Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications

  • Authors

    • Ahmed Bashar Fakhri
    • Sadik Kamel Gharghan
    • Saleem Latteef Mohammed
    2018-08-10
    https://doi.org/10.14419/ijet.v7i3.15409
  • LNSM, Path Loss Model, RSSI, WSN, ZigBee.

  • Wireless sensor networks (WSNs) and their applications have received significantly interested in the last few years. In WSN, knowing an accurate path-loss model as well as packet delivery should be taken into account for the successful distribution of several nodes in the net-work. This paper presents a path-loss modeling and performance evaluation of the ZigBee wireless standard. Received signal strength indi-cator (RSSI) measurements were achieved in outdoor and indoor environments to derive the path-loss based on Log-Normal Shadowing Model (LNSM). The path-loss parameters such as standard deviation and path loss exponents were estimated over point-to-point ZigBee WSN. In addition, the variances of received RSSI values and standard deviation for these values have been investigated. Furthermore, the data packets received is measured practically. Results revealed that the LNSM can be estimated to reflect the channel losses in both outdoor and indoor environments for medical application. The data delivery was achieved successfully of 100% in outdoor which better than indoor due to multipath propagation and shadowing. Moreover, the data packets delivery of the current work outperformed previous work.

     

     

  • References

    1. [1] Bhuvaneswari P, Vaidehi V, Saranya MA. Distance based transmission power control scheme for indoor wireless sensor network. Transactions on computational science XI: Springer; 2010. p. 207-22.

      [2] Pinto S, Cabral J, Gomes T. We-care: An IoT-based health care system for elderly people. Industrial Technology (ICIT), 2017 IEEE International Conference on: IEEE; 2017. p. 1378-83.

      [3] Gharghan SK, Nordin R, Ismail M. Development and validation of a track bicycle instrument for torque measurement using the zigbee wireless sensor network. International Journal on Smart Sensing and Intelligent Systems.2017 10:124-45. https://doi.org/10.21307/ijssis-2017-206.

      [4] Jawad HM, Nordin R, Gharghan SK, Jawad AM, Ismail M. Energy-Efficient wireless sensor networks for precision agriculture: A review. Sensors. 2017 17 1781. https://doi.org/10.3390/s17081781.

      [5] Gharghan SK, Nordin R, Ismail M. An ultra-low power wireless sensor network for bicycle torque performance measurements. Sensors. 2015 15 11741-68. https://doi.org/10.3390/s150511741.

      [6] Gharghan SK, Nordin R, Ismail M. Energy-efficient ZigBee-based wireless sensor network for track bicycle performance monitoring. Sensors. 2014 14 15573-92. https://doi.org/10.3390/s140815573.

      [7] Di Francesco M, Anastasi G, Conti M, Das SK, Neri V. Reliability and energy-efficiency in IEEE 802.15. 4/ZigBee sensor networks: an adaptive and cross-layer approach. IEEE Journal on selected areas in communications.2011 29:1508-24. https://doi.org/10.1109/JSAC.2011.110902.

      [8] Hebel M, Bricker G. Getting started with XBee RF modules. Tutorial Parallax Inc Consulta. 2010 8.

      [9] Bell C. Beginning sensor networks with Arduino and Raspberry Pi: Apress; 2014.

      [10] Inc. DI. XBee®/XBee-PRO S2C Zigbee, Available: https://www.digi.com/resources/documentation/digidocs/pdfs/90002002.pdf (accessed on 21 March 2018).

      [11] MaxStream, Inc. - a Digi International brand, XBeeâ„¢ Series 2 OEM RF Modules. Available: http://www.farnell.com/datasheets/27606.pdf (accessed on 21 March 2018).

      [12] Digi International Inc. DIGI XBEE® S1 802.15.4 RF MODULES, Available: https://www.digi.com/pdf/ds_xbeemultipointmodules.pdf (accessed on 21 March 2018).

      [13] Poutanen J, Haneda K, Salmi J, Kolmonen V-M, Koivunen J, Almers P, et al. Analysis of radio wave propagation from an indoor hall to a corridor. Antennas and Propagation Society International Symposium, 2009 APSURSI'09 IEEE: IEEE; 2009. p. 1-4. https://doi.org/10.1109/APS.2009.5172169.

      [14] Noori N, Karimzadeh-Baee R, Abolghasemi A. An empirical ultra wideband channel model for indoor laboratory environments. Radioengineering. 2009 18 68-74.

      [15] Geng S, Vainikainen P. Millimeter-wave propagation in indoor corridors. IEEE Antennas and Wireless Propagation Letters. 2009 8 1242-5. https://doi.org/10.1109/LAWP.2009.2035723.

      [16] Lim SY, Yun Z, Baker JM, Celik N, Youn H-s, Iskander MF. Propagation modeling and measurement for a multifloor stairwell. IEEE Antennas and Wireless Propagation Letters. 2009 8 583-6. https://doi.org/10.1109/LAWP.2009.2021870.

      [17] Valcarce A, Zhang J. Empirical indoor-to-outdoor propagation model for residential areas at 0.9–3.5 GHz. IEEE Antennas and Wireless Propagation Letters. 2010 9 682-5. https://doi.org/10.1109/LAWP.2010.2058085.

      [18] Benkic K, Malajner M, Planinsic P, Cucej Z. Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. Systems, signals and image processing, 2008 IWSSIP 2008 15th international conference on: IEEE; 2008. p. 303-6. https://doi.org/10.1109/IWSSIP.2008.4604427.

      [19] Malajner M, Benkic K, Planinsic P, Cucej Z. The accuracy of propagation models for distance measurement between WSN nodes. Systems, Signals and Image Processing, 2009 IWSSIP 2009 16th International Conference on: IEEE; 2009. p. 1-4. https://doi.org/10.1109/IWSSIP.2009.5367782.

      [20] Al Alawi R. RSSI based location estimation in wireless sensors networks. Networks (ICON), 2011 17th IEEE International Conference on: IEEE; 2011. p. 118-22.

      [21] Dezfouli B, Radi M, Razak SA, Hwee-Pink T, Bakar KA. Modeling low-power wireless communications. Journal of Network and Computer Applications. 201551: 102-26. https://doi.org/10.1016/j.jnca.2014.02.009.

      [22] Adewumi O, Djouani K, Kurien A. Performance evaluation of RSSI based distance measurement for localization in wireless sensor networks. International Conference on e-Infrastructure and e-Services for Developing Countries: Springer; 2012. p. 74-83.

      [23] Miramontes R, Aquino R, and Flores a, Rodríguez G, Anguiano R, Ríos A, et al. PlaIMoS: a remote mobile healthcare platform to monitor cardiovascular and respiratory variables. Sensors. 2017 17 176. https://doi.org/10.3390/s17010176.

      [24] Prakash R, Ganesh AB, Girish SV. Cooperative wireless network control based health and activity monitoring system. Journal of medical systems. 2016 40:216. https://doi.org/10.1007/s10916-016-0576-4.

      [25] Watthanawisuth N, Lomas T, Wisitsoraat A, Tuantranont A. Wireless wearable pulse oximeter for health monitoring using ZigBee wireless sensor network. Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on: IEEE; 2010. p. 575-9.

      [26] Wong A, McDonagh D, Omeni O, Nunn C, Hernandez-Silveira M, Burdett A. Sensium: An ultra-low-power wireless body sensor network platform: Design & application challenges. Engineering in Medicine and Biology Society, 2009 EMBC 2009 Annual International Conference of the IEEE: IEEE; 2009. p. 6576-9.

      [27] Chen S-K, Kao T, Chan C-T, Huang C-N, Chiang C-Y, Lai C-Y, et al. A reliable transmission protocol for zigbee-based wireless patient monitoring. IEEE Transactions on Information Technology in Biomedicine.2012 16:6-16. https://doi.org/10.1109/TITB.2011.2171704.

      [28] Liu F, Zhu H, Gu Z, Liu Y. A linear localization algorithm for wireless sensor network based on RSSI. Advanced Research on Computer Education, Simulation and Modeling: Springer 2011. p. 384-9.

      [29] Tang L, Liu M, Wang K-C, Huang Y, Yang F, and Zhang D. Study of path loss and data transmission error of IEEE 802.15.4 compliant wireless sensors in small-scale manufacturing environments. The International Journal of Advanced Manufacturing Technology. 2012 63 659-69. https://doi.org/10.1007/s00170-012-3928-3.

      [30] Pivato P, Palopoli L, Petri D. Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Transactions on Instrumentation and Measurement. 2011 60 3451-60. https://doi.org/10.1109/TIM.2011.2134890.

      [31] Chuang P-J, Jiang Y-J. Effective neural network-based node localisation scheme for wireless sensor networks. IET Wireless Sensor Systems.2014 4:97-103.

      [32] Gharghan SK, Nordin R, Ismail M. Energy efficiency of ultra-low-power bicycle wireless sensor networks based on a combination of power reduction techniques. Journal of Sensors. 2016 2016.

      [33] Lian K-Y, Hsiao S-J, Sung W-T. Intelligent multi-sensor control system based on innovative technology integration via ZigBee and Wi-Fi networks. Journal of network and computer applications. 2013 36 756-67. https://doi.org/10.1016/j.jnca.2012.12.012.

      [34] Ta X, Mao G, Anderson BD. On the giant component of wireless multihop networks in the presence of shadowing. IEEE Transactions on Vehicular Technology. 2009; 58:5152-63. https://doi.org/10.1109/TVT.2009.2026480.

      [35] Noh S-K, Kim K-S, Ji Y-K. Design of a room monitoring system for wireless sensor networks. International Journal of Distributed Sensor Networks. 2013 9:189840. https://doi.org/10.1155/2013/189840.

      [36] Sahu PK, Wu EH-K, Sahoo J. DuRT: Dual RSSI trend based localization for wireless sensor networks. IEEE Sensors Journal. 2013; 13:3115-23. https://doi.org/10.1109/JSEN.2013.2257731.

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    Bashar Fakhri, A., Kamel Gharghan, S., & Latteef Mohammed, S. (2018). Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications. International Journal of Engineering & Technology, 7(3), 1666-1671. https://doi.org/10.14419/ijet.v7i3.15409