Realistic propagation effects on wireless sensor networks for landslide management

This paper presents the development of propagation models for wireless sensor networks for landslide management systems. Measurements of path loss in potential areas of landslide occurrence in Thailand were set up. The effect of the vegetation and mountain terrain in the particular area was therefore taken into account regarding the measured path loss. The measurement was carried out with short-range transmission/reception at 2400 MHz corresponding to IEEE 802.15.4 wireless sensor networks. The measurement setup was divided into two main cases, namely, the transmitting and receiving antennas installed on the ground and 1-m high above the ground. The measurement results are shown in this paper and used to develop propagation models suitable for operation of short-range wireless sensor networks of landslide management systems. The propagation model developed for the first case was achieved by fitting the averaged experimental data by the log-normal model plus the standard deviation. For the second case, the model was derived from the ray tracing theory. The mountain-side reflection path was added into the model which contained the reflection coefficient defined for the soil property. Furthermore, the resulting propagation models were employed in order to realistically evaluate the performance of wireless sensor networks via simulations which were conducted by using Castalia. In the simulations, the sensor nodes were placed as deterministic and random distributions within square simulated networks. The comparison between the results obtained from the deterministic and random distributions are discussed.

[1]  Marco Chiani,et al.  A Robust Wireless Sensor Network for Landslide Risk Analysis: System Design, Deployment, and Field Testing , 2016, IEEE Sensors Journal.

[2]  Maneesha V. Ramesh,et al.  Real-Time Wireless Sensor Network for Landslide Detection , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[3]  Rajesh Singh,et al.  Land Slide detection and monitoring system using wireless sensor networks (WSN) , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[4]  Tharek Abdul Rahman,et al.  An improved ITU-R rain attenuation prediction model over terrestrial microwave links in tropical region , 2012, EURASIP J. Wirel. Commun. Netw..

[5]  Feng Wang,et al.  Ambient Data Collection with Wireless Sensor Networks , 2010, EURASIP J. Wirel. Commun. Netw..

[6]  Somchai Biansoongnern,et al.  Development of Low Cost Vibration Sensor Network for Early Warning System of Landslides , 2016 .

[7]  T. Tamir On radio-wave propagation in forest environments , 1967 .

[8]  Maneesha Vinodini Ramesh,et al.  Data Reduction and Energy Sustenance in Multisensor Networks for Landslide Monitoring , 2014, IEEE Sensors Journal.

[9]  K. H. Craig,et al.  Semi-empirical model for millimetre-wave vegetation attenuation rates , 1995 .

[10]  Hyun-Joo Oh,et al.  Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand , 2009 .

[11]  A. Anandarajah,et al.  Slip surface localization in wireless sensor networks for landslide prediction , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[12]  Hsing-Yi Chen,et al.  Calculation of radio loss in forest environments by an empirical formula , 2001 .

[13]  Kemal Ertugrul Tepe,et al.  Design and Implementation of a Testbed for IEEE 802.15.4 (Zigbee) Performance Measurements , 2010, EURASIP J. Wirel. Commun. Netw..

[14]  Jie Zhang,et al.  Intracell Handover for Interference and Handover Mitigation in OFDMA Two-Tier Macrocell-Femtocell Networks , 2010, EURASIP J. Wirel. Commun. Netw..

[15]  Dianhong Wang,et al.  Anomaly Detection and Visual Perception for Landslide Monitoring Based on a Heterogeneous Sensor Network , 2017, IEEE Sensors Journal.

[16]  Jing Liang,et al.  A Propagation Environment Modeling in Foliage , 2010, EURASIP J. Wirel. Commun. Netw..

[17]  M. Al-Nuaimi,et al.  Measurements and prediction model optimisation for signal attenuation in vegetation media at centimetre wave frequencies , 1998 .

[18]  M. V. S. N. Prasad,et al.  Ultra-high frequency near-ground short-range propagation measurements in forest and plantation environments for wireless sensor networks , 2013, IET Wirel. Sens. Syst..

[19]  Yee Hui Lee,et al.  Empirical Near Ground Path Loss Modeling in a Forest at VHF and UHF Bands , 2009, IEEE Transactions on Antennas and Propagation.

[20]  Kai-Hsiang Ke,et al.  Open-Source Wireless Sensor System for Long-Term Monitoring of Slope Movement , 2017, IEEE Transactions on Instrumentation and Measurement.

[21]  Theodor Tamir Radio wave propagation along mixed paths in forest environments , 1977 .

[22]  J. D. Parsons,et al.  The Mobile Radio Propagation Channel , 1991 .

[23]  Mark A. Weissberger,et al.  An initial critical summary of models for predicting the attenuation of radio waves by trees , 1982 .