, and to be fully documented. thus, it can handle large environments, put in place more complex structures: That is to say 10. 000 streets, and it can import many network formats such as XML for example. The latest software was released in November 2011. Overall, the use of this software can be summarized in the points detailed below. IV.

RELATED WORK A. CONSTRUCTION OF A NETWORK:

The generation of a map (here called a network) is not necessarily automated by an interface but relies on the construction of an XML file of the mapCasablanca.net.xml type, which can be obtained in multiple ways Two main tools exist to generate such networks: - NETGEN allows to generate in command line Purpose

Abstract road network generation

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portable (Linux/Windows tested); runs on command line Command line parameter

Input (mandatory): Output Programming Language

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A generated SUMO-road network; optionally also other outputs C++

– NETCONVERT Converts a wide range of possible inputs into SUMO readable road networks. Networks can be created from XML descriptions or automatically generated from OpenStreetMap 2

OpenStreetMap

netconvert --osm-files mapCasablanca .osm -o mapCasablanca.net.xml

Export

B. OPENSTREETMAP The use of OpenStreetMap (OSM) [2] data in traffic simulation environments is very important today [1, 4, 6] thanks to the free data. To obtain the desired network (creation of a map), it is possible to define a set of XML files. This approach may seem heavy and difficult to do manually, but remains sufficient and practical for small networks. In a complex case, we chose this method(OSM-data): After having selected the zone to be modeled, it must be translated into a SUMO traffic network. This process is facilitated by the NETCONVERT tool provided in SUMO Software, which allows the import of networks directly from OpenStreetMap. It allows you to export a rectangular area of a traffic network, which can then be imported into SUMO using the NETCONVERT tool.

Figure 2: OpenStreetMap network for the selected area - Casablanca city - Morocco

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mapCasablanca.poly.xml

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randomTrips.py : - generates a set of random trips for a given network (option -n) - The resulting trips are stored in an xml file (option -o, default trips.trips.xml)

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Figure 3: The steps to export the Openstreetmap data to generate a SUMO network file

C. GRAPHICAL INTERFACE Simulation of traffic with SUMO is executable in two different ways: • Use the command line directly: sumo-gui mapCasablanca.sumo.cfg • The application of SUMO-GUI The display of the graphical interface appears clean, and of various types: very basic, standard or under a more realistic view. The possibility of adjusting the speed of execution of the simulation always exists, this is adjustable by defining the number of milliseconds to which corresponds a program step. During execution, each vehicle movement and traffic progress can be observed

First, it's to convert the network to the Openstreetmap format (mapcasablanca.osm) into several XML files Figure 3: ▪ mapCasablanca.net.xml ▪ mapCasablanca.osm ▪ mapCasablanca.poly.xml ▪ mapCasablanca.rou.alt.xml ▪ mapCasablanca.rou.xml ▪ mapCasablanca.sumo.cfg , using the "netconvert" application, so that they can be read by SUMO. The file mapcasablanca.typ.xml, which contains important information about the network, such as speeds or priorities for each route class, should also be included. The figure below shows an overview of how to convert it with the command lines used.

Figure 4: Graphical interface: OpenStreetMap network (map of Casablanca city) imported into SUMO

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V.

SIMULATION RESULTS

SUMO allows to generate various outputs for each simulation run, visualization is done using SUMO-GUI. We were interested in the vehicles queues at the junctions, and the average waiting time on each lane, as the output of the simulator, the result is done using --queue-output . This command generates a file type XML Figure 5. named is the name of the file the Output will be wirtten to, below a part of this file:

Before deserializing, an XmlMapSerializer must be constructed using the type of the object that is being deserialized . After deserializing the XML file to an object class, we now proposed an algorithm (developed in C#) (Figure 7: Algorithm 2) that will allow us to calculate the average waiting time.

Figure 5: Output of SUMO

This file contains several information about the junctions of the imported map [23]: Name Time_step id Queueing_time

Type (simulation) seconds id seconds

Queueing_lenght

Meters

Queueing_lenght _experimental

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Description The time step described by the values within this time step-element The id of the line The total waiting time of vehicles due to a queue Thus the light from the junction until the final vehicle in line The length of the queue, thus until the last vehicle with a speed lower than 5klm/h

Figure 7: Algorithm for Calculating Average Wait Time(AWT)

The result is a file of type".txt" in the form of a table of two columns: The first column represents cycle number( iteration= time_step), and the second column represents the values of the average mean waiting time for a junction.

Now that we have the XML file, we just have to generate the classes allowing to have an object in which to deserialize our XML file into an object C#. For this we used the link [25]. We found the result below (Figure 6: Algorithm 1):

Figure 8-a): - Output of Algorithm 2- Average of WAT/ cycle

Figure 6: Algorithm for Deserializing an XML file to an object in C #.

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Figure 8-b) Average waiting time (AWT)

Diagram above shows the evolution of average waiting time / cycle. These results represent the output of algorithm 2. We recall that our objective is to reduce the energy consumption of the sensors deployed in the different intersections of the studied study area (city of Casablanca). This energy is consumed by the various units of the sensor (the processing unit, the acquisition unit, the communication unit). Our research team has previously proposed an efficient energy solution to minimize energy consumption during network activity by minimizing the number of packets sent: algorithm [13,14] based on the results of Simulation of the GLD simulator.

Figure 10: Total number of sent packets during 1000 cycles

The figure 10 shows the number of sent packets by the sensors which is placed before the traffic light for 1000 cycles. The results show in a normal function, this sensor sends about 275 messages to the fire controller, on the other hand with our proposed algorithm the same sensor sends about 216 messages. Consequently, the objective has been achieved to reduce the number of packets sent by the sensor and thus to minimize the energy consumption of the sensor. VI.

CONCLUSION AND PERSPECTIVE

Maximizing the lifetime of a wireless sensor network is considered a major challenge in the field of urban traffic management. In this paper, we have used the proposed algorithm previously but this time we simulate traffic urban in a more realistic case using SUMO and OpenStreetMap (OSM). The results show that our algorithm confirms the previous ones by the GLD simulator. This technique is one of the effective methods to solve the problem of energy consumption of sensor nodes, its principle is to reduce the number of communication between the sensor and the traffic light controller so as to minimize the number of packets sent.

Figure 9: Proposed Algorithm [13]

Future work includes a study and comparison on the algorithms that handles traffic lights. These algorithms process the data received from WSN, And determine the time of the green light according to the length of the queue for the different channels

Now we have used the SUMO simulator results (Output of Algorithm 2) being as the input parameters of the algorithm above Figure 9.

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[14] Kabrane, M., Elmaimouni, L., Krit, S., & Laassiri, J." Control of Urban Traffic Using Low-Cost and Energy-Saing for Wireless Sensor Network: Study and Simulation." International Journal of Engineering Research and Management(IJERM) ISSN: 2349-2058, Volume-03, Issue-04, April 2016 [15] Mustapha Kabrane, Salah-ddine Krit, Jalal Laassiri, Lahoucine El maimouni “ENERGY SAVING IN WIRELESS SENSOR NETWORKS: URBAN TRAFFIC MANAGEMENT APPLICATION” . Journal of Theoretical and Applied Information Technology. 15th January 2017. Vol.95.No1 [16] Kazi, R., Acharya, A., Narkar, A., & Yadav, H. (2017). Simulation to Demonstrate Traffic Junction Management. International Journal of Advanced Research in Computer Science, 7(2). [17] Enzinger, Martin. "Energy-efficient communication in wireless sensor networks." Sensor Nodes–Operation, Network and Application (SN) 25 (2012). [18] Israr, Nauman. "Energy Efficient Communication in Wireless Sensor Networks." Wireless Sensor Networks and Energy Efficiency: Protocols, Routing and Management: (2012): 274. [19] Krajzewicz, D., Hertkorn, G., Rössel, C., & Wagner, P. (2002). SUMO (Simulation of Urban MObility)-an open-source traffic simulation. In Proceedings of the 4th middle East Symposium on Simulation and Modelling (MESM20002) (pp. 183-187). [20] Krajzewicz, Daniel, and al. "Simulation of modern traffic lights control systems using the open source traffic simulation SUMO." Proceedings of the 3rd Industrial Simulation Conference 2005. EUROSIS-ETI, 2005. [21] Krajzewicz, D., Brockfeld, E., Mikat, J., Ringel, J., Rössel, C., Tuchscheerer, W., ... & Wösler, R. (2005, June). Simulation of modern traffic lights control systems using the open source traffic simulation SUMO. In Proceedings of the 3rd Industrial Simulation Conference 2005 (pp. 299-302). EUROSIS-ETI. [22] Krajzewicz, Daniel. "Traffic simulation with SUMO–simulation of urban mobility." Fundamentals of traffic simulation. Springer New York, 2010. 269-293. [23] Santana, Samuel Romero, Javier J. Sanchez-Medina, and Enrique Rubio-Royo. "How to Simulate Traffic with SUMO." International Conference on Computer Aided Systems Theory. Springer International Publishing, 2015. [24] www.sumo.dlr.de/wiki/Simulation/Output/Queue Output [25] http://xmltocsharp.azurewebsites.net

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AUTHORS Mustapha Kabrane received his the first Master’s degree in Electronics, Automatics and Computer, from the Faculty of Sciences, University of Perpignan Via Domitia , France, in 2012, and his the second Master’s degree in Computer Sciences from Institute of Sciences and Technology, University of Valenciennes, France, In 2013. He is currently a PhD student. His research interests include wireless sensor Networks implemented in the management and control of urban traffic at the Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco. Salah-ddine Krit received the B.S. and Ph.D degrees in Microectronics Engineering from Sidi Mohammed Ben Abdellah university, Fez, Morroco. Institute in 2004 and 2009, respectively. During 2002-2008, he is also an engineer Team leader in audio and power management Integrated Circuits (ICs) Research. Design, simulation and layout of analog and digital blocks dedicated for mobile phone and satellite communication systems using CMOS technology. He is currently a professor of informatics- Physics with Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco. His research interests include wireless sensor Networks (Software and Hardware), computer engineering and wireless communications.

Lahoucine EL MAIMOUNI was born in Zagora, Morocco, in 1970. He received in 2005, the Ph.D. degree in electronics from Institute of Electronics, Microelectronics and Nanotechnology (IEMN) University of Valenciennes, Valenciennes, France. In 2006, he joined the Polydisciplinary Faculty of ouarzazate, Ibn Zohr University, Morocco. In 2011, he received his Habilitation à Diriger des Recherches (HDR) from the Faculty of sciences Ibn Zohr University, Agadir. At present, his research activities are focused on acoustic wave propagation in piezoelectric structures, BAW resonators, piezoelectric sensor, acoustic wave resonators and filters for RF-MEMS, and audiovisual techniques for image and sound.

Kaoutar Bendaoud She is graduated in Electronics and Automatics Engineering from the National School of Applied Science of Tangier, Morocco in 2015. She is currently a Phd candidate at the Polydisciplinary. Faculty of Ouarzazate , Ibn Zohr University, Morocco. Her research interesets include linear regulators , DCDC power converters , switching converters for mobile applications Hassan Oudani received his Master’s degree in Network

and system from the Faculty of Sciences, University Ibno Zohr Agadir, Morroco, in 2009. He is currently a PhD student in Polydisciplinary Faculty of Ouarzazate , Ibn Zohr University Agadir ,Morroco, Network administrator in the Urbain Agency of OuarzazateZagora, Morroco. His research interests include wireless sensor Networks Design and implementation of a security protocol suitable for wireless sensor network at the polydisciplinary Faculty of ouarzazate,Ibn Zohr university, Agadir, Morocco. KARIMI Khaoula received the Engineer Degree In Software engineering from Faculty of Sciences and Technologies, Settat, Morocco, In 2015. She is currently a PhD student in Polydisciplinary Faculty of Ouarzazate, Department Mathematics and Informatics and Management, Ibn Zohr University Agadir, Morocco. Her research interests design and implementation of a Smart-home/Smartphone.

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