IJITCS Vol. 8, No. 7, 8 Jul. 2016
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Wireless Networks, Node Counting, OMNeT++, INET, Network Simulator, GPS
In this paper we introduce a novel algorithm for counting nodes based on wireless communications and their actual position, which works for stationary nodes and in scenarios where nodes are moving at high speeds. For this, each node is endowed with a Global Positioning System (GPS) receptor, allowing it to periodically send its actual position and speed through beacon messages. These data will be received by the first-hop neighboring nodes (which are within its scope or propagation range) that will have the ability to compute the actual position of the sending node based on the last broadcasted position and speed. The algorithm is constructed on the propagation of a count request message from the originator node toward nodes that are far away from it, and response messages traveling back to the originator, in the reverse path when it is possible, otherwise using the closest node on the way to the originator. To validate and evaluate the performance of our proposal, we simulate the algorithm using a famous network simulation tool called OMNeT++/INET. The results of our simulations show that the proposed algorithm efficiently computes a number of nodes very close to the real one, even in the case of scenarios of mobile nodes moving at high speeds, with an acceptable response time.
Manuel Contreras, Eric Gamess, "An Algorithm to Count Nodes in Wireless Networks Using their Actual Position", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.7, pp.43-52, 2016. DOI:10.5815/ijitcs.2016.07.07
[1]E. Huerta, A. Mangiaterra, and G. Noguera, GPS: Satellite Positioning. UNR Editora (Editorial of National University of Rosario), Rosario, Argentina, 2005.
[2]E. Gamess and M. Contreras, A Proposal for an Algorithm to Count Nodes using Wireless Technologies. International Journal of High Performance Computing and Networking. Vol 8, No. 4, pp. 345-357, 2015.
[3]E. Kell and E. Mills, Traffic Detector Handbook. U.S. Department of Transportation, Federal Highway Administration, 2nd Edition, pp. 1–39. USA, 1990.
[4]L. Klein, Sensors Technologies and Data Requirements for ITS Applications. Artech House Publishers, Norwood, USA, June 2001.
[5]L. Mimbela and L. Klein, A Summary of Vehicle Detection and Surveillance Technologies Used in Intelligent Transportation Systems. Handbook, Federal Highway Administration, Intelligent Transportation Systems, USA, 2007.
[6]G. Leduc, Road Traffic Data: Collection Methods and Applications. European Commission, Joint Research Center, Institute for Prospective Technological Studies, Seville, Spain, 2008.
[7]L. Sweeney and R. Gross, Mining Images in Publicly Available Cameras for Homeland Security. AAAI Spring Symposium on AI Technologies for Homeland Security, Palo Alto, California, USA, March 2005.
[8]S. Cho, T. Chow, and C. Leung, A Neural-Based Crowd Estimation by Hybrid Global Learning Algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 29, No. 4, pp. 535–541. August 1999.
[9]A. Bayona, People Counting by Laser Illumination and Image Processing Techniques. Master's Thesis. Department of Electronics and Communications, Autonomous University of Madrid, Madrid, Spain, May 2011.
[10]C. Yan-Yan, C. Ning, Z. Yu-Yang, W. Ke-Han, and Z. Wei-Wei, Pedestrian Detection and Tracking for Counting Applications in Metro Station, Discrete Dynamics in Nature and Society, Vol. 2014, Article ID 712041, 2014.
[11]K. Kopaczewski, M. Szczodrak, A. Czyzewski, and H. Krawczyk, A Method for Counting People Attending Large Public Events, Multimedia Tools and Applications, Springer US, pp. 1–13, August 2013.
[12]A. Knaian, A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems, Master Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, June 2000.
[13]Q. Chen, M. Gao, J. Ma, D. Zhang, L. Ni, and Y. Liu, Moving Object Counting using Ultrasonic Sensor Networks. International Journal of Sensor Networks, Vol. 3, No. 1, pp. 55–65, 2008.
[14]P. Zappi, E. Farella, and L. Benini, Enhancing the Spatial Resolution of Presence Detection in a PIR based Wireless Surveillance Network. In Proceedings of 2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007), pp. 295–300, London, United Kingdom, September 2007.
[15]E. Mathews and A. Poigné, Evaluation of a Smart Pedestrian Counting System based on Echo State Networks. Journal on Embedded Systems, EURASIP, Vol. 2009, Article 9, pp. 1–9, January 2009.
[16]L. Gu, D. Jia, P. Vicaire, T. Yan, L. Luo, A. Tirumala, Q. Cao, T. He, J. A. Stankovic, T. Abdelzaher, and B. Krogh, Lightweight Detection and Classification for Wireless
Sensor Networks in Realistic Environments. In Proceedings of the 3nd International Conference on Embedded Networked Sensor Systems (SenSys 2005), pp. 205–217, San Diego, California, USA, 2005.
[17]E. Gamess and I. Mahgoub, A Novel VANET-Based Approach to Determine the Position of the Last Vehicle Waiting at a Traffic Light. In proceedings of the 2011 International Conference on Wireless Networks (ICWN’11), Las Vegas, Nevada, USA, July 2011.
[18]A. Varga, The OMNeT++ Discrete Event Simulation System. In proceedings of the 15th European Simulation Multiconference (ESM’2001). Prague, Czech Republic, June 2001.
[19]IEEE Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE) - Multi-Channel Operation. IEEE 1609.4. November 2006.